Research Review of Agricultural Machinery Power Chassis in Hilly and Mountainous Areas

Abstract

The terrain in hilly and mountainous areas is complex, and the level of agricultural mechanization is low. This article systematically reviews the research progress of key technologies for agricultural machinery power chassis in hilly and mountainous areas, and conducts an analysis of five aspects: the power system, walking system, steering system, leveling system, and automatic navigation and path tracking control system. In this manuscript, (1) in terms of the power system, the technical characteristics and application scenarios of mechanical, hydraulic, and electric drive systems were compared. (2) In terms of the walking system, the performance differences between wheeled, crawler, legged, and composite walking devices and the application of suspension systems in agricultural machinery chassis were discussed. (3) In terms of the steering system, the steering characteristics of wheeled chassis and crawler chassis were analyzed, respectively. (4) In terms of the leveling system, the research progress on hydraulic and electric leveling mechanisms, as well as intelligent leveling control algorithms, was summarized. (5) The technology of automatic navigation and path tracking for agricultural machinery chassis was discussed, focusing on multi-sensor fusion and advanced control algorithms. In the future, agricultural machinery chassis will develop towards the directions of intelligence, automation, greening, being lightweight, and being multi-functionality.

1. Introduction

The research and development of advanced and applicable agricultural machinery power chassis for hilly and mountainous areas is one of key and most challenging topics in agricultural machinery equipment research [1]. Currently, the hilly and mountainous areas around the world cover a vast area. The area of cultivated land and the population covered by these areas account for a relatively large proportion overall. The level of agricultural development in these areas has a significant impact on the global agricultural production efficiency [2]. Hilly and mountainous areas are characterized by steep terrain slopes, uneven road surfaces, diverse soil conditions, and relatively low levels of agricultural mechanization. These factors create a complex operating environment for agricultural equipment, leading to situations where “machines are available but cannot be used, or no suitable machines are available at all”. Therefore, the research and development of intelligent agricultural machinery power chassis with excellent working performance, tailored to the operating environment of hilly and mountainous areas, is of great significance for improving agricultural production efficiency and advancing the level of agricultural mechanization in these areas [3,4].

Agricultural machinery power chassis for hilly and mountainous areas are highly stable and obstacle-crossing mobile power platforms that integrate system assemblies such as the power system, traveling system, steering system, leveling system, and intelligent control system. They can stably, efficiently, and reliably carry, tow, and externally mount other functional agricultural machinery and implements [5,6]. They enable the completion of various agricultural production tasks, including plowing, sowing, plant protection, picking, and harvesting [7,8,9]. The power system of an agricultural machinery chassis typically transmits power to various components of agricultural machinery, such as tillage tools and harvesters, through gearboxes, transmission shafts, and other mechanisms. This ensures the efficient operation of the equipment in production processes such as plowing, sowing, crop management, and harvesting [10,11]. In the complex terrain of hilly and mountainous areas, the walking system and steering system of an agricultural machinery chassis work together to adjust the chassis’ driving direction and speed, mitigate the vibration impact on the operating equipment caused by uneven roads, maintain chassis stability, and enable the chassis to adapt flexibly to various geographical conditions [12,13]. The leveling system of an agricultural machinery chassis can adjust the position and posture of both the chassis and the mounted agricultural machinery implements. This enhances the stability of the chassis during agricultural production operations in hilly and mountainous areas and reduces the risk of overturning when operating on rough and steep slopes [14]. The autonomous navigation and path tracking control system of an agricultural machinery chassis can significantly enhance the stability, passability, maneuverability, and autonomous operation capabilities of agricultural machinery [15,16]. As a result, the research and development of intelligent agricultural machinery chassis has become a focal point in the study of agricultural mechanization for hilly and mountainous areas.

In recent years, numerous agricultural science and technology researchers have conducted extensive studies on key technologies for agricultural machinery chassis in hilly and mountainous areas, including power drives, stepless speed changing, walking device design, suspension damping, active steering, differential steering, body leveling, autonomous navigation, and path tracking [17,18,19]. These efforts have led to the development of various agricultural machinery chassis capable of adapting to the operating environment in hilly and mountainous areas. By mounting agricultural machinery on these chassis to replace manual labor, labor costs can be significantly reduced while operation accuracy and efficiency are improved [20,21,22].

The development level of agricultural machinery power chassis is an important indicator reflecting the degree of agricultural modernization and intelligence. Globally, all leading countries in agricultural equipment regard the chassis as the core of research and development for intelligent agricultural machinery [23,24]. Therefore, this article elaborates on the current development status of agricultural machinery chassis for hilly and mountainous areas in five aspects: the power system, walking system, steering system, leveling system, and autonomous navigation and path tracking control system. It summarizes the basic principles, technical characteristics, and typical applications of each system of the agricultural machinery chassis, while also exploring future development directions for agricultural machinery chassis in hilly and mountainous areas. This provides a theoretical reference for the design of agricultural machinery chassis tailored to such areas. The framework of this article is shown in Figure 1.

2. Power Systems of Agricultural Machinery Power Chassis

2.1. Chassis Drive System Type

The power systems of agricultural machinery power chassis for hilly and mountainous areas can be classified into mechanical, hydraulic, and electric types based on their drive systems. Selecting the appropriate power system according to different operational requirements is crucial, as it directly impacts the efficiency and quality of agricultural production [25,26].

The mechanical drive system is the most common type of drive system in agricultural machinery chassis for hilly and mountainous areas. It offers advantages such as a simple structure, ease of maintenance, high transmission efficiency, suitability for long-term and high-intensity operations, and a low cost, making it ideal for large-scale adoption. In hilly and mountainous areas, tractor chassis primarily adopt mechanical four-wheel drive systems, such as in the John Deere 5 Series and Dongfanghong Series tractor models. Additionally, some crawler tractor chassis also utilize mechanical drive systems [27,28], where a transmission device transfers the engine’s power to the crawler. Some examples include the 865EX-T crawler combine harvester and the LV 800 PRO crawler chassis. Some prototypes are shown in Figure 2.

Zhu et al. [29] aimed to meet the driving requirements of tractors in various operating environments and designed a mechanical, electrical, and hydraulic power transmission system (MEH-PS) for tractors based on the characteristics of hydro-mechanical compound transmission and electro-mechanical hybrid power systems. Through experiments, they verified that this power system consumed less fuel than traditional CVT tractors. Damanauskas et al. [30] experimentally compared indicators such as wheel slip, fuel consumption, and driving performance between the mechanical single-wheel 4WD drive system and the double-wheel 2WD drive system of wheeled tractors. They further evaluated the impact of these two drive systems on farming efficiency. Xie et al. [31] aimed to meet the requirements of terrain adaptability, flexibility, and stability for agricultural machinery chassis operating in the complex terrain of hilly and mountainous areas and narrow fields, designing a mechanical-drive arched-waist chassis suitable for narrow plots. The maximum lateral and longitudinal slope slip angles of this chassis both exceed 25 degrees, enabling stable operation in hilly and mountainous areas.

Hydraulic-driven agricultural machinery chassis offer a wide speed regulation range and can achieve stepless speed changes by adjusting the flow and pressure of their hydraulic systems. Hydraulic systems effectively absorb shocks and vibrations, ensuring smooth transmission. Additionally, their operation is simpler and more flexible compared to mechanical drive systems. However, hydraulic systems suffer from energy loss, resulting in lower efficiency than mechanical drive systems. In recent years, fully hydraulic drive technology has been widely adopted in chassis for high-ground-clearance plant protection machines and harvesters. Integrated with advanced electronic information technology, it has significantly enhanced their driving performance.

Zhang et al. [32] proposed a fully hydraulic-driven flexible and intelligent sprayer chassis to meet the operational requirements of plant protection sprayers in the complex agricultural and forestry environments of hilly and mountainous areas. This study provides a valuable reference for the design of hydraulic synchronous control drive systems. To address the issues of high fuel consumption and poor emission performance in high-horsepower tractors, Zhu et al. [33] adopted a dual power source consisting of an engine and a motor, combined with a hydraulic continuously variable transmission (HMCVT), to design a parallel hybrid power tractor system. Liu et al. [34] designed a driving system for mountain crawler tractors based on hydrostatic transmission (HST), as illustrated in Figure 3. They tested the system’s traction performance and start-up acceleration performance. The results demonstrated that the designed HST system exhibited excellent transmission continuity and was well-suited for operations in hilly and mountainous areas.

The working principle of the hydraulic-driven system for mountain crawler tractors is as follows: Through the clutch and the acceleration of the transfer case, the power of the engine drives the piston variable pump to run and then drives the bidirectional hydraulic motor to run. The rear part of the bidirectional hydraulic motor is connected to the driving rear axle, which transmits the power to the driving wheel after reducing the speed and increasing the torque. The swash plate adjustment mechanism is used to adjust the displacement and flow direction of the piston variable pump, which can change the speed and rotation direction of the bidirectional hydraulic motor to adjust the driving speed and moving direction of the tractor [34].

Electrically driven agricultural machinery chassis typically use lead–acid batteries as their power supply and permanent magnet brushless motors as their power source. They feature zero exhaust emissions and low noise levels, meeting environmental protection requirements in the context of new energy development. Additionally, electric agricultural machinery chassis are characterized by flexible steering and rapid power response [35,36], making them highly suitable for operations in small plots in hilly and mountainous areas.

Liu et al. [37] addressed the issues of low motor-power utilization and short endurance time in electric tractors, proposing an electric drive control scheme based on the motor’s maximum efficiency characteristic curve. Experimental verification demonstrated that this scheme enhanced the drive efficiency of the tractor motor. Yuko et al. [38] designed a 10 kW pure electric tractor. Compared to the energy consumption of tractors powered by internal combustion engines of the same capacity during field travel and farming operations, this design reduced energy consumption by approximately 70%. Han et al. [39] developed an electric orchard tractor equipped with a power battery and driven by dual motors for transmission. The output shafts of the traveling motor and the power take-off (PTO) motor were connected via a wet clutch, which controlled the coupling of the two motors. Bench tests demonstrated that the tractor achieved a maximum PTO output power of 13.9 kW and could operate continuously for 4.5 h. Troncon et al. [40] designed a hybrid power assembly for electric tractors suitable for vineyard operations in hilly areas and optimized the motor size using the thermal equivalent torque method. Although electrically driven agricultural machinery chassis exhibit relatively low energy consumption, they face challenges such as their reliance on external power sources and limited endurance. To enhance the endurance capacity of electric agricultural machinery chassis, high-endurance chassis with hybrid oil–electric power systems will become a key focus of future research [41].

A comparison of the characteristics of agricultural machinery chassis of three different drive system types is shown in

Table 1. Comparison of characteristics of agricultural machinery chassis of three different drive system types.

Drive System Type	Fundamental Principle	Technical Characteristics	Applicable Models	Example Diagram	Prototype Performance
Mechanical	Mechanical transmission devices transfer power directly	Simple structure; high efficiency; low cost; limited speed regulation range	Transmission tractors; small agricultural machinery	BETTER 180 mountain tractor	Electronic 6-speed shifting BETCAM system that could downshift automatically: power: 129 kW/176 HP; maximum engine speed: 2375 rpm
Hydraulic	Hydraulic system transmits power	Wide speed regulation range; simple and convenient operation; stable transmission; low efficiency	Large agricultural machinery; agricultural machinery for operation in complex terrains	Rogator 600 series self-propelled sprayer	HydroStar CVT gearbox in combination with engine wheel hubs: 40/50 km/h at reduced engine speed; maximum operating width of 39 m.
Electric	Motor converts electrical energy into mechanical energy	Environmental protection; good speed regulation performance; simple structure; dependent on external power supply	Small-sized electric agricultural machinery; precision agricultural machinery	ET1004-W electric tractor	Power: 100 HP; distributed control technology, four-wheel drive and four-wheel steering combined for control; driverless wheel-side drive

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2.2. Agricultural Machinery CVT Technology

The agricultural machinery chassis in hilly and mountainous areas must adapt to complex and variable terrain and load conditions during operation. Traditional stepped transmission systems struggle to meet the demands for efficient and flexible power delivery. The adoption of continuously variable transmission technology enables the continuous adjustment of the transmission ratio, allowing the engine to operate consistently within its efficient range, thereby enhancing driving power and fuel economy [42].

The three commonly used types of continuously variable transmissions for agricultural machinery chassis are mechanical continuously variable transmission (CVT), hydro-mechanical continuously variable transmission (HMCVT), and hydrostatic continuously variable transmission (HST). In recent years, researchers have devoted themselves to the research and development of CVT technology and have achieved certain research results. To improve the shifting quality of tractors equipped with hydrostatic power-split continuously variable transmissions during start-up, Wang et al. [43] constructed mathematical models of the clutch hydraulic system, CVT, and tractors. They also determined the influence mechanisms of key component parameters on the shifting quality of tractors. To improve the reliability of CVT tractors, Xue et al. [44] constructed a test bench to measure the engagement pressure of the wet clutch under different fault modes. They employed an improved Gaussian Naive Bayes algorithm based on a time window to classify the different fault modes of the clutch control system. The analysis results demonstrate that the clutch pressure fluctuation during the CVT shifting process of the tractor can be utilized for fault diagnosis in the clutch control system.

Hydro-mechanical continuously variable transmissions (HMCVTs) are widely used in the chassis of agricultural machinery, such as tractors. Obtaining an accurate system response model is crucial for implementing HMCVT control strategies and designing controllers. Cheng et al. [45] decomposed and analyzed an HMCVT system, dividing it into three dynamic response stages: the response of the electronic proportional pressure-reducing valve, the displacement response of the variable piston, and the speed ratio response of the pump–motor system. They established response models for each stage, derived the transfer function of the HMCVT system using the series method, and validated the model’s accuracy through a heuristic intelligent optimization algorithm-based system identification method. Cheng et al. [46] identified the optimal position of the variable speed stage connection point based on the operating requirements of the tractor and the continuity of the HMCVT’s driving characteristics. They established an optimization design model for HMCVT parameters and employed a genetic algorithm to optimize these parameters, achieving a flexible, efficient, and rapid design of an HMCVT system.

Cheng et al. [47] aimed to achieve and enhance the constant speed control performance of tractors equipped with hydrostatic transmission (HST) variable-speed units. Using a HST test bench for tractors, they conducted verification tests for speed regulation characteristics, denoising and filtering tests on the response signals, tests on the influence of load disturbance on the speed regulation characteristics, and PID-based constant speed performance detection tests. Their experimental results demonstrated that an increase in load torque inhibited the output response of HST, PID-based constant speed control performed effectively, and the rate of change in load torque had the greatest impact on the stability of the HST output speed.

A comparison of the characteristics of the three typical continuously variable transmissions for agricultural machinery chassis is shown in

Table 2. Typical continuously variable transmissions for agricultural machinery chassis.

Type	Fundamental Principle	Technical Characteristics	Example Diagram	Applicable Models
Mechanical CVT	Stepless speed change is achieved through a chain belt and pulley system, enabling the continuous adjustment of the transmission ratio.	Continuous gear shifting capability; simple structure; limited torque capacity		Fendt 1100 Vario series tractors
HMCVT	Stepless speed variation is achieved through the coordinated operation of hydraulic and gear systems.	High transmission efficiency; stable power output; complex structure		Massey Ferguson MF8700 series tractors
HST	Power is transmitted through the hydraulic pump and the hydraulic motor to achieve stepless speed variation.	Precise speed and torque control; relatively low transmission efficiency		John Deere 4000 series large self-propelled harvester; Case A8000 sugarcane harvester

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Of the three typical continuously variable transmissions of agricultural machinery chassis mentioned in Table 2, mechanical CVT achieves continuously variable speed by dynamically adjusting the effective radius ratio of the transmission system. Its core consists of a pair of variable-diameter driving wheels and driven wheels, as well as a high-strength chain. The driving wheel is connected to the engine. The distance between the conical disks is controlled by hydraulic pressure or centrifugal force in order to change the radius of the belt contact. The driven wheel is connected to the output shaft. The belt tension is maintained by reverse pressure and the radius is adjusted synchronously. When accelerating, the radius of the driving wheel increases and the radius of the driven wheel decreases, and the transmission ratio increases (the output rotational speed accelerates); when decelerating, the opposite occurs, and the transmission ratio decreases (the torque increases). HMCVT achieves continuously variable transmission through the power split between hydraulic transmission and mechanical transmission. Its core part is composed of a hydraulic path (variable pump and fixed-displacement motor) and a mechanical path (gear set) in parallel. In the case of low speed and high torque, the hydraulic path plays a leading role. The variable pump changes the flow rate by adjusting the swash plate angle, thereby driving the motor for stepless speed regulation. In high-speed and high-efficiency working conditions, the mechanical path takes over the work, and the power is directly transmitted through the gears to reduce hydraulic losses. HST achieves stepless speed change through pure hydraulic energy conversion. Its core consists of a variable-displacement pump and a hydraulic motor, forming a closed-loop system. The variable-displacement pump receives power from the engine and changes the flow rate of hydraulic oil by adjusting the swash plate angle to control the output energy. The hydraulic motor converts hydraulic energy into mechanical energy to drive the load, and the flow rate determines its rotational speed and direction. The displacement of the pump is continuously adjustable, enabling a smooth transition of the motor’s rotational speed from zero to the maximum value.

In summary, research on agricultural machinery chassis power systems has primarily focused on analyzing drive theory and transmission characteristics to maximize transmission efficiency and operating condition adaptability. With the ongoing advancement of agricultural modernization and the increasing mechanization level, the future development of these power systems will emphasize intelligent, energy-efficient, and environmentally sustainable requirements. Research on the power systems of agricultural machinery chassis in the future should focus on matching the relationship between the complex operating environment in hilly and mountainous areas and the power transmission of the chassis to ensure the good power performance, continuity, and safety of chassis operation.

3. Walking Systems of Agricultural Machinery Power Chassis

The walking system of agricultural machinery power chassis in hilly and mountainous areas is a core component of agricultural machinery. The walking system provides structural support for the entire agricultural machinery through its frame and walking device. This system accommodates all the essential components, including the combustion engine, power transmission system, and attached implements. Furthermore, it delivers tractive power to the ground-engaging mechanisms via the drivetrain assembly, enabling precise bidirectional movement (forward/reverse) and controlled directional changes in the chassis. The integrated suspension system within the walking system serves to achieve the following outcomes: (1) attenuate the dynamic loads induced by terrain irregularities, (2) dampen harmful mechanical vibrations, (3) enhance operator comfort during field operations. Crucially, the dynamic performance of the walking system fundamentally determines the operational capacity of an agricultural machinery chassis and its kinematic adaptability to the complex topographies of hilly and mountainous areas.

3.1. Walking Device Design

To adapt to the complex terrain and operational conditions of hilly and mountainous areas, researchers have developed agricultural machinery chassis with different walking devices. These primarily include wheeled, crawler, legged, and composite types. The advancement of such walking systems enables agricultural chassis to perform crop planting, field management, and harvesting tasks across varied terrains, significantly enhancing the mechanization and automation levels in agricultural production [48,49,50].

Wheeled chassis demonstrate relatively high travel speeds on flat or gently undulating terrain, while also offering excellent maneuverability and ease of steering control [51]. Han et al. [52] developed a ride-on, fully automatic vegetable seedling transplanter. This transplanter is four-wheel-drive and a ride-on type, capable of maintaining a straight trajectory along field ridges while achieving a small turning radius. Additionally, the traveling wheels are significantly taller than the seedlings, preventing crop damage from the chassis and ensuring its good adaptability to sloped terrain. To meet the agronomic requirements for the efficient and high-quality operation of wheeled electric tractors with sliding battery packs, Wang et al. [53] developed a tillage speed–slip-ratio-switching control system for tillage operations. They employed a sliding mode control algorithm to reduce the wheeled tractor’s slip ratio and enhance traction efficiency.

Crawler chassis offer strong trafficability on muddy or uneven terrain and are suitable for slope operations. However, their low travel speed makes them unsuitable for long-distance operations [54,55]. To address the poor mobility of crawler combine harvesters in wet and soft paddy fields, Yuan et al. [56] proposed a high-traction track shoe grouser inspired by the structure of ostrich feet. They further optimized the crawler surface design with bionic convex patterns, significantly enhancing the traction performance of the crawler walking device under these conditions. Yu et al. [57] developed a compact crawler broccoli harvester designed for small farmland plots. The harvester’s small size enables it to maneuver effectively in fields with narrow crop spacing, significantly improving broccoli harvesting efficiency.

Legged chassis employ bionic designs inspired by the locomotion patterns of humans, animals, or insects. These designs enable movement across complex terrains in hilly and mountainous regions. However, their limited load-bearing capacities and unsuitability for long-distance operations constrain their practical applications [58]. To enhance autonomous goods transportation efficiency using legged agricultural robots, Yang et al. [59] developed a heavy-duty hexapod robot capable of traversing rough agricultural terrain. They implemented an adaptive fuzzy impedance algorithm for real-time force–position hybrid control, enabling dynamic foot position adjustment and improved adaptation to rugged hilly and mountainous landscapes. To enhance the walking stability and obstacle-crossing capability of legged robots on slopes, Zhang et al. [60] developed a bionic quadruped robot modeled after goat locomotion. The robot featured a 7.37 cm leg height and 28.40 cm stride length. Through the coordinated control of the four legs, it was able to cross more complex terrains.

In recent years, scholars have conducted more and more research on modular-designed composite power chassis. These composite power chassis can avoid the shortcomings of a single walking structure and are suitable for various operating terrains, such as agricultural operations in mixed terrains. However, there is also the problem that their walking structures are relatively complex, affecting mobility. Grazioso et al. [61] proposed a reconfigurable locomotion system for small agricultural vehicles featuring a wheeled–crawler mechanism. This design combined superior obstacle-crossing capability with minimized soil compaction through optimized performance characteristics. Bruzzone et al. [62] developed a multi-sensor integrated wheel–track–leg hybrid composite chassis. The chassis was able to adapt to varied terrain conditions by employing tracks for irregular or soft terrain walking, utilizing wheels for efficient hard-surface mobility, and executing stair-climbing maneuvers through its articulated leg joints. However, the system’s complex kinematic structure and demanding control requirements currently limit its agricultural applications.

A comparison of the characteristics of different types of walking devices for agricultural machinery chassis is shown in

Table 3. Comparison of characteristics of different walking devices for agricultural machinery chassis.

Type	Advantages	Disadvantages	Application Scenarios	Example Diagram
Wheeled	Fast speed; suitable for long-distance movement and transportation; convenient steering and handling	Susceptibility to skidding or becoming immobilized on soft or uneven terrain; significant rollover possibility when walking on slopes or slippery surfaces	Flat land; gently undulating slopes	Transplanter [52]
Crawler	Superior traversability on soft, muddy, and uneven terrain; high stability; suitable for working on slopes	Slow speed; unsuitable for long-distance movement; damages hard pavement easily	Muddy ground; sloping land	Grass cutter
Legged	Strong adaptability; capable of walking on complex terrain; high flexibility	Slow speed; unsuitable for long-distance movement; unsuitable for heavy-load operations	Mountainous areas; forest land	Transport robot [59]
Composite	Suitable for various types of working terrains	Complex structure; heavy weight	Hills; wetlands	Wheel–track tractor

.

In summary, the terrain in hilly and mountainous areas is complex, with large slopes and soft soil, which puts forward higher requirements for the performance of the walking devices of agricultural machinery chassis. According to different operation terrains and operation modes in hilly and mountainous areas, different types of agricultural machinery chassis walking devices are constantly being updated and iterated on. In the future, the walking devices of agricultural machinery chassis will develop in the directions of intelligence, lightweighting, and multi-functionality, improving their adaptability to complex terrains and reducing their impact on the soil environment at the same time.

3.2. Suspension Systems

The suspension system of agricultural machinery power chassis in hilly and mountainous areas is widely used in various types of machinery, such as tractors, combine harvesters, seeders, fertilizer applicators, plant protection machinery, rotary tillers, orchard machinery, harvesters, and self-driving agricultural machinery. It plays an important role in connecting the walking device and the chassis, supporting the vehicle body, adjusting the height and angle of the chassis, reducing the vibration of the body, enhancing the stability of the operating equipment [63,64], improving the terrain adaptability of the traveling device, increasing the operation efficiency, and providing equipment protection and safety protection [65,66]. Scholars have developed many suspension systems adapted to different types of agricultural machinery chassis and conducted theoretical analyses on the related vibration reduction mechanisms [67,68].

Sain et al. [69] focused on developing an efficient active suspension system for Kubota M110X tractors, and the model is shown in Figure 4a. Their study proposed a nonlinear fuzzy proportional-integral–proportional-derivative (PI-PD) controller using area/center of gravity (CoA/G) defuzzification. This suspension system improved ride quality and robustness by reducing vibrations caused by field-level fluctuations. To improve stability in unmanned wheel–leg agricultural vehicles, Fernandes et al. [70] designed an active suspension system with autonomous and remote-control functionality. This system utilized fuzzy logic to correct height and zero-moment point (ZMP) deviations, ensuring stable operation while maintaining the desired working height. To reduce the damage to the road surface, vehicle components, and agricultural products during the transportation process of agricultural machinery chassis, Chen et al. [71] studied an electronically controlled air suspension height adjustment system for agricultural transport vehicles and designed a vehicle height controller based on the single-neuron adaptive PID control algorithm. The vibration-damping performance of the electronically controlled air suspension was verified through simulation and bench tests. Aiming at the problem that the vehicle body height of a propelled sprayer would change due variations in liquid loading volume during spraying, Chen et al. [72] took a large high-passability self-propelled sprayer with air suspension as their research object and established a three-degree-of-freedom vertical dynamics model of the sprayer. Then, they designed an air suspension height stability controller based on the sliding mode control algorithm, and adjusted the working height of the spray boom by controlling the inflation and deflation of the air suspension. Zheng et al. [73] conducted dynamic modeling for the vibration characteristics of a wheeled tractor with front-axle hydro-pneumatic suspension. It was assumed that the cabin and the driver seat were rigidly connected to the chassis in the longitudinal and lateral directions. The effect of the stiffness of the thrust and the guide bars was not considered in their work to simplify the model. A multi-body dynamic model of the wheeled tractor/implement system with hydro-pneumatic suspension on its front axle was developed, as shown in Figure 4b. Through simulation, the vibration responses of the driver’s seat, cab, chassis, and implement under different forward speeds and field road conditions, as well as the corresponding power spectral densities, were obtained. The experimental results showed that front-axle hydro-pneumatic suspension would reduce the driver’s ride comfort, but improve the pitch and roll stability.

In Figure 4a, $m_1$ and $m_2$ are the body mass of the quarter-scale tractor and the suspension mass, kg. $k_1$ and $k_2$ are the spring constants of the suspension system and the wheel and tire. $b_1$ and $b_2$ are the damping constants of the suspension system and the wheel and tire, respectively. The displacement of the tractor body is denoted by $x_1(t)$, whereas that of the suspension mass is denoted by $x_2(t)$. $u(t)$ is the control force generated by the actuator, and $w(t)$ is the field disturbance [69]. In Figure 4b, $K_{tx,fl}$, $K_{tx,fr}$, $K_{tx,rl}$, and $K_{tx,rr}$ are the longitudinal stiffnesses of the front and rear tires, N/m; $K_{ty,fl}$, $K_{ty,fr}$, $K_{ty,rl}$, and $K_{ty,rr}$ are the lateral stiffnesses of the front and rear tires, N/m; $K_{tz,fl}$, $K_{tz,fr}$, $K_{tz,rl}$, and $K_{tz,rr}$ are the vertical stiffnesses of the front and rear tires, N/m; $C_{tx,fl}$, $C_{tx,fr}$, $C_{tx,rl}$, and $C_{tx,rr}$ are the longitudinal damping values of the front and rear tires, N·s/m; $C_{ty,fl}$, $C_{ty,fr}$, $C_{ty,rl}$, and $C_{ty,rr}$ are the lateral damping values of the front and rear tires, N·s/m; $C_{tz,fl}$, $C_{tz,fr}$, $C_{tz,rl}$, and $C_{tz,rr}$ are the vertical damping values of the front and rear tires, N·s/m; $F_{z,fl}$, $F_{z,fr}$, $F_{z,rl}$, and $F_{z,rr}$ are the dynamic loads of the four tires, N; $K_{cfl}$, $K_{cfr}$, $K_{crl}$, and $K_{crr}$ are the vertical stiffnesses of the cabin, N/m; $C_{cfl}$, $C_{cfr}$, $C_{crl}$, and $C_{crr}$ are the vertical damping values of the cabin, N·s/m; $K_c$ and $K_d$ are the stiffness and damping of the driver’s seat, N/m; $K_{sfl}$ and $K_{sfr}$ are the vertical stiffness of the front axle suspension, N/m; ${\phi }_{b1}$ and ${\phi }_{b2}$ are the pitch and roll angles of the chassis around the y and x axles, rad; ${\phi }_{c1}$ and ${\phi }_{c2}$ are the pitch and roll angles of the cabin around the y and x axles, rad; $m_a$, $m_b$, $m_c$, $m_d$, and $m_f$ are, respectively, the mass of the implement, chassis, cabin, driver, and front axle, kg; $l_f$ and $l_r$ are the distances between the front axle and the center of mass of the chassis and between the rear axle and the center of mass of the chassis, m; $l_{bf}$ and $l_{br}$ are the distances between the front cabin suspension and the center of mass of the chassis and between the rear cabin suspension and the center of mass of the chassis, m; $l_{sw}$ and $l_{bw}$ are the distances between the front axle suspension and the center of gravity of the chassis and between the front tire and the center of gravity of the chassis, m; $l_{cf}$ and $l_{cr}$ are the distances between the front cabin suspension and the center of mass of the cabin and between the rear cabin suspension and the center of mass of the cabin, m; and $l_d$ is the distance between the driver’s seat and the center of mass of the cabin, m [73].

Sim et al. [74] focused on improving driving comfort in agricultural tractors through advanced suspension control. Their study involved the following steps: developing a computational model of an oil–gas suspension system with semi-active control capabilities, formulating an optimal control algorithm using the Linear Quadratic Gaussian (LQG) method specifically for tractor applications, and conducting a comparative performance evaluation between passive cab suspensions and the proposed semi-active oil–gas suspension system in terms of driving comfort enhancement. Cui et al. [75] believed that if the spray uniformity of large sprayers was to be improved, the spray boom attitude should be kept parallel to the ground slope or the crop canopy below the spray boom. Passive suspension can attenuate frequencies higher than the resonance frequency, but it cannot align the spray boom with the inclined ground. Therefore, they designed an electro-hydraulic active suspension system. The transient and steady-state performances of the spray boom control system using the velocity feedforward PID algorithm were tested on a six-degree-of-freedom motion simulator. The test results showed that, after adding the electro-hydraulic active suspension, the influence of ground excitation interference on the spray process of the spray boom was significantly reduced.

Currently, the suspension systems of agricultural machinery chassis in hilly and mountainous areas primarily use traditional suspensions. Hydraulic and air suspensions are gradually being adopted. High-performance suspension systems, such as semi-active and active suspensions, are too costly and difficult to popularize in small and medium-sized agricultural machinery chassis. Meanwhile, the harsh environment in hilly and mountainous areas imposes higher requirements for the reliability of suspension systems. In the future, efforts should focus on overcoming development bottlenecks such as cost, reliability, and intelligent algorithms to promote the widespread adoption of composite, lightweight, and intelligent suspension systems and improve the vibration-damping performance of agricultural machinery chassis [76].

4. Steering Systems of Agricultural Machinery Power Chassis

The primary function of the steering systems of agricultural machinery power chassis in hilly and mountainous areas is to utilize external driving torque to control the steering mechanism, ensuring the chassis follows the intended path. The agility of the steering system significantly impacts the chassis’ driving performance and operational efficiency in such terrains. A stable steering system can reduce the possibility of slippage and rollover of the agricultural machinery chassis on slopes and rough road surfaces [77,78]. Wheeled and crawler chassis, commonly used in hilly and mountainous areas, exhibit distinct steering system performance characteristics due to their different walking devices [79].

4.1. Steering Systems of Wheeled Agricultural Machinery Chassis

Wheeled agricultural machinery chassis employ diverse steering methods. The most common way to achieve steering is through the deflection of the steering wheel, and this way is applicable to medium- and small-sized agricultural machinery [80]. Other steering methods include the following: (1) Articulated steering: Achieved through relative deflection between the front and rear frames, offering high flexibility for complex terrain. (2) Four-wheel steering: Simultaneous steering of front and rear wheels reduces the turning radius and enhances maneuverability, making it ideal for large machinery or orchard operations. (3) All-wheel steering: This provides superior terrain adaptability by enabling all the wheels to steer [81]. Current power steering technologies include the following: (1) Hydraulic Power Steering: This uses hydraulic systems to reduce driver effort while improving controllability. (2) Electric Power Steering: This method employs motor assistance, offering energy efficiency and precise control [82,83].

Scholars have produced relevant designs for and studies on the steering systems of wheeled chassis. Tejero et al. [84] observed that most current wheeled agricultural robots rely on electric power and differential steering. To address this limitation, they designed an autonomous wheeled tractor for fruit tree plantations featuring a mechanical steering system with dual steering axles. As illustrated in Figure 5a, this innovative system operates in three distinct modes: front-wheel steering, front and rear inverse steering (FRIS), and a hybrid steering mode (HS). These multiple steering modes significantly enhance the tractor’s maneuverability during both transit and operational tasks in orchard environments. Liu et al. [85] developed a model predictive control (MPC) approach based on direct yaw moment control (DYC) to enable self-steering capabilities in autonomous four-wheel independent-drive (4WID) agricultural electric chassis. Their innovative design features front and rear axles capable of simultaneously rotating around their respective centers, reducing the turning radius by up to 50%. By integrating the DYC-based MPC controller, the system optimally distributes torque to all four wheels according to the required steering angle, target yaw moment, and desired driving speed. To address the need for enhanced maneuverability in confined horticultural spaces, Chen et al. [86] developed a distributed electric mobile chassis for plant protection applications. Their study introduced the following: (1) a novel lateral coupling dynamic model between the electric chassis and rotary tiller, which is shown in Figure 5b, (2) an advanced robust model predictive controller (RMPC) designed to minimize the chassis yaw rate and sideslip angle during operation. Satyam et al. [87], based on the Global Navigation Satellite System (GNSS) and other sensors, then designed a four-wheel-steering orchard mobile chassis. This chassis can travel along a prescribed path at the speed and steering rate given by the navigation controller. Its four-wheel steering model is shown in Figure 5c. The test results show that the normalized root mean square value error of the steering offset of this chassis during steering is within 0.2–0.4°, and 0.05° during straight-line driving, indicating good steering performance. He et al. [88] demonstrated that, under complex terrain conditions, the rollover risk of small tractors is particularly severe, and active steering (AS) technology provided an effective solution. To address parameter variations and external disturbances during rollover processes, they developed a sliding mode control (SMC) strategy using exponential terminal sliding surfaces, constructed diverse test scenarios (variable speeds, slopes, and obstacles), and conducted dynamic tests on wheeled chassis. Their results confirmed that the SMC-AS controller outperformed PID-AS. Xu et al. [89] designed a tractor with an electro-hydraulic coupling steering system, as shown in Figure 5d, and proposed a hierarchical multi-loop robust control architecture (LMLRC). They considered uncertainties and disturbances in the tracking path and designed a path tracking layer based on MPC, and then considered the parameter uncertainties and disturbances caused by electro-hydraulic coupling steering and designed an angle tracking layer based on SMC. Additionally, a current tracking layer based on PID was added to enable the steering system to respond quickly.

In Figure 5a, $\alpha$ is the steering angle, deg [84]. In Figure 5b, $F_{x,fl}$ is front-wheel longitudinal force, N; $F_{x,rl}$ is rear-wheel longitudinal force, N; $F_{y,fl}$ is front-wheel lateral force, N; $F_{y,rl}$ is rear-wheel lateral force, N; ${\alpha }_{fl}$, ${\alpha }_{fr}$, ${\alpha }_{rl}$${\alpha }_{rr}$, ${\alpha }_f$, and ${\alpha }_z$ are the lateral slip angles of each wheel, deg; $\delta$ and ${\delta }_f$ are the steering angles, rad; $C{G}_1$ is the centroid of the EDMP; $C{G}_2$ is the centroid of the rototiller; $F_{yf}$ is the lateral force on the front wheel, N; $l_t$ is the distance from CG₂ to the rear axle, m; $l_f$ is the distance from the front axle to the centroid, m; $l_r$ is the distance from the rear axle to the centroid, m; $v_x$ is longitudinal velocity, m/s; $v_y$ is lateral velocity, m/s; ${\omega }_z$ is yaw rate, rad/s; and $\theta$ is sideslip angle, rad [86]. In Figure 5d, $C_w$ is the damping coefficient of the steering system; $T_a$ is motor torque, N·m; $\omega$ is axle angular velocity, rad/s; $T_f$ is tire lateral torque on the front axle, N·m; $T_L$ is rotary valve torsion bar torque, N·m; $K_L$ is rotary valve torsion bar stiffness, N·m; $C_v$ is the piston damping coefficient; $\Delta {\theta }_L$ is the rotation angle of the rotary valve torsion bar, deg; $T_r$ is the steering resistance moment, N·m; and ${\delta }_f$ is the front-wheel steering angle, deg [89].

An et al. [90] took a wheeled agricultural machinery chassis equipped with a Hydraulic-Power-Assisted Steering (HPAS) system as their research object and proposed an Electric Automatic Steering System (EASS). Their system realizes effective steering by using fuzzy control and adopts the PID algorithm to handle the difference between the expected steering angle and the actual steering angle, achieving the automatic control of the rotation of the steering wheel. Moreover, it uses fuzzy control to realize the self-adjustment of PID parameters. Test results demonstrated that the designed EASS, based on fuzzy control, was suitable for various wheeled agricultural machinery chassis. Yang et al. [91] aimed to enhance tractor headland turning efficiency; thus, they achieved this by installing a 360° steerable auxiliary wheel at the front of the tractor. The prototype is shown in Figure 5e. The designed electro-hydraulic system enabled automatic steering, lifting, and driving functions of the auxiliary wheel. Experimental results showed that, compared to conventional turning methods, the auxiliary wheel system improved automatic headland turning efficiency by 50% in time efficiency, 80% in travel distance, and 50% in space utilization.

Figure 5. Example diagrams of wheeled steering systems. (a) Three distinct steering modes [84]; (b) lateral coupling dynamics model [86]; (c) four-wheel steering model [87]; (d) electric–hydraulic coupling steering mechanical model [89]; (e) tractor equipped with auxiliary wheels [91].

Figure 5. Example diagrams of wheeled steering systems. (a) Three distinct steering modes [84]; (b) lateral coupling dynamics model [86]; (c) four-wheel steering model [87]; (d) electric–hydraulic coupling steering mechanical model [89]; (e) tractor equipped with auxiliary wheels [91].

4.2. Steering Systems of Crawler Agricultural Machinery Chassis

The most commonly used steering method for crawler agricultural machinery chassis is differential steering. This method achieves steering by controlling the speed difference between the two crawler tracks through either a differential mechanism or drive motors, making it suitable for traditional crawler tractors and combine harvesters [92]. The unilateral braking steering method involves cutting off power to one track while applying braking force to that side, forcing the other track to drive alone to achieve steering. This approach is suitable for small crawler agricultural machinery [93]. The independent drive steering method involves driving the tracks on both sides through independent hydraulic motors or electric motors and adjusting the speed or direction of a single-side track to achieve steering, which is suitable for intelligent unmanned agricultural machinery.

The steering process of crawler agricultural machinery chassis is more complex than that of wheeled chassis. During steering, phenomena such as track sinking, slipping, and skidding are highly prone to occurring. Therefore, research on the steering characteristics of crawler agricultural machinery chassis is essential to enhance their steering performance in hilly and mountainous areas. Lv et al. [94] selected the slip ratios of the inner and outer tracks of a fully hydraulic crawler chassis as evaluation indicators. Considering road conditions, speed, and steering radius as influencing factors, they measured the forces during three steering processes: reverse differential steering, unilateral braking steering, and forward differential steering. Then, they obtained the relationship between steering moment and steering radius. Simulation and experimental results demonstrated that the steering radius had a greater impact on the slip ratios of both tracks than either road conditions or driving speed. To address the issues of soil compaction and accumulation caused by crawler steering systems, Tang et al. [95] developed a positive and negative steering gearbox (PNS gearbox) based on the working principles of straight-forward steering and in-place steering modes in crawler combine harvesters equipped with unilateral braking steering gearboxes. The PNS gearbox is shown in Figure 6a. Experimental results demonstrated that the PNS gearbox could achieve three operational modes: straight-forward movement, unilateral braking steering, and in-place forward and reverse steering. Wang et al. [96] designed a drive steering crawler harvester chassis to address issues such as low transmission efficiency and large steering radius in conventional hydrostatic-drive crawler harvesters. They analyzed the chassis transmission system structure and its steering characteristics under three conditions: differential steering, differential reverse steering, and unilateral braking steering. Chen et al. [97] established a simplified geometric model of a crawler combine harvester to improve the headland turning efficiency of unmanned combine harvesters. Their model accounted for the influence of both the header and crawler system on turning performance. Through in-depth analysis, they derived calculation formulas for path length, turning time, and required headland width for four turning methods: fish-tail, bulb, semi-circular, and U-shaped turning. Their work provides navigation guidance for combine harvester steering while consistently determining the minimum necessary headland turning width. To addressed the poor controllability of differential steering in crawler tractors, which negatively affects automatic navigation performance, Wang et al. [98] used a hydraulic-transmission-controlled planetary differential steering crawler tractor as their research platform and established a mathematical model of its turning radius. Additionally, they developed a crawler tractor turning radius control method based on a Gaussian mixture model, specifically designed for straight-path tracking and U-turns, to improve the accuracy of autonomous steering.

In Figure 6b, $X(E)$ and $Y(N)$ are the coordinate axes of the Gaussian plane coordinate system; $AB$ is the target line; $\Theta$ is the target heading angle, deg; $v_1$ and $v_2$ are, respectively, the speed of the left and right tracks, m/s; $b$ is the track width, m; $\theta$ is the real-time heading angle of the tractor, deg; $d$ is the distance deviation from the tractor to the target straight line, m; $R$ is the turning radius of the tractor, m; $\omega$ is tractor turning angular velocity, rad/s; $C$ is the instantaneous steering center of the tractor; and $O_c$ is the geometric center of the tractor [99].

Qin et al. [99] proposed a state-feedback-based differential steering control system to address three key issues in single-path hydrostatic transmission crawler tractors during automatic steering control: poor stability, low steering resolution, and the significant soil damage caused by unilateral braking steering. Based on a crawler tractor kinematic model (Figure 6b), they designed a pulse-width-modulation-based differential steering control method. This approach precisely adjusts the steering hydraulic cylinder stroke to achieve enhanced steering resolution and stability. Zhou et al. [100] investigated the dynamic performance of an articulated steering semi-tracked tractor, as shown in Figure 6c. Considering the distinct dynamic characteristics exhibited on surfaces with different hardness levels, they built separate soft-surface and hard-surface models by the harmonic superposition method. They conducted simulation and experimental comparisons of the tractors’ straight-line and turning movements on two types of road surfaces, and selected the average speed error and turning radius error to evaluate the steering performance of the tractor.

A comparison of the characteristics of the steering systems of wheeled and crawler agricultural machinery chassis is shown in

Table 4. Comparison of characteristics of wheeled and crawler steering systems.

Type	Example Diagram	Advantages	Disadvantages	Application Scenarios
Wheeled steering		Small turning radius; steering flexibly; prompt steering response; small steering resistance	prone to skidding when turning on soft and slippery ground; turning easily aggravates soil compaction; prone to tipping over when turning on sloping ground	Field margins; narrow plots; flat and hard road surfaces
Crawler steering	[95]	Turns on complex terrain stably; uniform traction force distribution during turning; low center of gravity means it is not easy to roll over when turning	Complex steering operation; slow steering speed; easy to scratch ground when steering on hard pavement	Muddy land; sloping land; rugged mountainous land

.

In summary, most research by experts and scholars on agricultural machinery chassis steering systems involves establishing dynamic and kinematic models of chassis steering to analyze factors affecting turning radius and mechanical characteristics during steering [101,102]. They have developed and optimized steering control methods and controllers, while designing various steering mechanisms to adapt to different terrains in hilly and mountainous areas, thereby improving chassis steering performance. Furthermore, it is necessary to further integrate high-precision automatic steering navigation technology for real-time correction, improve the accuracy of autonomous steering, and reduce the influence of skidding and slipping on chassis steering [103].

5. Leveling Systems of Agricultural Machinery Power Chassis

Currently, to meet the operational requirements of agricultural machinery in hilly and mountainous areas, multiple chassis leveling systems suitable for different working environments have been developed. These systems incorporate advanced technologies such as sensors, Internet of Things devices, single-chip microcontrollers, and human–computer interaction interfaces [104,105]. The automatic leveling systems of agricultural machinery chassis effectively address issues like poor operational quality caused by uneven terrain and steep slopes, and have been successfully implemented in various agricultural scenarios including hilly and mountainous areas, orchards, and cultivated fields [106,107].

5.1. Leveling Mechanism Design

The passability, stability, and safety of agricultural machinery power chassis face significant challenges in the complex terrains of hilly and mountainous areas. In these environments with sudden terrain variations, agricultural machinery chassis are prone to hazardous situations including slippage, rollover, and backward tilting. Consequently, chassis stability during slope operations including climbing, descending, and slope turning requires further improvement [108]. The chassis leveling mechanism calculates necessary adjustments based on chassis inclination degree and achieves chassis leveling through the controlled actuation of hydraulic cylinders, electric cylinders, or linkage systems [109]. This posture adjustment system effectively lowers the center of gravity, thereby enhancing the operational stability of agricultural machinery chassis in hilly and mountainous areas.

Notably, Sun et al. [110] designed a compact chassis attitude adjustment device based on a parallel four-bar mechanism to address the leveling difficulties and stability issues of mountain crawler tractors. The device utilizes a hydraulic system to control the extension and retraction of both active and driven rockers, enabling the lateral leveling of the chassis platform and consequently improving stability during lateral slope operations. However, the leveled chassis demonstrates a limited load-bearing capacity. The prototype is illustrated in Figure 7a. Shang et al. [111] developed a multi-functional leveling chassis suitable for orchards in hilly and mountainous areas. This chassis included an optimally designed a dual-loop hydraulic system and a horizontal and vertical two-way leveling structure which could achieve synchronous adjustment. Based on the inclination sensor, they developed an intelligent detection and control system to realize the automatic leveling of the operation platform. The test results showed that the maximum climbing ability of this chassis was 30° and the maximum leveling angle was 15°. The prototype is shown in Figure 7b. To address the operational challenges of significant body inclination variations and poor stability in hilly and mountainous areas, Jiang et al. [112,113] designed an articulated omnidirectional leveling mechanism based on a “three-layer frame” structure. This system achieves full-platform leveling through controlled hydraulic cylinder actuation, though with a relatively limited operational platform carrying capacity. The prototype’s implementation is shown in Figure 7c. Cui et al. [114] developed an active suspension system to enhance spray uniformity for large spray booms on agricultural sprayers. The system integrates hydraulic cylinders, attitude sensors, and a boom attitude control algorithm. By processing sensor detection signals through the suspension control system, it dynamically adjusts the spray boom’s working position to maintain stability. This configuration effectively mitigates ground excitation interference during pesticide application, significantly improving spraying uniformity. The system prototype is illustrated in Figure 7d.

Lv et al. [115] developed a controllable adaptive tractor-leveling mechanism to enhance automatic leveling performance in hilly and mountainous areas. The mechanism employs a hybrid support system combining linear three-point support with planar positioning. One servo motor drives the longitudinal leveling mechanism via a self-locking reducer, while another servo-controlled electric cylinder executes lateral leveling through precision actuation. This system achieves leveling accuracy within 1°. Sun et al. [116,117] developed a hydraulic four-point adjustable lifting and lowering crawler chassis leveling mechanism for a combine harvester to improve its operation performance and trafficability. The chassis posture can be adjusted by controlling the expansion and contraction of the hydraulic cylinder, enabling the combine harvester to remain level during field operation, reducing the possibility of rollover and improving the harvesting efficiency. The prototype is shown in Figure 7e. Peng et al. [118] designed a hydraulic leveling mechanism for tractor bodies to solve the problems of tractor bodies in hilly and mountainous areas being difficult to keep horizontal and being prone to rollover during operation under complex working conditions. They also built a vehicle test bench to verify the leveling performance of the mechanism through experiments. The test results show that the hydraulic leveling mechanism can achieve the lateral leveling of a vehicle body relatively well. The prototype is shown in Figure 7f. For the different attitude adjustment requirements of body leveling and farm implement profiling operations when mountain crawler tractors operate along contour lines, Yang et al. [119] designed a tractor chassis with a coordinated control system for the attitudes of the body and the farm implements. This leveling mechanism could achieve the coordinated adjustment of the vehicle body and the mounted agricultural implements, and keep the entire chassis parallel to the cultivated land, thereby meeting the requirements of contour operation on sloping land in hilly and mountainous areas. The prototype is shown in Figure 7g.

Figure 7. Example diagrams of leveling mechanisms for agricultural machinery chassis. (a) Lateral attitude adjustment device [110]; (b) multi-functional leveling chassis for orchards [111]; (c) three-layer frame leveling machine [112,113]; (d) active suspension leveling system [114]; (e) hydraulic four-point adjustable lifting chassis [116,117]; (f) hydraulic leveling tractor [118]; (g) mountain crawler tractor [119].

Figure 7. Example diagrams of leveling mechanisms for agricultural machinery chassis. (a) Lateral attitude adjustment device [110]; (b) multi-functional leveling chassis for orchards [111]; (c) three-layer frame leveling machine [112,113]; (d) active suspension leveling system [114]; (e) hydraulic four-point adjustable lifting chassis [116,117]; (f) hydraulic leveling tractor [118]; (g) mountain crawler tractor [119].

In summary, leveling mechanisms are constantly being improved according to the operation requirements in hilly and mountainous areas, which can improve the adaptability of agricultural machinery chassis in the complex terrain of hilly and mountainous areas. However, there are still deficiencies in the research in this field. For example, some chassis structures are relatively small and have insufficient bearing capacities; the leveling systems of some chassis increase vehicles’ overall height, and the centers of gravity of the chassis will accordingly increase, affecting the stability and passability of the chassis; the response time of the leveling control systems of some chassis is relatively long and needs further optimization and improvement. In the future, it will be necessary to design a highly matched leveling mechanism according to the operating environment of a chassis, coordinate the leveling stability and speed of the leveling mechanism, and develop and design a control system with higher leveling accuracy.

5.2. Leveling Control Algorithm

During agricultural operations in hilly and mountainous areas, the leveling control algorithm assumes a crucial role in precisely adjusting the posture of agricultural machinery chassis. The accurate perception of road information in different operating environments and the intelligent processing of effective information benefit from high-precision sensors and intelligent control systems. Scholars have conducted relevant research on the control algorithms required for posture adjustment under different working conditions [120].

Lv et al. [121] proposed an automatic leveling control method of feedforward PID control to improve the automatic leveling control performance of a high-level operation platform in orchards. By introducing the feedforward link into the traditional PID control, the ground interference can be compensated directly and the influence of the interference can be eliminated in time. This not only does not damage the stability of the system, but can also improve the control accuracy. Qi et al. [122] proposed an automatic leveling control method for adjusting the swing angle of tractor wheels using a double closed-loop fuzzy PID algorithm for the attitude adjustment mechanism of tractors in hilly and mountainous areas. Simulations and experiments demonstrated that, under the same PID parameters, the double closed-loop fuzzy PID control had better performance than the double closed-loop PID control. Zhang et al. [123] pointed out the problem of insufficient accuracy and reliability in attitude adjustment when conventional tractors operate in hilly and mountainous areas. To solve this problem, they developed a synchronous control system for tractor bodies and implement attitude based on a neural network PID algorithm. This adaptive control algorithm enabled real-time PID parameter self-tuning, significantly enhancing system control accuracy and stability. Sun et al. [124], based on the omnidirectional leveling system of the “three-layer frame”, introduced the Q-learning algorithm to update the connection weights of the BP neural network, then optimizing the PID control parameters through the BP neural network, thereby achieving the effective control of the attitude of a crawler-type working machine. The Q-BP-PID algorithm architecture is presented in Figure 8.

In Figure 8, $Y_d(k)$ is the ideal inclination, deg; $Y(k)$ is fuselage inclination, deg, $e(k)$ is angular error, deg; $x_1$, $x_2$, and $x_3$ are input layer nodes; $i$, $j$, and $k$ are, respectively, nodes of the input layer, the hidden layer, and the output layer; $w_{ij}$ is the weighted coefficient of the input layer and hidden layer neuron nodes; $w_{jk}$ is the weighted coefficient of the hidden layer and output layer neuron nodes; $O_1$, $O_2$, and $O_3$ are the output of the output layer; $K_p$, $K_i$, and $K_d$ are the PID control’s proportional, integral, and derivative parameters; and $u(k)$ is the solenoid valve’s opening degree signal [124].

Wang et al. [125] designed an omnidirectional leveling system with hydraulic interconnection. They maintained the stability of the platform by ensuring the fixed nature of the center point during the leveling process. Furthermore, they proposed a disturbance observer-based sliding mode synchronous control method to minimize hydraulic cylinder synchronization errors and enhance leveling precision. Aiming at the problems existing in the automatic leveling of mountain tractors under complex working conditions, Peng et al. [126] developed an automatic leveling control system based on a four-point vehicle body leveling mechanism. They adopted a sliding mode variable structure control algorithm based on fuzzy switch gain adjustment to achieve real-time dynamic automatic leveling control. Federico et al. [127] designed a chassis leveling control system for a combine harvester. Starting with the identification of the black box model, they designed a model predictive control algorithm to control the yaw angle and pitch angle of the vehicle body. In addition, an automatic and data-driven tuning protocol was proposed by them to reduce the time required for the traditional manual tuning process. Chen et al. [128] designed an adaptive leveling control system for crawler tractors in hilly and mountainous areas. A dual-axis tilt sensor was used by them to detect the tilt angle of the tractor, and a servo motor was controlled to achieve the omnidirectional leveling of the vehicle body. The control algorithm adopted a proportional algorithm, and the leveling action could be completed within 0–6 s.

In summary, current leveling control algorithms primarily consist of the following techniques: feedforward PID, fuzzy PID, and BP neural network PID controls, sliding mode control, model predictive control, and linear proportional control.

Table 5. Performance comparison of various leveling control algorithms.

Control Algorithm	Advantages	Disadvantages	Reference
Feedforward PID Control	The compensation amount can be directly generated based on the interference signal to offset the disturbance influence in advance; the leveling response speed is fast; and the dependence on the feedback loop is low.	The method relies on the precise mathematical model of the leveling system; the unmeasurable disturbances cannot be eliminated.	Lv et al. [121]
Fuzzy PID Control	There is no need for an accurate mathematical model; nonlinear, time-varying or complex coupled leveling systems can be handled through empirical rules; and it has strong robustness.	The design complexity of the fuzzy rules is high, and the controls’ real-time performance is limited.	Qi et al. [122]
Neural Network PID Control	The method is applicable to strongly nonlinear or time-varying leveling systems, and the PID parameters are dynamically adjusted through the backpropagation algorithm to adapt to environmental disturbances.	The computational complexity is high, and the control response may be delayed; the network may overfit the training data, and the performance when generalized to new working conditions is unstable.	Zhang et al. [123] Sun et al. [124]
Synovial Control	The method has strong robustness against parameter changes in the leveling system and external disturbances; it converges to a sliding surface within a finite time and has a relatively fast response speed.	The method may cause high-frequency buffeting, and the high-frequency switching control signal will increase the energy consumption of the system.	Wang et al. [125] Peng et al. [126]
Model Predictive Control	The method’s multi-variable control ability is strong; it is suitable for solving coupling problems; and through rolling optimization, the control strategy can be adjusted in real time.	The method’s computational complexity is high and the parameter debugging is complex; it relies on the precise mathematical model of the leveling system.	Federico et al. [127]
Linear Proportional Control	The structure is simple and easy to implement, and the leveling control response speed is fast.	The steady-state error cannot be completely eliminated, the method is sensitive to changes in control parameters, and its anti-interference ability is weak.	Chen et al. [128]

presents a comparative performance analysis of these leveling control algorithms. However, systematic analyses of controlled object characteristics and external disturbance factors remain insufficient. Future research should focus on integrating chassis attitude leveling systems with intelligent control algorithms to improve control accuracy, reliability, and intelligence, thereby enhancing the operational stability and safety of agricultural machinery in hilly and mountainous areas.

6. Automatic Navigation and Path Tracking Control Systems of Agricultural Machinery Power Chassis

The automatic navigation and path tracking control systems of agricultural machinery power chassis in hilly and mountainous areas have developed rapidly and have become an important direction for modern agricultural intelligence in recent years. The application of high-precision positioning technologies (such as RTK-GNSS) and multi-sensor fusion (such as RGB cameras and lidar) has significantly improved navigation’s accuracy and adaptability in complex environments [129]. In terms of path tracking control, the research based on advanced control approaches such as adaptive and deep learning algorithms has continuously deepened, achieving precise tracking and efficient operation in complex terrain [130]. Furthermore, the integration of automatic navigation systems tends to be modular and networked, supporting remote control and dynamic obstacle avoidance [131]. The application scenarios of automatic navigation systems have expanded from large-scale field operations to diverse environments such as orchards, tea gardens, and hilly and mountainous areas [132,133].

6.1. Automatic Navigation Systems of Agricultural Machinery Chassis

Due to the continuous increase in the demand for food, the shortage of a global agricultural labor force, and the decrease in the utilization rate of agricultural resources, the demand for autonomous navigation technologies and equipment in agricultural machinery chassis is becoming increasingly urgent. The agricultural operation scenarios are complex and diverse. The automatic navigation technology of agricultural machinery chassis has different technical characteristics and application values in scenarios such as farmland, orchards, and hilly and mountainous areas [134,135]. The composition structure of automatic navigation systems in agricultural machinery chassis is shown in Figure 9.

For different agricultural operation scenarios, researchers have conducted in-depth studies on automatic navigation technology to improve the autonomous operation ability of agricultural machinery chassis. To improve the accuracy and stability of automatic navigation technology in agricultural machinery chassis in farmland operation scenarios, Li et al. [136] integrated Global Navigation Satellite System (GNSS) positioning equipment and an inertial measurement unit integrating accelerometers and gyroscopes. Then, they proposed a fuzzy adaptive finite impulse response Kalman filter (FA-FIR-KF) algorithm to integrate the position information and attitude information of the chassis, and introduced the quaternion method to suppress the actual nonlinear problem of the coordinates of agricultural machinery chassis caused by their attitude angles. Cui et al. [137] designed an unmanned tractor automatic navigation system based on dynamic path searching and the Fuzzy Stanley Model (FSM). They established a navigation decision-making system based on a unified reference waypoint search framework, provided a path generation method for full-field coverage, and used a fuzzy logic controller to adaptively adjust the gain coefficient of the Stanley Model (SM) according to the tracking error.

To improve the operational efficiency of the auxiliary navigation operation of combine harvesters, Chen et al. [138] designed a navigation control system for combine harvesters based on the fusion of visual simultaneous localization and mapping (SLAM) and inertial guidance. This system acquires field image information through binocular cameras and extracts the crop boundary lines as its navigation reference. They adopted a sliding window optimization method based on tightly coupled nonlinear optimization to achieve the constrained optimization of the image and inertial guidance information. To improve the operation quality of an automatic navigation boom sprayer during its breakpoint continuous operation process, Li et al. [139], based on the spray lag compensation algorithm, proposed an operation breakpoint identification algorithm combining the real-time dynamic Global Navigation Satellite System (RTK-GNSS) and a wheel odometer. The test results showed that this system reduced the repeated spray area and improved the pesticide spray quality of the automatic breakpoint continuous spray. In orchard and plantation environments, high vegetation density resulting in RF scintillation can affect the effectiveness of GNSS mechanical automatic guidance. To solve this problem, Nakaguchi et al. [140] developed a deep learning machine stereo vision guidance system, which combined an RGB-D anti-collision system with deep learning-driven machine vision for inter-row positioning and a dead reckoning rule set for alternate U-turns. The test results showed that the system displayed good navigation performance in orchards. Thanpattranon et al. [141] developed a single-sensor navigation control algorithm for the navigation of tractor–trailer systems in single-row plantations and the travel between plots, and proposed a control scheme for stopping the tractor–trailer using a laser rangefinder (LRF) in various field tasks. LRFs can be used for navigating autonomous agricultural tractors equipped with dual-wheel trailers. To solve the problem of the autonomous navigation control of agricultural tractors on cross-slope terrains, Wang et al. [142] proposed a navigation control method that combined a Long Short-Term Memory (LSTM) network with Robust Tube Model Predictive Control (TMPC). This method used LSTM to conduct real-time slope estimation and interference prediction within the prediction range, forming a time-varying probabilistic interference boundary for the TMPC framework. The experimental results showed that TMPC enhanced by LSTM could significantly reduce the lateral tracking error and improve the navigation accuracy and stability of tractors on cross-slope terrains. Liu et al. [143] proposed a machine vision navigation method based on the color of field ridges to solve the problem that traditional agricultural navigation systems could not distinguish the shape of navigation field ridges and could not guide the machinery to operate along these ridges. The experimental results showed that this method could effectively realize the identification of the navigation line of the field ridges.

6.2. Path Tracking Control System of Agricultural Machinery Chassis

Path tracking control technology for agricultural machinery chassis is the key to automatic driving technology for agricultural machinery. Its principle lies in calculating the expected steering angle of a chassis based on the lateral deviation and heading deviation between the agricultural machinery and the preset expected operation path, by means of a specific path tracking control algorithm, and correcting the expected steering angle in a timely manner according to real-time position information and steering angle information from the chassis to obtain the final steering angle control quantity [144]. Through this technology, the operation accuracy and efficiency of agricultural machinery can be improved, while avoiding repeated operations and omissions and reducing agricultural production material waste [145].

A schematic diagram of the path tracking control operation of agricultural machinery chassis is shown in Figure 10.

An efficient path tracking method for agricultural machinery chassis can improve the operation accuracy of self-driving agricultural machinery chassis and enhance their land utilization rate. Cheng et al. [146], based on preview theory, designed a tracking system based on the heading deviation angle. After linearizing the tracking system, they developed an MFAPC tracking system. They referred to the control structure of the incremental PID algorithm, regarded MFAPC as the adaptive integral term, and obtained an MFAPC-PID path tracking controller by adding adaptive proportional and differential terms. Simulations and tests showed that the MFAPC-PID method was insensitive to external disturbances and changes in the controlled object model. Sun et al. [147] proposed a path tracking control scheme by using the fixed-time nonsingular terminal sliding mode and adaptive disturbance observer technology. They designed a fixed-time terminal sliding mode controller for unmanned agricultural tractors, which effectively improved the dynamic performance and reduced the chattering effect, and used an adaptive disturbance observer to estimate and compensate for unknown disturbances. Ge et al. [148] proposed an adaptive sliding mode control (ASMC) method for path tracking in unmanned agricultural vehicles. The test results show that the designed ASMC can drive the closed-loop lateral position error to asymptotically converge to zero dynamically. The designed sliding mode observer (SMO) is used to estimate the system state in order to adaptively adjust the uncertain boundary of the cornering stiffness. Li et al. [149] proposed an autonomous rice transplanter path tracking method based on adaptive sliding mode variable structure control in order to deal with the influence of uncertain interference factors, such as sideslip, in the field environment on the path tracking control accuracy of unmanned rice transplanters. This method used a radial basis function (RBF) neural network, which can accurately approximate any nonlinear function, for online parameter self-tuning, and constructed the sliding surface by combining parameter self-tuning with the power reaching law.

Zhang et al. [150] proposed a multi-parameter optimization feedback algorithm to improve the accuracy of fully autonomous operation and path tracking in crawler harvesters. They utilized a nonlinear PID controller to adjust the steering according to real-time tracking and made adjustments based on multi-parameter optimization and different field conditions, thereby improving the dynamic performance and steady-state accuracy of the harvester. In order to reduce the dependence of the path tracking control of agricultural machinery chassis on precision modeling or parameter adjustment, Zhang et al. [151] proposed a path tracking control algorithm based on deep reinforcement learning combined with path curvature. This algorithm constructed a deep Q network (DQN) based on a five-layer backpropagation (BP) neural network. It optimized the input state of the network by integrating the average path curvature within a certain distance in front of the vehicle, thereby improving the path tracking accuracy of agricultural machinery chassis. Lu et al. [152] considered the uncertainty of model parameters and various disturbances, and then established kinematic and dynamic models of an autonomous tractor–trailer system. The model predictive control (MPC) method was adopted in the attitude control layer, the sliding mode control (SMC) method was adopted in the power layer, and a nonlinear disturbance observer (NDO) was designed to estimate various system disturbances and compensate the tracking control system to improve the robustness of the system. To solve the problem that the path tracking and control of unmanned agricultural machinery in paddy fields are rather difficult, He et al. [153] took agricultural machinery bodies in paddy fields as their control object, established a kinematic model of agricultural machinery based on the attitude correction of agricultural machinery, and established an MPC path tracking control method based on the attitude of the agricultural machinery. The test results showed that this path tracking method could effectively suppress the sudden lateral position deviation caused by changes in the relative position and attitude of a machine.

Table 6. Summary of automatic navigation technology and path tracking control methods.

Reference	Automatic Navigation Technology	Reference	Path Tracking Control Method
Li et al. [136]	A system based on the integration of Global Navigation Satellite System (GNSS) positioning equipment and an inertial measurement unit, integrating accelerometers and gyroscopes.	Cheng et al. [146]	A tracking system based on the heading deviation angle and a MFAPC-PID path tracking controller.
Cui et al. [137]	An unmanned tractor automatic navigation system based on dynamic path search and the Fuzzy Stanley Model (FSM).	Sun et al. [147]	A fixed-time terminal sliding mode controller for unmanned agricultural tractors based on the fixed-time nonsingular terminal sliding mode and adaptive disturbance observer technology.
Chen et al. [138]	A navigation control system for combine harvesters based on the fusion of visual simultaneous localization and mapping (SLAM) and inertial guidance.	Ge et al. [148]	An adaptive sliding mode control (ASMC) method for path tracking in unmanned agricultural vehicles.
Li et al. [139]	An operation breakpoint identification algorithm combining the real-time dynamic Global Navigation Satellite System (RTK-GNSS) and a wheel odometer based on the spray lag compensation algorithm.	Li et al. [149]	An autonomous rice transplanter path tracking method based on adaptive sliding mode variable structure control.
Nakaguchi et al. [140]	A deep learning machine stereo vision guidance system.	Zhang et al. [150]	A multi-parameter optimization feedback algorithm to improve the accuracy of fully autonomous operation and path tracking of crawler harvesters.
Thanpattranon et al. [141]	A single-sensor navigation control algorithm and a control scheme for stopping a tractor–trailer using a laser rangefinder (LRF) in various field tasks.	Zhang et al. [151]	A path tracking control algorithm based on deep reinforcement learning combined with path curvature.
Wang et al. [142]	A navigation control method that combines a Long Short-Term Memory (LSTM) network with Robust Tube Model Predictive Control (TMPC).	Lu et al. [152]	The model predictive control (MPC) method was adopted in the attitude control layer, the sliding mode control (SMC) method was adopted in the power layer, and a nonlinear disturbance observer (NDO) was designed.
Liu et al. [143]	A machine vision navigation method based on the color of field ridges.	He et al. [153]	An MPC path tracking control method based on the attitude of agricultural machinery.

summarizes the above-mentioned automatic navigation technologies and path tracking control methods.

In summary, in their research on automatic navigation technology, scholars have designed high-precision navigation systems and integrated them with various functionally advanced sensors to further improve the accuracy and stability of the automatic navigation of agricultural machinery chassis. In their research into path tracking control technology, scholars have proposed various path tracking control methods with excellent control effects, conducted experimental tests on the path tracking control effects of these control methods, and continuously optimized the algorithms through using the test results as feedback. In the future, with the further maturity and cost reduction in automatic navigation and path tracking control technologies, these systems will play greater roles in improving agricultural operation efficiency, reducing the intensity of manual labor, and achieving precision agriculture.

7. Conclusions and Prospects

This article discussed the research and application of five subsystems of agricultural machinery power chassis in hilly and mountainous areas, namely their power systems, walking systems, steering systems, leveling systems, and automatic navigation and path tracking control systems. The power system is the core component of agricultural machinery chassis. Its function is to provide power for agricultural machinery and achieve the transmission, distribution, and control of energy, ensuring the efficient operation of agricultural machinery in different working scenarios. The realization of the working functions of the other subsystems requires the support of the power system. The walking system of an agricultural machinery chassis is mainly responsible for supporting the weight of the whole machine, transmitting the driving force, enabling the movement and steering of the agricultural machinery, and adapting to different terrains and operation requirements. Its functions cover multiple aspects, such as mechanical support, power transmission, handling stability, and environmental adaptability. The steering system of an agricultural machinery chassis is the core subsystem that ensures the flexible operation and precise work of the agricultural machinery. Its functions are not limited to changing the traveling direction, but also involve operational stability, terrain adaptability, and intelligent control. The agricultural machinery chassis leveling system is a key subsystem that ensures the agricultural machinery maintains a horizontal body during operation on complex terrains. Its functions cover multiple aspects, including stability maintenance, the improvement of operation accuracy, the enhancement of safety, and equipment protection. Automatic navigation and path tracking control systems for agricultural machinery chassis are the core technologies required to achieve precision agriculture. Through the coordination of high-precision positioning, intelligent algorithms, and actuators, they ensure that agricultural machinery can autonomously complete preset operation tasks, thereby improving efficiency, accuracy, and resource utilization rate. The overall operating performance of agricultural machinery chassis benefits from the integration of the functions of each subsystem. Each subsystem works together to ensure that agricultural machinery chassis can complete complex agricultural operation tasks.

With the continuous advancement of global agricultural modernization, the research and application of agricultural machinery power chassis in hilly and mountainous areas are ushering in new development opportunities and challenges. In the future, research in this field will unfold around the directions of intelligence, greening, lightweighting, and multi-functionality, in order to meet the needs of complex terrains and diverse agricultural operations in hilly and mountainous areas.

The future development trends of agricultural machinery chassis in hilly and mountainous areas are as follows:

(1)

Intelligence and automation.

Intelligence is one of the cores of the future development of agricultural machinery. With the rapid development of technologies such as artificial intelligence, the Internet of Things, and big data, the chassis of agricultural machinery in hilly and mountainous areas will gradually achieve intelligence and automation. Future power chassis will be equipped with advanced sensors, navigation systems, and control algorithms, which will be able to sense the operating environment in real time, automatically plan paths, and precisely control operating parameters, thereby achieving unmanned or semi-unmanned operations. Furthermore, intelligent power chassis will also be deeply integrated with the agricultural Internet of Things to achieve remote monitoring, fault diagnosis, and the intelligent scheduling of agricultural machinery equipment. Through cloud data platforms, farmers will be able to grasp the working status and operation progress of agricultural machinery in real time, optimize resource allocation, and reduce operating costs.

Future research can be carried out in the following aspects: Strengthening the integration of satellite navigation systems with technologies such as inertial navigation systems and multi-source heterogeneous sensors to improve the adaptability of agricultural machinery chassis in complex environments. Researching real-time environmental perception algorithms based on deep learning and machine vision to improve the accuracy of target recognition in complex hilly and mountainous area scenarios. Researching advanced adaptive control algorithms such as reinforcement learning to achieve autonomous decision-making in different operation scenarios. In order to apply advanced technologies, actual production needs should be fully considered during the research process.

(2)

Greening and energy saving.

Under the background of global climate change and resource shortage, greening and energy saving have become important trends in the development of agricultural machinery. In the future, the chassis of agricultural machinery in hilly and mountainous areas will pay more attention to environmental protection performance and energy efficiency in order to reduce their negative impact on the environment and lower their operating costs.

On the one hand, the application of new energy technologies will become an important research direction. The electric power chassis has broad application prospects in hilly and mountainous areas due to its characteristics of releasing zero emissions, being low noise, and having high energy efficiency. With the continuous progress of battery technology, the endurance and power performance of electric agricultural machinery will be significantly improved, gradually replacing traditional fuel-powered chassis. On the other hand, energy-saving designs of power chassis will be further optimized. Through the use of lightweight materials, the intelligent adjustment of power systems, and the application of energy recovery technology, future power chassis will significantly reduce their energy consumption and their improve energy utilization efficiency.

For electric agricultural machinery, in the future, a battery system with high energy density can be developed, and fast charging technology can be studied in order to address the problem of the difficulty of charging electric agricultural machinery in hilly and mountainous areas. To meet the power requirements of agricultural machinery chassis, developing a dedicated engine for hybrid electric vehicles is a viable research direction. In addition, intelligent energy management strategies should be developed to improve the energy utilization efficiency and endurance of agricultural machinery chassis.

(3)

Lightweighting and generalization.

The terrain in hilly and mountainous areas is complex, which puts higher requirements on the weight and flexibility of agricultural machinery. Future power chassis technology will pay more attention to lightweight designs to reduce the self-weight of the equipment and improve the chassis’ passability and operation efficiency. The wide application of new high-strength materials (such as carbon fiber composite materials, aluminum alloys, etc.) will significantly reduce the weight of power chassis while maintaining their structural strength and durability. In addition, generalized design will become an important development direction for future power chassis technology. Through generalized design, power chassis will be able to quickly replace functional modules according to different operation requirements in order to achieve the multi-purpose use of one machine. For example, the same chassis could be equipped with different operation modules such as those for seeding, fertilizing, and harvesting to meet the needs of diverse agricultural operations in hilly and mountainous areas. Generalized design not only improves the utilization rate of equipment but also reduces the purchase and maintenance costs for farmers.

In the future, in terms of the lightweighting of agricultural machinery chassis, efforts should be focused on breaking through the application of new materials (such as carbon fiber composites and high-strength aluminum alloys) and optimized structural design (topology optimization, bionic configuration). By utilizing advanced processes such as additive manufacturing, chassis weights can be reduced without sacrificing bearing strength. In the direction of generalization, it is necessary to construct a modular platform architecture, formulate standardized mechanical, hydraulic, and electrical interfaces, and develop an adaptive suspension and power matching system, so that a single chassis can be quickly adapted to various types of agricultural tools such as sowing, harvesting, and plant protection, meeting the diverse needs of modern agriculture.

Overall, the future development of power chassis for agricultural machinery in hilly and mountainous areas is full of opportunities and challenges. With the continuous progress of technology and the deepening of its application, the chassis of agricultural machinery in hilly and mountainous areas will become important engines to promote the high-quality development of agriculture and make important contributions to achieving sustainable agricultural development and agricultural production.