Search Results (48)

Sensor fusion in intelligent suspension systems constitutes a fundamental technology for optimizing vehicle dynamic stability, ride comfort, and occupant safety. By integrating data from multiple sensor modalities, this study proposes a hierarchical multi-sensor fusion framework for active suspension control, aiming to enhance control precision. Initially, a binocular vision system is employed for target detection, enabling the identification of lane curvature initiation points and speed bumps, with real-time distance measurements. Subsequently, the integration of Global Positioning System (GPS) and inertial measurement unit (IMU) data facilitates the extraction of road elevation profiles ahead of the vehicle. A BP-PID control strategy is implemented to formulate mode-switching rules for the active suspension under three distinct road conditions: flat road, curved road, and obstacle road. Additionally, an ant colony optimization algorithm is utilized to fine-tune four suspension parameters. Utilizing the hardware-in-the-loop (HIL) simulation platform, the observed reductions in vertical, pitch, and roll accelerations were 5.37%, 9.63%, and 11.58%, respectively, thereby substantiating the efficacy and robustness of this approach. Full article

With the development of electrification and intelligent technologies in vehicles, ride comfort issues represented by motion sickness have become a key constraint on the performance of autonomous driving. The occurrence of motion sickness is influenced by the comprehensive movement of the vehicle in the longitudinal, lateral, and vertical directions, involving ACC, LKA, active suspension, etc. Existing motion sickness control method focuses on optimizing the longitudinal, lateral, and vertical directions separately, or coordinating the optimization control of the longitudinal and lateral directions, while there is relatively little research on the coupling effect and coupled optimization of the longitudinal and vertical directions. This study proposes a coupled framework of ACC and active suspension control system based on MPC. By adding pitch angle changes caused by longitudinal acceleration to the suspension model, a coupled state equation of half-car vertical dynamics and ACC longitudinal dynamics is constructed to achieve integrated optimization of ACC and suspension for motion suppression. The suspension active forces and vehicle acceleration are regulated coordinately to optimize vehicle vertical, longitudinal, and pitch dynamics simultaneously. Simulation experiments show that compared to decoupled control of ACC and suspension, the integrated control framework can be more effective. The research results confirm that the dynamic coordination between the suspension and ACC system can effectively suppress the motion sickness, providing a new idea for solving the comfort conflict in the human vehicle environment coupling system. Full article

In response to the challenges associated with weld treatment during the on-site corrosion protection of hydraulic steel gates, this paper proposes a method utilizing a magnetic adsorption climbing robot to perform corrosion protection operations. Firstly, a magnetic adsorption climbing robot with a multi-wheel independent drive configuration is proposed as a mobile platform. The robot body consists of six joint modules, with the two middle joints featuring adjustable suspension. The joints are connected in series via an EtherCAT bus communication system. Secondly, the kinematic model of the climbing robot is analyzed and a PID trajectory tracking control method is designed, based on the kinematic model and trajectory deviation information collected by the vision system. Subsequently, the proposed kinematic model and trajectory tracking control method are validated through Python3 simulation and actual operation tests on a curved trajectory, demonstrating the rationality of the designed PID controller and control parameters. Finally, an intelligent software system for weld defect detection based on computer vision is developed. This system is demonstrated to conduct defect detection on images of the current weld position using a trained model. Full article

Rear-end collisions represent a major concern in automotive safety, particularly due to the risk of whiplash injuries among vehicle occupants. The accurate simulation of occupant kinematics during such impacts is critical for the development of advanced safety systems. This paper presents an enhanced multibody simulation model specifically designed for rear-end crash scenarios, incorporating integrated active headrest mechanisms and sensor-based activation logic. The model combines detailed representations of vehicle structures, suspension systems, restraint systems, and occupant biomechanics, allowing for the precise prediction of crash dynamics and occupant responses. The system was developed using Simscape Multibody, with CAD-derived components interconnected through physical joints and validated using controlled experimental crash tests. Special attention was given to modelling contact forces, suspension behaviour, and actuator response times for the active headrest system. The model achieved a root mean square error (RMSE) of 4.19 m/s² and a mean absolute percentage error (MAPE) of 0.71% when comparing head acceleration in frontal collision tests, confirming its high accuracy. Validation results demonstrate that the model accurately reproduces occupant kinematics and head acceleration profiles, confirming its reliability and effectiveness as a predictive tool. This research highlights the critical role of integrated sensor-actuator systems in improving occupant safety and provides a flexible platform for future studies on intelligent vehicle safety technologies. Full article

Distributed-drive electric vehicles (DDEVs), through independent, rapid, and precise control of the driving/braking torque of each wheel, offer unprecedented opportunities to enhance their handling stability, ride comfort, energy economy, and safety. However, their inherent over-actuation characteristics and multi-degree-of-freedom motion coupling pose significant challenges to the vehicle chassis control system. Chassis coordinated control, by coordinating multiple subsystems such as drive, braking, steering, and suspension, has become a key technology to fully leverage the advantages of distributed drive and address its challenges. This paper reviews the core issues in chassis coordinated control for DDEVs, comparatively analyzes several distributed electric drive coordinated control architectures, and systematically outlines recent research progress in lateral–longitudinal, lateral–vertical, longitudinal–vertical, and combined three-dimensional (lateral–longitudinal–vertical) coordinated control, including control architectures, key technologies, commonly used algorithms, and control allocation strategies. By analyzing and comparing the advantages, disadvantages, and application scenarios of different coordinated control schemes, this paper summarizes the key scientific problems and technical bottlenecks in this field and looks forward to development trends in intelligence, integration, and scenario-based fusion, aiming to provide a reference for the development of high-performance chassis control technology for DDEVs. Full article

This paper presents a novel control design methodology for a magnetorheological (MR) damper-based semi-active suspension system operating under communication-induced time delays, which introduce nonlinear sampled-data dynamics. To address these challenges, a linear matrix inequality (LMI) framework is developed for synthesizing the current controller, with the dual goals of enhancing ride comfort and safety while ensuring system stability and robustness against road disturbances. The proposed approach deliberately avoids the use of Lyapunov-Krasovskii functionals, offering a more practical and computationally efficient alternative. Experimental results confirm that the proposed MR damper model outperforms traditional Lyapunov-Krasovskii-based methods. Additionally, two simulated road profiles are used to evaluate the suspension system’s behavior, further demonstrating the effectiveness of the proposed control strategy. Full article

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. Full article

The online dynamic collection of irrigation and plant physiological information is crucial for the precise irrigation management of nutrient solutions and efficient crop cultivation in vegetable soilless substrate cultivation facilities. In this study, an intelligent weighing system was installed in a tomato substrate cultivation greenhouse. The monitored values from the intelligent weighing system’s pressure-type module were used to calculate irrigation start–stop times, frequency, volume, drainage volume, drainage rate, evapotranspiration, evapotranspiration rate, and stomatal conductance. In contrast, the monitored values of the suspension-type weighing module were used to calculate the amount of weight change in the plants, which supported the dynamic and quantitative characterization of substrate cultivation irrigation and crop growth based on an intelligent weighing system. The results showed that the monitoring curves of pressure and flow sensors based on the pressure-type module could accurately identify the irrigation start time and number of irrigations and calculate the irrigation volume, drainage volume, and drainage rate. The calculated irrigation amount was closely aligned with that determined by an integrated-water–fertilizer automatic control system (R² = 0.923; mean absolute error (MAE) = 0.105 mL; root-mean-square error (RMSE) = 0.132 mL). Furthermore, transpiration rate and leaf stomatal conductance were obtained through inversion, and the R², MAE, and RMSE of the extinction coefficient correction model were 0.820, 0.014 mol·m⁻²·s⁻¹, and 0.017 mol·m⁻²·s⁻¹, respectively. Compared to traditional estimation methods, the MAE and RMSE decreased by 12.5% and 15.0%, respectively. The measured values of fruit picking and leaf stripping linearly fitted with the calculated values of the suspended weighing module, and R², MAE, and RMSE were 0.958, 0.145 g, and 0.143 g, respectively. This indicated that data collection based on the suspension-type weighing module could allow for a dynamic analysis of plant weight changes and fruit yield. In summary, the intelligent weighing system could accurately analyze irrigation information and crop growth physiological indicators under the practical application conditions of facility vegetable substrate cultivation, providing technical support for the precise management of nutrient solutions. Full article

Despite significant advancements in roll stability control for individual vehicle types, comparative research across on-road and off-road vehicles remains limited, hindering cross-disciplinary innovation. This study bridges this gap by systematically analyzing roll stability control in both vehicle categories, focusing on theoretical foundations, key technologies, and experimental validation methods. On-road vehicles rely on mature technologies like active suspension, braking, and steering, which enhance safety through sensor monitoring, rollover prediction, and integrated stability control. Validation is primarily performed through hardware-in-the-loop simulations and on-road testing. Off-road vehicles, operating in more complex environments with dynamic load changes and rugged terrain, emphasize adaptive leveling, direct torque control, and active steering. Their stability control strategies must also account for terrain irregularities, real-time load shifts, and extreme slopes, validated through scaled-model tests and field trials. Comparative analysis reveals that while both vehicle types face similar challenges, their control strategies differ significantly: on-road vehicles focus on handling and high-speed stability, while off-road vehicles require more robust, adaptive mechanisms to manage environmental uncertainties. Future research should explore multi-system collaborative control, such as integrating active suspension with intelligent terrain perception, to improve adaptability and robustness across both vehicle categories. Furthermore, the integration of machine learning and advanced predictive algorithms promises to enhance the intelligence and versatility of roll stability control systems. Full article

With the advancement of vehicle electrification and intelligence, changes in powertrain system architecture and the addition of battery components have significantly deteriorated vehicle dynamics characteristics. Adjustable damping dampers improve vehicle performance by modifying suspension damping characteristics. Therefore, in this paper, a high-precision multi-dimensional vehicle performance representation model is developed, which includes the dynamics models of the steering and suspension systems and utilizes test results to reflect the constitutive properties of the complex nonlinear structures. The influence regularities of the suspension damping ratio on ride comfort and handling stability are analyzed, and a hybrid control strategy is proposed, which utilizes acceleration-driven damper–Skyhook control logic for vertical control and trigger rule-based control logic for lateral and longitudinal control. Finally, the effectiveness of the control strategy is validated through ride comfort and handling stability tests. The results show that the proposed hybrid control strategy significantly reduces vehicle body vibration acceleration and improves vehicle dynamic response during steering and acceleration/braking conditions. Full article

Mecanum and omni wheel-based assistive technologies present an alternative to conventional mobility devices for individuals with motor impairments, owing to their omnidirectional movement capabilities and high maneuverability in constrained environments. This systematic review identifies and categorizes the key challenges and emerging trends in the development of such systems. Primary obstacles include limited stability and maneuverability on uneven terrain, high energy consumption, complex control requirements, and elevated production costs. In response, recent studies have introduced several innovative approaches, such as advanced suspension systems to enhance terrain adaptability, modular mechanical designs to reduce manufacturing complexity, energy-efficient motor control strategies such as field-oriented control, AI-driven autonomous navigation, and hands-free user interfaces—including gesture recognition and brain–computer interfaces. By synthesizing findings from 26 peer-reviewed studies, this review outlines current technical limitations, surveys state-of-the-art solutions, and offers strategic recommendations to inform future research in intelligent assistive mobility technologies. Full article

This study examines the smoothness of an intelligent chassis for electric vehicles, analyzes the chassis structure and configuration, and considers the impacts of the primary energy subsystem, electric drive subsystem, and auxiliary control subsystem on smoothness. The influence of suspension parameters on smoothness is examined, highlighting the significance of elastic element stiffness and the shock absorber damping ratio. Dynamic models of quarter- and half-car suspension systems, as well as a comprehensive nine-degree-of-freedom vehicle model, are developed to examine the vibration characteristics under varying road conditions. The chassis suspension dynamic model is developed, simulated, and analyzed using ADAMS/View software 2024. The suspension damping value is optimized with the ADAMS/PostProcessor tool, revealing that smoothness can be enhanced by judiciously decreasing the damping value. The article discusses the human body’s reaction to vibration and assessment metrics, referencing worldwide standards to establish a foundation for evaluation. The study offers theoretical backing for the design and optimization of an intelligent chassis, hence advancing the technological development of electric vehicles. Full article

This paper aims to present a hybrid steering control method combining the self-guidance capability of a wheelset and fuzzy logic controller (FLC), which were applied to our new micro-autonomous railway inspection vehicle, enhancing the vehicle’s stability. The vehicle features intelligent inspection systems and a suspension system with variable damping capability that uses smart magnetorheological fluid to control vertical oscillations. A mathematical model of the steering dynamic system was developed based on the vehicle’s unique structure. Two simulation models of the vehicle were built on Simpack and Simulink to evaluate the lateral dynamic capability of the wheelset, applying Hertzian normal theory and Kalker’s linear theory. The hybrid steering control was designed to adjust the torque differential of the two front-wheel drive motors of the vehicle to keep the vehicle centered on the track during operation. The control simulation results show that this hybrid control system has better performance than an uncontrolled vehicle, effectively keeps the car on the track centerline with deviation below 10% under working conditions, and takes advantage of the natural self-guiding force of the wheelset. In conclusion, the proposed hybrid steering system controller demonstrates stable and efficient operation and meets the working requirements of intelligent track inspection systems installed on vehicles. Full article

This paper systematically reviews research progress in semi-active suspension systems for agricultural machinery seats, focusing on key technologies and methods to enhance ride comfort. First, through an analysis of the comfort evaluation indicators and constraints of seat suspension systems, the current applications of variable stiffness and damping components, as well as semi-active control technologies, are outlined. Second, a comparative analysis of single control methods (such as PID control, fuzzy control, and sliding mode control) and composite control methods (such as fuzzy PID control, intelligent algorithm-based integrated control, and fuzzy sliding mode control) is conducted, with control mechanisms explained using principle block diagrams. Furthermore, key technical challenges in current research are summarized, including dynamic characteristic optimization design, adaptability to complex operating environments, and the robustness of control algorithms. Further research could explore the refinement of composite control strategies, the integrated application of intelligent materials, and the development of intelligent vibration damping technologies. This paper provides theoretical references for the optimization design and engineering practice of agricultural machinery suspension systems. Full article

The optimization of independent automotive suspension systems, which is one of the main pillars of the vehicle performance and comfort, is currently going through a revolutionary change due to the development of artificial intelligence and quantum computing. This paper aims to review the multi-objective optimization of suspension parameters including camber, caster, and toe to discuss the complex design issues that arise from geometric and dynamic considerations. Some of the most common computational methodologies, which are Genetic Algorithms, Particle Swarm Optimization, and Gradient Descent, are discussed in this paper along with the new quantum computing techniques such as Gate-Based quantum computing and Quantum Annealing (QA). In addition, this review incorporates information from the practice of automotive manufacturers who have incorporated the use of artificial intelligence and quantum computing in their suspension systems. However, there are still some issues remaining, such as the computational cost, real-time flexibility, and the applicability of theoretical concepts to actual engineering structures. Some potential future research directions are introduced in this paper, such as hybrid optimization approaches, quantum techniques, and adaptive materials, which are considered as potential directions for future development. This systematic review presents a conceptual framework for researchers and engineers to follow, stressing the importance of interdisciplinarity in the development of intelligent suspension systems with performance objectives that are capable of adjusting to various road conditions. The findings of this work underscore the growing importance of complex computational techniques in modern automotive industry and highlight their potential to shape future developments based on emerging trends and industry practices. Full article