Search Results (141)

Traditional vehicle emergency braking research suffers from inaccurate maximum road adhesion coefficient identification and suboptimal wheel slip ratio control. To address these challenges in electronic hydraulic braking systems’ anti-lock braking technology, firstly, this paper proposes a CDOA-SENet-CNN neural network to precisely estimate the maximum road adhesion coefficient by monitoring and analyzing the braking process. Secondly, correlation curves between peak adhesion coefficients and ideal slip ratios are established using the Burckhardt model and CarSim 2020, and the estimated maximum adhesion coefficient from the CDOA-SENet-CNN network is used with these curves to determine the optimal slip ratio for the single-neuron integral sliding mode control (SNISMC) algorithm. Finally, an SNISMC control strategy is developed to adjust the wheel slip ratio to the optimal value, achieving stable wheel control across diverse road surfaces. Results indicate that the CDOA-SENet-CNN network rapidly and accurately estimates the peak braking surface adhesion coefficient. The SNISMC control strategy significantly enhances wheel slip ratio control, consequently increasing the effectiveness of vehicle brakes. This paper introduces an innovative, stable, and efficient solution for enhancing vehicle braking safety. Full article

The design and optimization of drive distribution strategies are critical for enhancing the performance of Formula Student electric racing cars, which face demanding operational conditions such as rapid acceleration, tight cornering, and variable track surfaces. Given the increasing complexity of racing environments and the need for adaptive control solutions, a multi-mode adaptive drive distribution strategy for four-wheel-drive Formula Student electric racing cars is proposed in this study to meet specialized operational demands. Based on the dynamic characteristics of standardized test scenarios (e.g., straight-line acceleration and figure-eight loop), two control modes are designed: slip-ratio-based anti-slip control for longitudinal dynamics and direct yaw moment control for lateral stability. A CarSim–Simulink co-simulation platform is established, with test scenarios conforming to competition standards, including variable road adhesion coefficients (μ is 0.3–0.9) and composite curves. Simulation results indicate that, compared to conventional PID control, the proposed strategy reduces the peak slip ratio to the optimal range of 18% during acceleration and enhances lateral stability in the figure-eight loop, maintaining the sideslip angle around −0.3°. These findings demonstrate the potential for significant improvements in both performance and safety, offering a scalable framework for future developments in racing vehicle control systems. Full article

This paper addresses the issues of sensor failures and actuator faults in mining tracked mobile vehicles (TMVs) operating in harsh environments by proposing a global fixed-time fault-tolerant control strategy based on a hierarchical unknown input observer structure. First, a kinematic and dynamic model of the TMV is established considering side slip and track slip, and its linear parameter-varying (LPV) model is constructed through parameter-dependent linearization. Then, a distributed structure consisting of four collaborating low-dimensional observers is designed, including a state observer, a disturbance observer, a position sensor fault observer, and a wheel speed sensor fault observer, and the fixed-time convergence of the closed-loop system is proven. Additionally, by equivalently treating actuator faults as power losses, an observer capable of identifying and compensating for motor efficiency losses is designed. Finally, an adaptive fault-tolerant control law is proposed by combining nominal control, disturbance compensation, and sliding mode switching terms, achieving global fixed-time stability and fault tolerance. Experimental results demonstrate that the proposed control system maintains excellent trajectory tracking performance even in the presence of sensor faults and actuator power losses, with tracking errors less than 0.1 m. Full article

Aiming at the problems of energy utilization efficiency and braking stability in electric vehicles, a high-efficiency and energy-saving control strategy that takes both driving and braking into account is proposed with the distributed hub motor-driven vehicle as the research object. Under regular driving and braking conditions, the front and rear axle torque distribution coefficients are optimized by an adaptive particle swarm algorithm based on simulated annealing and a multi-objective co-optimization strategy based on variable weight coefficients, respectively. During emergency braking, the anti-lock braking strategy (ABS) based on sliding mode control realizes the independent distribution of torque among four wheels. The joint simulation verification based on MATLAB R2023a/Simulink-Carsim 2020.0 shows that under World Light Vehicle Test Cycle (WLTC) conditions, the optimization strategy reduces the driving energy consumption by 3.20% and 2.00%, respectively, compared with the average allocation and the traditional strategy. The braking recovery energy increases by 4.07% compared with the fixed proportion allocation, improving the energy utilization rate of the entire vehicle. The wheel slip rate can be quickly stabilized near the optimal value during emergency braking under different adhesion coefficients, which ensures the braking stability of the vehicle. The effectiveness of the strategy is verified. Full article

The presence of a bogie in three-axle vehicles when moving along a curved trajectory leads to deterioration in its handling and maneuverability. The paper developed a mathematical model of the elastic bogie wheel while moving along a curvilinear trajectory, according to which the bogie wheel simultaneously participates in curvilinear and plane-parallel motion with a slip angle. Such movement of the bogie wheels develops significant lateral and longitudinal forces on the steered wheels, which leads to the movement of the steered wheels with slip, redistribution of the load on them, tire twisting, and a decrease in the steering angle of the outside steered wheel due to the elasticity of the steering trapezoid. Based on the mathematical model of the bogie wheel, an analytical dependence was obtained to determine the minimum turning radius of a three-axle vehicle. The reliability of the analytical dependencies characterizing the movement of the bogie wheel along a curvilinear trajectory was determined by comparing the minimum turning radii of a three-axle vehicle with the intermediate axle lowered and raised. It has been established that the minimum turning radius of a vehicle with a bogie increases compared to a two-axle vehicle and depends on the cornering stiffnesses of the tires of the bogie and steered wheels, the bogie and vehicle wheelbases, the kinematic and elastic parameters of the steering trapezoid, the direction of turning of the steered wheels, and the load on the steered and the bogie wheels. Full article

To enhance the precision and real-time performance of trajectory tracking control in differential-steering intelligent sweeping robots and to improve the adaptability of the control algorithm to errors caused by sensor noise, tire slip, and skid, an MPC-PID (Model Predictive Control–Proportional-Integral-Derivative) dual closed-loop control strategy was proposed. This strategy integrates a Kalman filter-based state estimator and a sliding compensation module. Based on the kinematic model of the intelligent sweeping robot, a model predictive controller (MPC) was designed to regulate the vehicle’s pose, while a PID controller was used to adjust the longitudinal speed, forming a dual closed-loop control algorithm. A Kalman filter was employed for state estimation, and a sliding compensation module was introduced to mitigate wheel slip and lateral drift, thereby improving the stability of the control system. Simulation results demonstrated that, compared to traditional MPC control, the maximum lateral deviation, maximum heading angle deviation, and speed response time were reduced by 50.83%, 53.65%, and 7.10%, respectively, during sweeping operations. In normal driving conditions, these parameters were improved by 41.58%, 45.54%, and 24.17%, respectively. Experimental validation on an intelligent sweeper platform demonstrates that the proposed algorithm achieves a 16.48% reduction in maximum lateral deviation and 9.52% faster speed response time compared to traditional MPC, effectively validating its enhanced tracking effectiveness in intelligent cleaning operations. Full article

This paper presents a method for designing a static output feedback integrated path tracking controller for autonomous vehicles. For path tracking, state–space model-based control methods, such as linear quadratic regulator, H_∞ control, sliding mode control, and model predictive control, have been selected as controller design methodologies. However, these methods adopt full-state feedback. Among the state variables, the lateral velocity, or the side-slip angle, is hard to measure in real vehicles. To cope with this problem, it is desirable to use a state estimator or static output feedback (SOF) control. In this paper, an SOF control is selected as the controller structure. To design the SOF controller, a linear quadratic optimal control and sliding mode control are adopted as controller design methodologies. Front wheel steering (FWS), rear wheel steering (RWS), four-wheel steering (4WS), four-wheel independent braking (4WIB), and driving (4WID) are adopted as actuators for path tracking and integrated as several actuator configurations. For better performance, a lookahead or preview function is introduced into the state–space model built for path tracking. To verify the performance of the SOF path tracking controller, simulations are conducted on vehicle simulation software. From the simulation results, it is shown that the SOF path tracking controller presented in this paper is effective for path tracking with limited sensor outputs. Full article

To enhance the balance between lateral stability and energy efficiency, we propose an adaptive compound controller based on phase plane analysis for four-wheel independent drive electric vehicles (4WID-EVs). The adaptive stability and energy-saving controller (SEC) is designed with a three-layer structure. The upper-layer controller employs model predictive control (MPC) to compute the external yaw moment based on the desired yaw rate and side slip angle derived from a reference model. The adaptive-layer controller utilizes a phase plane diagram to evaluate vehicle stability and reduces unnecessary external yaw moment consumption by accounting for the vehicle’s steering state and battery’s state-of-charge (SOC) level. The lower-layer controller implements an optimal torque distribution algorithm to minimize an objective function that considers tire workload, energy consumption, and smooth motor control. Numerical simulations are performed in MATLAB/Simulink using three distinct steering angles to evaluate the performance of the proposed control strategy. At each steering angle, the SEC’s stability and energy efficiency are compared to those of the energy-saving controller (EC) and stability controller (SC) under varying battery charge levels. The results indicate that, at small steering angles, the vehicle operates in a highly stable state, enabling a reduction in the external yaw moment to achieve substantial energy savings. As the steering angle increases, the vehicle approaches a critical stability state, where the external yaw moment is applied to maintain lateral stability. Furthermore, as the SOC decreases, the SEC strategy will increasingly prioritize energy savings. Simulation results verify that the SEC strategy effectively balances lateral stability and energy savings while maintaining consistent performance across a range of operating conditions. Full article

This paper describes an integrated control strategy that utilizes semi-active suspension and differential braking to enhance lateral stability while maintaining roll performance. The integrated control architecture adopts a hierarchical structure consisting of an estimator, a supervisor, a controller, and an allocator. In the estimation layer, an algorithm is proposed to robustly estimate the side slip angle and roll angle in various situations. The control mode is established by the supervision layer based on the state of the vehicle. The maneuverability mode tracks the driver’s intentions, and the lateral stability mode ensures the vehicle’s stability. Reference values such as yaw rate and roll angle are determined by the control mode. In the controller layer, the yaw and roll moments are generated using sliding mode control to achieve the target yaw rate and roll angle. Brake torque and suspension damping force are distributed to each wheel in the allocator layer. In particular, a damping distribution method based on the roll region index is proposed. The proposed method is compared with conventional methods, such as full stiff damping and yaw-assisted damping, through simulation and real-world evaluation. The tests demonstrate that the proposed approach enhances lateral and roll stability, particularly regarding maximum side slip and roll angle values. The roll-region-index-based distribution method reduces the maximum roll angle by about 17.4% and the maximum side slip angle by about 8.7% compared to each conventional method. Compared to conventional methods, the proposed method showed more stable driving performance by ensuring stability in both directions in extreme lane change situations. Full article

The movement of a wheeled vehicle is a non-regular dynamic process characterized by a large number of states that depend on the movement conditions. This movement involves a large number of situations where elastic tires skid and slip against the base surface. This reduces the efficiency of movement as useful mechanical energy of the electromechanical drive is spent to overcome the increased skidding and slipping. Complete sliding results in the loss of control over the vehicle, which is unsafe. Processes that take place immediately before such phenomena are of special interest as their parameters can be useful in diagnostics and control. Additionally, such situations involve adverse oscillatory processes that cause additional dynamic mechanical and electrical loading in the electromechanical drive that can result in its failure. The authors provide the results of laboratory road research into the emergence of self-oscillatory phenomena during the rolling of a wheel with increased skidding on the base surface and a low traction factor. This paper reviews the methods of designing an anti-slip control system for wheels with an oscillation damping function and studies the applicability and efficiency of the suggested method using mathematical simulation of the virtual vehicle operation in the Matlab Simulink software package. Using the self-oscillation suppression algorithm in the control system helps reduce the maximum amplitude values by 5 times and average amplitudes by 2.5 times while preventing the moment operator from changing. The maximum values of current oscillation amplitude during algorithm changes were reduced by 2.5 times, while the current change rate was reduced by 3 times. The reduction in the current-change amplitude and rate proves the efficiency of the self-oscillation suppression algorithm. The high change rate of the current consumed by the drive’s inverters may have a negative impact on the remaining operating life of the rechargeable electric power storage system. This impact increases with the proximity of its location due to the low inductance of the connecting lines and the operating parameters, and the useful life of the components of the autonomous voltage inverters. Full article

To improve the slip rate control effect for different road conditions during emergency braking of wheel hub motor vehicles, as well as to address the problems of uncertainty and nonlinearity of the system when the electro-mechanical braking system is used as the actuator of the ABS, a hierarchical control strategy of the anti-lock braking system (ABS) using active disturbance rejection control (ADRC) is proposed. Firstly, a vehicle dynamics model and an ABS model based on the EMB system are established; secondly, a speed observer based on the dilated state observer is used in the upper layer to design a pavement recognition algorithm, which recognizes the current pavement and outputs the optimal slip rate; then, an ABS controller based on the ADRC algorithm is designed for the lower layer to track the optimal slip rate. In order to verify the performance of the pavement recognition method and control strategy, vehicle simulation software is used to establish the model and simulation. The results show that the road surface recognition method can quickly and effectively recognize the road surface, and comparing the emergency braking control effects of PID and SMC under different road surface conditions, the ADRC strategy has better robustness and reliability, and improves the braking effect. Full article

An integrated navigation system is a promising solution to improve positioning performance by complementing estimated positioning in each sensor, such as a global positioning system (GPS), an inertial measurement unit (IMU), and an odometer sensor. However, under GPS-disabled environments, such as urban canyons or tunnels where the GPS signals are difficult to receive, the positioning performance of the integrated navigation system decreases. Therefore, deep learning-based integrated navigation systems have been proposed to ensure seamless localization under various positioning conditions. Nevertheless, the conventional deep learning-based systems are applied with a lack of consideration of context features on surface condition, wheel slip, and movement pattern, which are factors causing positioning performance. In this paper, a context-aware integrated navigation system (CAINS) is proposed to ensure seamless localization, especially under GPS-disabled conditions. In the proposed CAINS, two deep learning layers are designed with context-aware and state estimation layers. The context-aware layer extracts vehicle context features from IMU data, while the state estimation layer predicts the GPS position increments by modeling the relationship between context features, velocity, attitude, and position increments. From simulation results, it is confirmed that the positioning accuracy can be significantly improved based on the proposed CAINS when compared with conventional navigation systems. Full article

Motor railway vehicles necessitate enhanced control of wheel-rail contact mechanics to ensure optimal adhesion. During train running, driving wheelsets exhibit torsional vibrations that compromise adhesion and potentially lead to axle damage. Consequently, the development of dynamic models for analyzing driving wheelset stick-slip phenomena and control strategies is an area of significant research interest for traction control, studies on rail corrugation, and locomotive drivetrain design. Despite their application in various railway vehicle problems, non-smooth models have not been explored as an alternative for analyzing stick-slip, and existing research has focused on extensive computations based on Kalker’s theory or simplified models using constitutive friction laws. This work demonstrates the efficacy of non-smooth models in studying motor wheelset stick-slip. The non-smooth approach is suited for control systems, prioritizes simplicity while capturing the essential friction characteristics, and enables efficient dynamic simulations. The proposed model incorporates a set-valued friction law, and the equations of motion are formulated as a switch model. Numerical integration is achieved through an event-driven algorithm. The paper showcases application examples for the model. A direct comparison with an equivalent model using a constitutive friction law shows that the non-smooth integration is an order of magnitude more efficient in the stick phase. Full article

The motion of automobiles significantly depends on the conditions of interaction between a tyre and a road surface. One of the most frequently used ways of presenting the conditions of cooperation between a tyre and a road surface is a characteristic showing a longitudinal adhesion coefficient as a function of a longitudinal slip of a tyre. One of the methods for determining tyre-to-road adhesion characteristics is to use a special trailer combined with a towing vehicle. This type of method is commonly used to determine adhesion characteristics for a braked wheel. This article presents a method for determining adhesion characteristics for a driven wheel based on the road tests of automobiles. For this purpose, vehicle wheel velocity signals from a vehicle CAN network and a vehicle velocity signal from a GPS receiver were used. The signals from the CAN network were recorded using a special measurement card and an application developed in LabVIEW environment. The application developed in LabVIEW also allowed for simultaneous recording of automobile velocity from the GPS receiver. In this paper, the courses of a wheel velocity, longitudinal acceleration of automobile, longitudinal slip of the front wheels in time domain, as well as the coefficient of tyre-to-road longitudinal adhesion as a function of the longitudinal slip of the wheel are presented. Full article

The influence of the external environment can reduce the braking performance of the electric vehicle (EV) with in-wheel motors (IWM). In this paper, an adaptive sliding mode wheel slip control method with a vehicle speed observer consideration is proposed, which enables the EV to accurately track the optimal slip ratio in various environments and improve braking performance. First, the braking system dynamics model is established by taking the EV with IWM as the study object. Second, a super-twisting sliding mode observer is used to estimate the vehicle speed, and a new adaptive second-order sliding mode controller is constructed to control the braking torque. Finally, co-simulation experiments are performed under different conditions based on Carsim and MATLAB/Simulink, and the proposed scheme is validated by comparison with three control methods. The experimental results show that the proposed scheme has better control performance, and both the safety and control quality of the EV is improved. Full article