A novel control strategy is proposed for the autonomous vehicle equipped with the front and rear independent steering systems to enhance the capability of collision avoidance and improve path tracking and handling stability under critical maneuvers. The novel control system includes a path replanning controller, a path tracking controller, and a stability controller. The path replanning controller is designed to generate a collision-free trajectory by optimizing a cost function. The cost function can be updated quickly and iteratively, according to the dynamic fitting matrix and measurement signals. The path tracking controller and the stability controller with considering multi-constraints are designed to generate the steering angle of the front wheel and the rear wheel, respectively, which improve the ability of path tracking and handling stability of the autonomous vehicle under extreme conditions. The real-time effectiveness of the proposed control strategy is verified and evaluated in Hardware-in-the-loop (HIL) simulation environment. The function of collision avoidance of the control algorithm is verified in the scenario of the preset obstacle artificially on the globally planned path. The proposed control strategy, presents superior performance against two other control strategies based on model predictive control algorithm, and also exhibits satisfactory performance in terms of path tracking as well as handling stability of the autonomous vehicle.

An optimized handling stability control strategy is put forward aiming at enhancing a four in-wheel independent-drive motors electric vehicle (4WIDEV) performance capable of handling and stability. The optimized control strategy is designed by hierarchical control structure, which mainly includes vehicle motion controller and vehicle torque distribution controller. Lateral stability controller, in vehicle motion controller, which yields the generalized force and generalized yaw moment required of the vehicle, is designed into two modes: an instability control mode and a continuous control mode. Which mode is activated determining by stability judgement controller based on phase portraits of sideslip angle and yaw rate. When the vehicle state exceeds the stability envelope region, instability control mode based on envelope control is utilized by sliding mode control method, simultaneously controlling the two vehicle states variables, whereas continuous control mode is performed with adaptive adjustment of the weight parameter according to pre-defined stability degree. The lateral stability controller with adaptive switching two modes exhibits excellent handling stability performance under normal driving conditions, especially under critical conditions, such as on low tire-road adhesion coefficient. The vehicle torque distribution controller assigns driving or regenerative braking torque to four wheels reasonably and efficiently to satisfy the generalized force and the generalize yaw moment acquired by the vehicle motion controller, which synchronously considers the motor output capability and tire friction ellipse constraints while maintains each wheel slip ratio within stable range. The results in co-simulation experiments based on Carsim and MATLAB/Simulink verify the proposed control strategy, compared with other control strategies, and demonstrate effective improvement 4WIDEV’s performance in terms of handling stability especially under critical conditions.

Path planning is an indispensable component in guiding unmanned ground vehicles (UGVs) from their initial positions to designated destinations, aiming to determine trajectories that are either optimal or near-optimal. While conventional path-planning techniques have been employed for this purpose, planners utilizing reinforcement learning (RL) exhibit superior adaptability within exceedingly complex and dynamic environments. Nevertheless, existing RL-based path planners encounter several shortcomings, notably, redundant map representations, inadequate feature extraction, and limited adaptiveness across diverse environments. In response to these challenges, this paper proposes an innovative and highly self-adaptive path-planning approach based on Transformer encoder feature extraction coupled with incremental reinforcement learning (IRL). Initially, an autoencoder is utilized to compress redundant map representations, providing the planner with sufficient environmental data while minimizing dimensional complexity. Subsequently, the Transformer encoder, renowned for its capacity to analyze global long-range dependencies, is employed to capture intricate correlations among UGV statuses at continuous intervals. Finally, IRL is harnessed to enhance the path planner’s generalization capabilities, particularly when the trained agent is deployed in environments distinct from its training counterparts. Our empirical findings demonstrate that the proposed method outperforms traditional uniform-sampling-based approaches in terms of execution time, path length, and trajectory smoothness. Furthermore, it exhibits a fivefold increase in adaptivity compared to conventional transfer-learning-based fine-tuning methodologies.

Intelligent truck platoons can benefit road transportation due to the short gap and better fuel economy, but they are also subject to dynamic uncertainties and external disturbances. Therefore, this paper develops a novel robust control algorithm for connected truck platoons. By introducing a linearized expression method of platoon error dynamics based on state measurement, the state feedback mechanism combined with a nonsmooth controller for a truck platoon is proposed in the development of the distributed control method. The state-feedback controller can drive the nominal platoon system to the state of second-order consensus, and the nonsmooth controller counterparts the uncertainties and disturbances. The convergence and string stability of the proposed control algorithm are demonstrated both theoretically and experimentally, and the effectiveness and robustness are also verified by simulation tests.

A coordinated control of front/rear traction, four-wheel independent braking, and an active roll control system is proposed in this paper to improve overall vehicle cornering performance. The control algorithm is structured in a hierarchical form: the supervisory controller is used to calculate the desired yaw rate and roll angle based on the driver input and vehicle states; the upper-level controller is utilized to decide the target longitudinal tire forces, yaw moment, and anti-roll moment based on the sliding mode control method; and the control allocator is used to optimally map the virtual control input to the control commands of specific actuators. Co-simulations of MATLAB/Simulink and CarSim were conducted to verify the performance enhancement of the proposed controller. The results suggest that the proposed controller can effectively enhance the driving performance in limit cornering while maintaining lateral stability, and it reduces tire usage, as indicated by the index of tire dissipation energy when compared to control methods such as AWD, AWD-ESC, and ICC.

This study presents an adaptive integrated chassis control strategy for enhancing vehicle stability under different road conditions, specifically through the real-time estimation of tire cornering stiffness. A hierarchical control architecture is developed, combining active front steering (AFS) and direct yaw moment control (DYC). A recursive regularized weighted least squares algorithm is designed to estimate tire cornering stiffness from measurable vehicle states, eliminating the need for additional tire sensors. Leveraging this estimation, an adaptive sliding mode controller (ASMC) is proposed in the upper layer, where a novel self-tuning mechanism adjusts control parameters based on tire saturation levels and cornering stiffness variation trends. The lower-layer controller employs a weighted least squares allocation method to distribute control efforts while respecting physical and friction constraints. Co-simulations using MATLAB 2018a/Simulink and CarSim validate the effectiveness of the proposed framework under both high- and low-friction scenarios. Compared with conventional ASMC and DYC strategies, the proposed controller exhibits improved robustness, reduced sideslip, and enhanced trajectory tracking performance. The results demonstrate the significance of the real-time integration of tire dynamics into chassis control in improving vehicle handling and stability.