This paper presents a novel adaptive multiple-model predictive control (MPC) scheme for Four-Wheel Independent Drive (4WID) autonomous vehicles with a holistic structure. Firstly, the combined vehicle-path model is established. To ensure the real-time performance of MPC, the coupling relationships in the control output and the longitudinal/lateral motions are well decoupled, in this way a linear integrated model is utilized as the internal model of the controller. Then, the holistic MPC is proposed to acquire the steering angle and the force on each corner. Based on the advantages of the proposed structure, a weight adaptive mechanism is introduced to improve the handling ability of the controller to various driving conditions, especially some extreme conditions. For the uncertainties in tire cornering stiffness, a multiple-model adaptive law is designed with its convergence proved by Lyapunov theory. Numerical results based on Carsim-Simulink co-simulation platform demonstrate the effectiveness and superiority of the proposed control method in both normal and extreme driving conditions.

Trajectory tracking and stability control are two essential functions of autonomous vehicles, and there is inevitable mutual interference between them, especially under extreme conditions. Towards addressing this challenge, this paper proposes a novel cooperative strategy of trajectory tracking and stability control for four-wheel independent drive (4WID) autonomous vehicles. An adaptive trajectory tracking controller is designed based on the model predictive control (MPC) theory, in which the tire cornering stiffness is modified in real-time leverages the lateral force estimated by the square-root cubature Kalman filter (SCKF). Then, the vehicle stability controller is designed based on the sliding mode control (SMC), in which the tire force saturation constraint and the relative weight of yaw rate and sideslip angle are considered. Furthermore, a weight adaptive criterion with normalized stability index is defined to develop the cooperative strategy of trajectory tracking and stability control. The feasibility and adaptability of the proposed method to different extreme conditions are verified by hardware-in-the-loop (HIL) test and CarSim-Simulink co-simulation. Finally, an experimental case is presented to demonstrate the realizability of the proposed method.

This study proposes an effective trajectory planning algorithm based on the quartic Bézier curve and dangerous potential field for automatic vehicles. To generate collision‐free trajectories, potential field functions are introduced to evaluate the collision risk of path candidates. However, many studies on artificial potential field approaches primarily focus on static and straight roads, and attach less importance to more complex driving scenarios, such as curving roads. In this study, a novel method based on the Frenet coordinate system is proposed to address such limitations. Moreover, to balance the driving comfortability and the driving safety of the path candidate, the path‐planning problem is converted to an optimisation problem, and sequential quadratic programming algorithm is employed to tackle this task. Another merit of this algorithm is the curvature of the generated path is continuous even at the joints of adjacent sub‐trajectories by utilising several specific properties of the Bézier curve. Furthermore, to execute the generated trajectory, a framework of velocity generation is proposed while vehicle dynamic constraints are considered. Some typical traffic scenarios, including lane‐changing, lane‐keeping, and collision avoidance have been designed to verify the performance of the proposed algorithm, and simulations demonstrate the validity of this method.

This article presents a hierarchy control strategy for magneto-rheological suspension system integrated with active braking and active front steering subsystems. This is proposed for the improvement of ride comfort and vehicle stability under different kinds of driving conditions. A nonlinear complicated full vehicle model which includes the longitudinal, lateral and vertical motions is established and combined with a modified coupling ‘Magic Formula’ tyre model. Subsequently, the global state identification and task assignment logic are formulated by adopting several driving conditions such as the straight driving and cornering state. A fuzzy control strategy is then used for the suspension system, while a sliding mode control technique is utilised for both braking and steering systems. In order to demonstrate the effectiveness of the proposed control methodology, control performances such as roll angle, yaw rate and vehicle trajectory are evaluated and presented.

this paper presents a control methodology for solving the vibration issues emerged in IWM-EVs (in-wheel-motor electric vehicles). Unlike existing techniques and methods, the proposed investigation focus on the electromechanical coupling effects between subsystems in IWM-EV which was considered as another negative effects bought by power integration. To this aim, an integrated model which describing the dynamic coupling process between electromagnetic excitation in motor and transient dynamics in vehicle is established and developed. The characteristics of the electromechanically motivated harassment are discussed and its coupling mechanism are analyzed. The key factors are extracted and adopted as the feedback signals in the design of control methods. The effectiveness verification is conducted within numerous practical scenario of vehicle dynamics. Theoretical analysis and simulation results reveal that the proposed approaches can prevent further enlargement of air-gap deformation and unbalanced electromagnetic excitation by cutting off the electromagnetic force outputting periodically, which are benefit to attenuate the negative issues arisen by electromechanical coupling in IWM-EV. In addition, a more balanced outcome in vehicle dynamics is achieved by the independent-phase chopping method with a less side effects on output torque and speed tracking ability.

In this study, an integrated path tracking control framework is proposed for the independent‐driven autonomous electric vehicles. The proposed control scheme includes three parts: the non‐linear model predictive path tracking controller, the lateral stability controller, and the optimal torque vectoring controller. Firstly, the upper bound speed limit is regulated based on the known curvature and adhesion coefficient of the road to prevent the tyre saturation. The model predictive controller generates the steering angle and the desired longitudinal force for path tracking. Simultaneously, the lateral stability controller calculates the desired yaw moment to balance the vehicle stability and motility under different situations. Finally, the optimal torque vectoring controller distributes the wheel torques to generate the desired longitudinal force and yaw moment. Three test cases are designed and verified based on a Carsim/Simulink platform to evaluate the control performance. The test results illustrate that the proposed control framework has satisfactory path tracking performance, and the desired balance between vehicle mobility and stability is achieved under different road conditions.