To improve the handling and maneuverability of four-wheel steer (4WS) and in-wheel motor driven electric vehicle (EV) in which the mechanical properties of tires are unknown, an optimal coordinated control combining active rear wheel steering (ARS) and direct yaw moment control (DYC) in the form of active drive torque distribution is proposed. By designing a new type of particle filter (PF), the online identification of unknown tire model is realized and its uncertainties and road surface disturbances are both considered. The constraints of vehicle motions tracking ideal values are derived according to the estimated tire lateral forces with Lyapunov stability theory. And then based on the thought of backstepping and optimization, the drive torque distribution method and the rear wheel steer strategy are obtained aiming at minimizing tire load and control inputs, which also take the effective working range of in-wheel motor into account. Finally, the validity and practicability of overall controller are verified through the simulation of Carsim and Matlab/Simulink. The proposed control strategy lays a theoretical foundation for the integration and optimization of 4WS EVs driven by in-wheel motors.

The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity tracking. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the yaw stability is seldom considered during trajectory tracking. In this research, a combination of the longitudinal–lateral control method with the yaw stability in the trajectory tracking for autonomous vehicles is studied. Based on the vehicle dynamics, considering the longitudinal and lateral motion of the vehicle, the velocity tracking and trajectory tracking problems can be attributed to the longitudinal and lateral control. A sliding mode variable structure control method is used in the longitudinal control. The total driving force is obtained from the velocity error in order to carry out velocity tracking. A linear time-varying model predictive control method is used in the lateral control to predict the required front wheel angle for trajectory tracking. Furthermore, a combined control framework is established to control the longitudinal and lateral motions and improve the reliability of the longitudinal and lateral direction control. On this basis, the driving force of a tire is allocated reasonably by using the direct yaw moment control, which ensures good yaw stability of the vehicle when tracking the trajectory. Simulation results indicate that the proposed control strategy is good in tracking the reference velocity and trajectory and improves the performance of the stability of the vehicle.

In most cases, a vehicle works in a complex environment, with working conditions changing frequently. For most model predictive tracking controllers, however, the impacts of some important working conditions, such as speed and road conditions, are not concerned. In this regard, an adaptive model predictive controller is proposed, which improves tracking accuracy and stability compared with general model predictive controllers. First, the proposed controller utilizes the recursive least square algorithm to estimate tire cornering stiffness and road friction coefficient online. Then, the estimated tire cornering stiffness is used to update vehicle dynamics model and the estimated road friction coefficient is used to update the road adhesion constraint. Moreover, the control parameters consist of prediction horizon, control horizon, and sampling time, all of which are set according to vehicle speed. A co-simulation based on MATLAB/Simulink and CarSim is conducted. The simulation results illustrate that the proposed controller has a great adaptive ability to changing working conditions, especially to speed and road conditions.

A personalized motion planning and tracking control framework is proposed to prevent collisions between autonomous vehicles and obstacles ahead. The presented framework consists of a motion planning generator and a cooperative tracking controller. For motion planning, a multi-constrained numerical optimization method is used to generate the obstacle avoidance motion planning for autonomous vehicles according to the individual needs of the passengers. For tracking control, a cooperative longitudinal and lateral motion controller is designed to track the planning results. The longitudinal controller is composed of a model predictive controller, and the lateral controller is composed of a nonlinear steering controller. The planning results show that the adopted motion planning algorithm can achieve personalized motion planning according to the needs of the passengers. Simulink and CarSim co-simulation results show the excellent tracking performance of the proposed cooperative controller. The performance analysis results show that the proposed framework can improve the corresponding driving performance according to the individual needs of the passengers.

For autonomous vehicle path tracking control, the general path tracking controllers usually only consider vehicle dynamics’ constraints, without taking vehicle stability evaluation index into account. In this paper, a linear three-degree-of-freedom vehicle dynamics model is used as a predictive model. A comprehensive control method combining Model Predictive Control and Fuzzy proportional–integral–derivative control is proposed. Model Predictive Control is used to control the vehicle yaw stability and track the target path by considering the front wheel angle, sideslip angle, tire slip angles, and yaw rate during the path tracking. Fuzzy proportional–integral–derivative algorithm is adopted to maintain the vehicle roll stability by controlling the braking force of each tire. Co-simulation with CarSim and MATLAB/Simulink shows the designed controller has good tracking performance. The controller is smooth and effective and ensures handling stability in tracking the target path.

Mechanical elastic wheel (MEW) has the advantage of explosion-proof, which helps to guarantee the safety and maneuverability of the automobile. Aiming to improve the stability of vehicles matching with MEW, this paper proposes an integrated control strategy that combines the active front steering and the active suspension systems. The integrated controller consists of feed-forward and feedback controllers. Firstly, the differential flatness system of the vehicle is constructed concerning the coupling among yaw, lateral, and roll motion. And the flatness based feed-forward control inputs of the desired front wheel angle and active roll moment are derived. At the same time, the backstepping-adaptive feedback controllers for yaw rate and roll angle are designed. Then, the feed-forward and feedback controllers are coordinated and their weight coefficients are adjusted according to the sideslip angle. Furthermore, this paper also adopts a nonlinear disturbance observer to estimate the vertical load of MEW. The influence of the vertical load is fully considered when estimating the equivalent cornering stiffness of each MEW, so as to realize the online adjustment of the controller parameters. Finally, the maneuver tests of step input steering and double lane change are simulated, respectively. The results show that the proposed control strategy yields a better control effect than the fuzzy-PID controller, and the performance of the vehicle matching with MEW has been significantly improved.