Exact sideslip angle estimation is significant to the dynamics control of four-wheel independent drive electric vehicles. It is costly and difficult-to-popularize to equip vehicular sensors for real-time sideslip angle measurement; therefore, the reliable sideslip angle estimation method is investigated in this paper. The electric driving wheel model is proposed and applied to the longitudinal force estimation. Considering that electric driving wheel model is a nonlinear model with unknown input, an unknown input estimation method is proposed to facilitate the longitudinal force observer design, in which the adaptive high-order sliding mode observer is designed to achieve the state estimation, the analytic formula of longitudinal force is obtained by decoupling electric driving wheel model, and the longitudinal force estimator is designed by recurrence estimation method. With the designed virtual longitudinal force sensor, an adaptive attenuated Kalman filtering is proposed to estimate the vehicle running state, in which the time-varying attenuation factor is applied to weaken the past data to the current filter and the covariance of process noise and measurement noise can be adjusted adaptively. Finally, simulations and experiments are conducted and the effectiveness of proposed estimation method is validated.

A hierarchical trajectory tracking and yaw stability combined control strategy of autonomous ground vehicle within-wheel motor is proposed in this paper to achieve simultaneous and accurate trajectory tracking and vehicle yaw stability control. A trajectory-tracking controller is designed on the basis of model predictive control algorithm, and the ideal front-wheel steering angle requirement is calculated to follow the referenced trajectory. In order to improve the accuracy of vehicle steering control, a vehicle steering controller is designed based on high-order sliding mode control method, in which the control demand of front-wheel steering angle is satisfied by real-time torque control of vehicle steering motor. Simultaneously, a double power reaching rate-based sliding mode control method is applied to design the vehicle yaw stability controller, in which the yaw moment control requirement is met by an optimal oriented tire force allocation algorithm. The simulation and experiment results show that the presented control method can improve the accuracy and real-time performance of trajectory tracking control while ensuring vehicle yaw stability.

Accurate and real-time position of preview point is significant to trajectory tracking control of vision-guided intelligent vehicle. The unavoidable delay of road automatic identification system weakens trajectory tracking control performance, and even deteriorates the vehicle stability. Therefore, a compensator for the delay of road automatic identification system was proposed which combines the current statistical model and adaptive Kalman predictor to estimate the state of preview point position. The trajectory tracking sliding mode controller of intelligent vehicle is established through a 2–degrees of freedom vehicle dynamic model and motion model by using MATLAB/Simulink and CarSim. The trajectory tracking performance under 20–100 ms delay is analyzed. The simulation results show that the trajectory tracking performance of intelligent vehicle will be affected by the delay of road automatic identification system, reducing tracking accuracy. And when the delay is too large, it will deteriorate the vehicle stability and safety. In addition, the simulation results also verify the effectiveness of current statistical–adaptive Kalman predictor compensator at different delays.