Abstract:

To enhance the estimation accuracy of key parameters and reduce the effects of model uncertainty on the robustness of control system in the autonomous path tracking process of distributed?drive intelligent electric vehicle， an observer?based adaptive sliding mode path tracking control strategy is proposed in this paper. Firstly， in view of the difficulty in directly and accurately measuring the longitudinal and lateral speeds， a state estimation system with 5 inputs， 3 outputs and 3 states is established， and the minimal model error criterion is adopted to reduce the error of observation model caused by the nonlinear feature of tire. Then based on kinematic model， the desired yaw rate response of path tracking is calculated， the sliding mode algorithm is employed to achieve active steering control， and with consideration of the potential failure risk of steer?by?wire system， the RBF neural network is introduced to perform an online estimation on system uncertainty. Meanwhile， the direct yaw controller is designed with optimal torque distribution strategy used to further improve the stability of vehicle. Finally， a Carsim/Matlab co?simulation is conducted on vehicle state estimation and path tracking. The results demonstrate that the observer based on minimal model error criterion can get more reliable estimation results and the path tracking controller can ensure the vehicle have higher tracking accuracy and robustness.

Key words: path tracking, minimal model error, adaptive sliding mode control, RBF neural network