Adaptive Sliding Mode Predictive Control for Path Tracking of Wheeled Agricultural Vehicles

This study presents an adaptive sliding mode predictive control (ASMPC) algorithm intended to improve the control precision and robustness of path tracking for wheeled agricultural vehicles. Firstly, the kinematics state equations of the vehicle were established based on path tracking errors. Secondly, in order to design the path tracking controller by combining the precision advantage of model predictive control (MPC) algorithm with the robustness advantage of sliding mode control (SMC) algorithm, the sliding mode functions were designed and used as the output equations to establish the kinematics state space model of the vehicle. Thirdly, on the basis of linearization and discretization for the kinematics state space model, the control law of path tracking was obtained using the MPC algorithm. Finally, according to the fuzzy rules designed by the working speed of the vehicle and the curvature of the reference path, the prediction horizon and control horizon of the MPC algorithm were adaptively adjusted to further improve the control precision and robustness of the path tracking system. The results of CarSim and MATLAB/Simulink co-simulation show that the proposed ASMPC algorithm is superior to the traditional SMC algorithm and conventional MPC algorithm in terms of control precision, dynamic performance, and robustness. The results of our field test show that the root mean square (RMS) values of the lateral errors for straight path tracking and curve path tracking do not exceed 2.1 and 8.7 cm, respectively, in the speed range of 1.0 to 3.5 m/s, suitable for field working. The control precision and robustness of the proposed ASMPC algorithm can meet the working requirements of wheeled agricultural vehicles.