Sliding mode gain-adaptive iterative learning control for the pushing ore truck manipulator

Aiming at the problem of position and velocity tracking error of the pushing ore truck manipulator in the process of repetitive motion, an improved sliding mode-gain rectification-adaptive iterative learning control method is proposed. The stability of the control system is analytically guaranteed by formulating a Lyapunov function, which ensures boundedness of all signals and convergence of the trajectory tracking error. Simulation results show that without external interference, the proposed ISMC-AGILC controller reduces the average position errors of the three joints by 79%, 82%, and 81%, respectively, the velocity errors by 59%, 64%, and 75%, respectively, and the performance metrics by 79%, 49%, and 84%, respectively, with only one iteration. Its control effect further improves as the number of iterations increases. When facing external interference, the ISMC-AGILC algorithm exhibits higher initial error suppression rates and excellent convergence rates compared with commonly used existing algorithms, demonstrate strong response rate and robustness.