Iterative learning control algorithm for optimal path industrial manipulator

Abstract

In this paper, a two-degree-of-freedom manipulator is taken as the research object, and the relevant dynamic model is established, the iterative learning controller is designed, and the trajectory tracking control of the manipulator is carried out by using the iterative learning control algorithm. Iterative learning control (ILC) has a better control effect on a two-degree-of-freedom manipulator with repetitive motion characteristics for its non-linear system. In the case of disturbance, a PD-type iterative learning control law is designed. With the increasing number of iterations of the system, the required correction interval is shortened by modifying the gain matrix in real time in the interval, so as to accelerate the convergence speed. The simulation results show that the convergence speed of PD-type ILC is faster than that of P-type ILC, and the convergence effect of PD-type ILC with disturbance is better than that of traditional disturbance-type ILC. The industrial robot system is guaranteed to have good dynamic performance.