An adaptive switching learning control method for trajectory tracking of robot manipulators

Abstract

In this paper, a new adaptive switching learning control approach, called adaptive switching learning PD control (ASL-PD), is proposed for trajectory tracking of robot manipulators in an iterative operation mode. The ASL-PD control method is a combination of the feedback PD control law with a gain switching technique and the feedforward learning control law with the input torque profile. The torque profile is updated by the previous torque profile (which makes sense for learning). Furthermore, in this new control method, the switching control scheme is integrated into the iterative learning procedure; as such, the trajectory tracking converges very fast. The ASL-PD method achieves the asymptotical convergence based on the Lyapunov’s method. The ASL-PD method possesses both adaptive and learning capabilities with a simple control structure. The simulation study validates this new method. In particular, both position and velocity tracking errors monotonically decrease with the increase of the number of iterations. The convergence rate with the ASL-PD method is faster than that of the adaptive iterative learning control method proposed by others in literature.