Iterative reference adjustment for high precision and repetitive motion control applications

Abstract

In this paper, a new iterative learning control (ILC) scheme is proposed which is suitable for high precision and repetitive motion control applications. Unlike the usual ILC scheme which adapts a feedforward control signal to achieve improved tracking performance over time, the proposed scheme iteratively adjusts the reference signal. To achieve a higher convergence rate, a Radial Basis Function (RBF) neural network is employed to model the tracking error over a cycle, and subsequently used implicitly in the iterative adaptation of the reference signal over the next cycle. Simulation examples are furnished to elaborate the various highlights of the proposed method.