Data-driven Adaptive Iterative Learning Control Based on a Local Dynamic Linearization

Abstract

Linearization technique is inevitable for a nonlinear control system design. However, the traditional linearization methods require model information, which is difficult to obtain for the complex nonlinear system. In this article, a new local dynamic linearization method is proposed via a mean-value theorem and can be estimated by using the I/O data only. Then a new adaptive iterative learning control is proposed by using the optimal technology. The simulation verifies the monotonic convergence and practicability of this method.