Adaptive Iterative Learning Controller with Input Learning Technique for a Class of Uncertain MIMO Nonlinear Systems

Abstract

In this paper, an adaptive iterative learning controller (AILC) with input learning technique is presentedfor uncertain multi-input multi-output (MIMO) nonlinear systems in the normal form. The proposed AILC learnsthe internal parameter of the state equation as well as the input gain parameter, and also estimates the desired inputusing an input learning rule to track the whole history of command trajectory. The features of the proposed controlscheme can be briefly summarized as follows: 1) To the best of authors’ knowledge, the AILC with input learningis first developed for uncertain MIMO nonlinear systems in the normal form; 2) The convergence of learninginput error is ensured; 3) The input learning rule is simple; therefore, it can be easily implemented in industrialapplications. With the proposed AILC scheme, the tracking error and desired input error converge to zero as therepetition of the learning operation increases. Single-link and two-link manipulators are presented as simulationexamples to confirm the feasibility and performance of the proposed AILC.