Design of Iterative Learning Control Method with Global Convergence Property for Nonlinear Systems

Abstract EN

We address an iterative learning control (ILC) method for overcoming initial value problem caused by local convergence methods.

Introducing a feedback recursive form of tracking errors into iterative learning law, this algorithm can avoid a crude linear approximation to nonlinear plants to reach global convergence property.

The algorithm’s structure is entirely illustrated.

Under assumptions, it is guaranteed that tracking errors of the closed-loop system converge to zero.

Besides, we discuss the roles of parameters in iterative learning law for algorithm realization, and a nonlinear case study is presented to demonstrate the effectivenessand tracking performance of the proposed algorithm.