Open-closed-loop PD-Type Iterative Learning Control And Its Convergence

It is well known that nonlinear time-varying systems are very difficult to control; their controllers are very difficult to design even with modern control theory. But for systems that track same trajectory repetitively, e.g., robotic manipulators, disk drive systems, etc., iterative learning control (ILC) is a simple and effective approach to the control of such systems. Iterative learning control is a technique for improving the transient response performance of systems or processes that operate repetitively over a fixed time interval. It refines the next control input using the information such as current control input and error signals after each trial until the specified desired trajectory is followed to a high precision. Many researchers have paid their attention to iterative learning control because of its simplicity and effectiveness in recent years, the content of research include learning algorithm, convergence, robustness, initial state and learning rate etc..In this dissertation, the convergence of ILC schemes is analyzed mainly. First, Basic knowledge about iterative learning control are introduced, including its history, mathematic description, and some common ILC schemes etc. And the convergence conditions for open-loop, closed-loop and open-closed-loop Iterative learning control schemes are discussed separately. Especially, the author uses an open-closed-loop PD-type ILC scheme for the control of a class of nonlinear systems; sufficient conditions for guaranteeing the convergence of the ILC system are given and proved with linear operator theory. Some opening problems and future directions in the research of ILC are discussed at the end.