Theory Of Nonlinear Iterative Learning Control And Its Application On Robot Systems

This paper aims at the basic theory of nonlinear iterative learning control, and have studied the iterative learning control methods of nonlinear continuous systems, discrete-time systems, distributed parameter systems, singular systems, and the application of the iterative learning control when a general convergence condition doesn't be satisfied. Finally, we have also discussed the application problems of the iterative learning control on robot systems.This paper contains six parts. The first part is the chapter I, Introduction, Which mainly introduced the background of the iterative learning control theory and the research status of this theory in our country and abroad, and points out the main existing problems in current studies, and elucidats the research purpose and contents of this paper.The second part includes chapter II, chapter III. We mainly presents the popular nonlinear system iterative learning control method in the chapter II, and introduces a new concept—(λ,ξ) norm, Based on using fully former control experiences, we studies the input and output as a whole, which makes the algorithm having the global convergence instead of local convergence in the existing results. We study the attractive manifold method of the iterative learning control for a class of degenerate systems in the chapter III. When the learning control systems don't satisfy the convergence condition, we present the learning control method satisfying the condition by constructing a stable and attractive manifold corresponding system. This method makes the system with better robustness.The third part includes chapter IV and chapter V, Which is mainly studies the trajectory tracking problems of a general nonlinear discrete-time system and a class of uncertain discrete-time systems. To the discrete-time systems, we introduce a new discrete-time (λ,ξ) norm, the algorithm given is not depend upon the initial data. For a class of uncertain discrete-time...