Decentralized Iterative Learning Control for Large-Scale Interconnected Non-Affine Nonlinear Discrete-Time Systems

摘要

This thesis discusses the decentralized iterative learning control for large-scale discrete-time single-input single-output (SISO) systems, which is interconnected by non-affine nonlinear systems. In view of the structure of the system, the P-type learning algorithm is constructed. Under certain assumptions, the algorithm can make sure that the error precision required in each subsystem is attained through repeated iteration. The given example indicates that the proposed scheme is effective.