Adaptive Iterative Learning Control For Nonlinear Discrete-Time Systems And Its Applications

This dissertation mainly focuses on the exploration of some new adaptive iterative learning control (AILC) schemes for different nonlinear discrete-time systems. The prominent contribute lies in that the system initial conditions and desired reference trajectories can be randomly varying along the iteration axis. The main work and key innovations are summarized as the following five points.1. Based a new iteration-dependent non-parametric dynamic linearization method, a novel non-parametric adaptive iterative learning control (NP-AILC) scheme and its higher-order form (Higher-order non-parametric adaptive iterative learning control, HONP-AILC) are developed for a class of general nonlinear discrete-time SISO and MISO systems. The uniform convergence over the entire finite time interval can be guaranteed by theoretical analysis when the initial conditions are randomly varying along the iteration axis. Essentially, this scheme is model-free, and the controller design and analysis only depends on the I/O data of the dynamic systems.2. Considering a class of nonlinear discrete-time systems with known structures and unknown time-varying parametric uncertainties, we present two novel parametric adaptive iterative learning control schemes (P-AILC) by cooperating recursive Least Squares algorithm and Projection algorithm, respectively. By rigorous analysis, we show that the new P-AILC can deal with random initial conditions and iteration-varying reference trajectories, in the sequel achieving an almost perfect tracking performance over a finite interval (uniform convergence over the entire interval except for the very first initial point).3. When considering a class of nonlinear affine discrete-time systems, we present a novel neural network adaptive iterative learning control (NN-AILC). The theoretical analysis shows that the presented method can achieve a bounded convergence with a arbitrarily pointed precision along the iteation axis without requiring the identical conditions of initial state and reference trajectory.4. A higher-order non-parametric adaptive control (HONP-AC) is presented for a...