HIGHER-ORDER ITERATIVE LEARNING CONTROL FOR NONLINEAR CONTINUOUS SYSTEMS WITH VARIABLE INPUT TRAIL LENGTHS AND INPUT SATURATION

. A higher-order iterative learning control (ILC) is developed for nonlinear continuous-time systems with input saturation, the process of which consists of input-driven part and the free part. In the time interval of the inputdriven part, the controlled system operates according to the imposed control input. Then, it executes autonomously in the rest of the time interval, which is regarded as the free part. Thus, the developed higher-order ILC law pursues the desired trajectory tracking within the time interval affected by control input. Based on the assumption that the initial state is variable around the desired one within a bound, the ILC tracking errors can be driven into a range whose bound is proportional to the initial state vibration. As a special case, when the initial state equals the desired initial state, the ILC tracking errors are controlled to zero as the iteration number tends to infinity. Robustness and convergence analysis of the proposed higher-order ILC law are provided against the initial states vibration. Simulation results are given to illustrate the effectiveness of the presented higher-order ILC scheme.