Research On Several Problems About Iterative Learning Control

Iterative learning control(ILC for short) theory is an important branch of intelligence control field for improving the transient response performance of systems that operate repetitively over a fixed time interval.Its fundamental principle is to acquire the current iterative input based on the former input signals and error signals.ILC can achieve the tracking of a desired output trajectory to a high precision.This control method suits extremely in the objects that always run repetitively, such as manipulator,numerical control engine bed,magnetic head driver,electrical machinery servo system and so on.The main contents and results of this thesis are summarized as follows:1.The convergence conditions of the basic ILC algorithm are given.The improved D-type, PD-type iterative learning control algorithm for time-invariant systems are proposed.The robustness and convergence of open-closed loop PID-type for nonlinear systems and open-closed loop PID-type for nonlinear discrete systems interative learning control algorithms are proved respectively.Similarly,the conditions are verfied with simulation results.2.Studied the convergence of ILC based on the 2-D theory.The open-closed-loop D-type iterative learning control based on the linear continuous-discrete system theory is debated.Simulation shows that the algorithm is effective.3.The iterative learning control algorithm for time delay systems are discussed.The PD-type iterative learning control for linear systems with control delay are investigated.Simulations illustrate the validation of the algorithm.4.The iterative learning control method is validated in the models of networked control systems.The verification of iterative learning control in the network time-delay and data packed dropout for the networked control systems.The effectiveness of the proposed method is illustrated by simulation experiments.