Robust Iterative Learning Control Of Uncertain Linear System

Iterative learning control (ILC) is an novel control algorithm, ich doesn't Depend on the precise mode. It can generate input signal and reduce error through repeating learning so that the output of the system can approximate to the expectation. In this thesis, design a few iterative learning controllers for the uncertain linear systems and present the corresponding theory analysis and simulation study. This paper is organized as follows:Introduce the research significance of iterative learning control, gether with the current development situation of this field all over the word.Study the convergence and robust problem of the continue uncertain linear systems. Design the iterative learning controller and iterative learning controller aspect by the form of Riccati equation. the lyapunov methodology is developed to solve the proposed problem. The controller can keep the systems convergent and Robust stable. In these algorithms, e convergence speed can be adjusted easily just by the parameters in the performance function. Lastly The simulation result shows effectiveness of the proposed method.Study the convergence and robust problem of the discrete uncertain linear systems by the 2 direct linear system theory. Firstly, esign the iterative learning algorithms of the uncertain linear discrete system and the uncertain linear discrete delay system by the 2-D Roessor model. Secondly, sign the guaranteed cost iterative learning algorithms based on LMI method. lastly design the iterative learning algorithms by the 2-D FM model.Study the stochastic noise property problem of the super-vector iterative learning control. A Kalman filter is utilized for finding the baseline error analytically, timate the error of the output measurement. An algebraic Riccati equation is solved analytically to find the steady-state covariance matrix and to prove that the system eventually converges to the baseline error. Lastly the monotone convergence condition is given.