The New Algorithm Of Adaptive Iterative Leaning Control And Its Application To Industrial Process Control

Iterative learning control is an important branch of intelligent control. The basic method of traditional ILC is to achieve control input based on the previous input and the PID-revised error of previous output. After some iteration, perfect tracking can be achieved over a fixed time interval. However, when the plant has uncertain parameters or the variant gain coefficient of iterative leaning control, the present method has some defects, such as Lipschitz continuity of nonlinear function and the dependence of convergence analysis on actually unknown ideal input. As adaptive control achieves success in nonlinear uncertain systems, how to make full use of the prior information and design ILC by adaptive control, it's a new subject worthwhile to research. This paper considers the ILC from an adaptive control viewpoint. Two kinds of new algorithm are proposed for ILC of essential nonlinear systems, which avoid some drawbacks and restricted assumptions of traditional ILC, based on Lyapunov stability theory and Backstepping technique of nonlinear system. The application of proposed AILC to the steady state optimization of nonlinear industrial process, in which the set-point and the target trajectory vibrate frequently, is investigated to improve the transient dynamic performance. The main results are as follows. Firstly, for a class of unknown nonlinear high-order system, the neural network is introduced into ILC. The ILC is incorporated with the adaptive control to achieve arbitrary tracking accuracy. The effectiveness and advantage of proposed algorithm are illustrated through simulation examples. Secondly, an adaptive robust iterative learning control scheme is developed for a class of uncertain nonlinear systems. The iterative learning control, the variable structure sliding mode control and neural network control are combined in complementary manner. The asymptotic convergence of the tracking error to zero is established. Thirdly, the adaptive iterative learning control is used for the dynamics in the steady state optimization of nonlinear industrial process. The process operates continuously without resetting of initial value. The difficulty of selecting target trajectory is overcome effectively. The accurate tracking of a series of desired trajectory is achieved by extension to a class of nonlinear system. Lastly, the simulation researches are done to every method, which illustrate the effectiveness and feasibility of the proposed algorithm.