An iterative learning control algorithm with convergence analysis for batch processes

Abstract

It is difficult to analyze the convergence of iterative learning optimal control for quality control of batch processes, and there exist the disturbance and uncertainties in practical processes. In this paper, a neural fuzzy (NF) model-based approach was used to predict the quality of product and an adaptive update algorithm in the direction of batch was also presented after analyzing the problem of parameters dynamic updating. On this basis, an iterative learning control algorithm with convergence analysis for batch processes was proposed. Moreover, the convergence of the proposed algorithm was analyzed and the rigorous proof was given. Lastly, to verify the efficiency of the proposed algorithm, the algorithm was applied to a classical batch process, and the simulation results showed the efficiency and practicability of the proposed method. Thus it provides a new way for the control of batch processes. © All Rights Reserved.