Robustness of reinforced gradient-type iterative learning control for batch processes with Gaussian noise  

作　　者：Xuan Yang Xiao'e Ruan 

机构地区：[1]Department of Applied Mathematics,School of Mathematics and Statistics,Xi'an Jiaotong University

出　　处：《Chinese Journal of Chemical Engineering》2016年第5期623-629,共7页中国化学工程学报（英文版）

基　　金：Supported by National Natural Science Foundation of China(F010114-6097414061273135)

摘　　要：In this paper,a reinforced gradient-type iterative learning control pro file is proposed by making use of system matrices and a proper learning step to improve the tracking performance of batch processes disturbed by external Gaussian white noise.The robustness is analyzed and the range of the step is speci fied by means of statistical technique and matrix theory.Compared with the conventional one,the proposed algorithm is more ef ficient to resist external noise.Numerical simulations of an injection molding process illustrate that the proposed scheme is feasible and effective.In this paper, a reinforced gradient-type iterative learning control profile is proposed by making use of system matrices and a proper learning step to improve the tracking performance of batch processes disturbed by exter- nal Gaussian white noise. The robustness is analyzed and the range of the step is specified by means of statistical technique and matrix theory. Compared with the conventional one, the proposed algorithm is more efficient to resist external noise. Numerical simulations of an injection molding process illustrate that the proposed scheme is feasible and effective.

关 键 词：Batch process lterative learning control Reinforced gradient Gaussian white noise 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]