Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence  

作　　者：Zhiguo Wang Fangqing Gao Fei Liu 

机构地区：[1]Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education),Institute of Automation,Jiangnan University,Wuxi,214122,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第1期343-355,共13页工程与科学中的计算机建模（英文）

摘　　要：In this paper,an improved high-order model-free adaptive iterative control(IHOMFAILC)method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method.This method adds the differential of tracking error in the criteria function to compensate for the effect of the random disturbance.Meanwhile,a high-order estimation algorithmis used to estimate the value of pseudo partial derivative(PPD),that is,the current value of PPD is updated by that of previous iterations.Thus the rapid convergence of the maximumtracking error is not limited by the initial value of PPD.The convergence of the maximumtracking error is deduced in detail.This method can track the desired output with enhanced convergence and improved tracking performance.Two examples are used to verify the convergence and effectiveness of the proposed method.

关 键 词：Pseudo partial derivative enhanced convergence tracking error disturbance compensation 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]