Iteration Learning Control for Uncertain Nonlinear Systems with Time-Varying Output Constraint

被引：0

作者：

Zhao, Yangyang ^{[1
]}

Zhang, Heng ^{[1
]}

Liu, Xiaopei ^{[1
]}

Wang, Yang ^{[1
]}

机构：

[1] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China

来源：

IFAC PAPERSONLINE | 2023年 / 56卷 / 02期

关键词：

Iterative and repetitive learning control; Control of constrained systems; Uncertain systems; Disturbance rejection; High fidelity simulation; STATE;

D O I：

10.1016/j.ifacol.2023.10.592

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A novel Adaptive Iteration Learning Control (AILC) method is proposed to solve the trajectory tracking problem for a class of nonlinear uncertain systems with external disturbances. Furthermore, the output of system is required to be bounded by a time-varying function. To this end, a Barrier Lyapunov Function (BLF) term is integrated into the AILC scheme such that the impact of the uncertainties and disturbances are significantly reduced without violating the output constraints. A Barrier Composite Energy Function (BCEF) is utilized to analyze the convergence of state error and the boundedness of output. The validity of the proposed AILC scheme is verified by a numerical example. In addition, a high-fidelity simulation platform that can generate a real-life turbulent flow is utilized to demonstrate the robustness of the algorithm. Copyright (c) 2023 The Authors.

引用

页码：11867 / 11873

页数：7

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