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
收藏
页码：11867 / 11873
页数：7
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