引用本文: | 施卉辉,陈强.一类不确定系统的自适应滑模迭代学习控制[J].控制理论与应用,2023,40(7):1162~1171.[点击复制] |
SHI Hui-hui,CHEN Qiang.Adaptive sliding-mode iterative learning control for a class of uncertain systems[J].Control Theory and Technology,2023,40(7):1162~1171.[点击复制] |
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一类不确定系统的自适应滑模迭代学习控制 |
Adaptive sliding-mode iterative learning control for a class of uncertain systems |
摘要点击 1869 全文点击 674 投稿时间:2022-05-30 修订日期:2022-10-21 |
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DOI编号 10.7641/CTA.2022.20474 |
2023,40(7):1162-1171 |
中文关键词 迭代学习控制 非参数不确定性 自适应控制 类Lyapunov方法 |
英文关键词 iterative learning control nonparametric uncertainty adaptive control Lyapunov-like approach |
基金项目 国家自然科学基金项目(61973274, 62222315), 浙江省自然科学基金重点项目(LZ22F030007)资助. |
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中文摘要 |
本文针对一类在有限时间内执行重复任务的不确定非线性系统状态跟踪问题,提出一种自适应滑模迭代
学习控制方法, 在存在初始偏移的情况下也能实现对参考轨迹的完全收敛. 本文通过设计全饱和自适应迭代学习
更新律, 估计参数和非参数不确定性以及未知期望控制输入, 并将估计值限制在指定界内, 避免估计值的正向累加.
文章设计的自适应滑模迭代学习控制方法对系统模型的信息需求少, 在对系统非参数不确定性的上界估计时不需
要Lipschitz界函数已知. 本文给出严格的理论分析, 证明闭环系统所有信号的一致有界性以及跟踪误差的一致收敛
性, 并通过仿真验证所提控制方法的有效性。 |
英文摘要 |
In this paper, an adaptive sliding-mode iterative learning control method is proposed for the state tracking
problem of a class of uncertain nonlinear systems that perform repetitive tasks in finite time, and the complete convergence
to the reference trajectory can be achieved in the presence of arbitrary initial shifts. The fully saturated adaptive iterative
learning laws are designed to estimate the parametric and nonparametric uncertainties and the desired control input, and
the estimated values are constrained within the specified bounds to avoid the positive accumulation of the estimated values.
The designed control method requires less system model information, and does not need the Lipschitz bound function to
be known when estimating the upper bound of the system nonparametric uncertainties. Rigorous theoretical analysis is
provided to ensure the uniform boundedness of all signals and the uniform convergence of tracking error in the closed-loop
system. The simulation results verify the effectiveness of the proposed control method. |