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纯反非线性系统的自适应神经识别和非单一控制
Ang Zheng1, Yingbo Huang1, Jing Na1
1Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, 650500, China; Yunnan Key Laboratory of Intelligent Control and Application, Kunming University of Science and Technology, Kunming 650500, China.
ISA transactions
|November 17, 2023
概括
本研究介绍了一种用于非线性纯反系统的新型自适应控制方法,克服了复杂性和奇点问题. 新方法确保了准确的系统识别和强大的控制,通过模拟和实验验证.
科学领域:
- 控制工程 控制工程 控制工程
- 非线性系统理论 非线性系统理论
- 适应性控制控制是适应性的
背景情况:
- 对于非线性纯反系统的传统自适应后退控制器,存在"复杂性爆炸"和潜在控制奇点.
- 现有的方法往往难以准确的状态重建和在线估计未知的系统动态.
研究的目的:
- 为非线性纯反系统开发一种新的结构性识别和自适应性控制方法.
- 解决和纠正"复杂性爆炸",并控制先前方法固有的奇点问题.
- 为了实现追踪和估计错误的同时趋同.
主要方法:
- 通过坐标转换,将纯反系统重新构成一个相当的正规模型.
- 高级滑动模式 (HOSM) 观察器用于未知状态重建的应用.
- 利用辐射基函数神经网络 (RBFNN) 进行一次性动态的在线估计,避免控制增益估计的奇点.
主要成果:
- 提出的方法成功地避免了"复杂性爆炸"和控制奇点.
- 通过RBFNNs和一种新的学习算法,可以对未知的系统动态进行准确的在线估计.
- 追踪错误和估计错误的同时趋同在理论上被证明并经过实验验证.
结论:
- 开发的构造性识别和自适应性控制方法对于非线性纯反系统是有效的.
- 与传统方法相比,这种方法提供了更好的稳定性和准确性.
- 在液压伺服器测试装置上的实验结果证实了拟议技术的实际实用性和有效性.
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