具有输入非线性非线性严格反系统的跟踪控制问题:一种自适应神经网络动态表面控制方法
Minglong Zhou1, Xiyu Zhang2, Xiongfeng Deng3
1School of Electrical Engineering, Anhui Technical College of Mechanical and Electrical Engineering, Wuhu, China.
PloS one
|October 24, 2024
概括
本研究介绍了一种新的自适应动态表面跟踪控制器,使用神经网络来管理具有输入非线性非线性系统. 控制器确保准确的轨迹跟踪和有限的系统信号,克服复杂性问题.
科学领域:
- 控制系统工程 控制系统工程
- 非线性动力学是一种非线性动力学.
- 人工智能的人工智能
背景情况:
- 由于固有的复杂性,非线性严格反系统在控制设计中存在重大挑战.
- 输入非线性和未知的系统动态进一步复杂化实现精确的跟踪控制.
研究的目的:
- 为输入非线性非线性严格反系统开发适应性动态表面跟踪控制器.
- 为了解决动态表面控制设计中的复杂性爆炸问题.
- 为了确保系统输出跟踪所需的轨迹,带有边界信号.
主要方法:
- 设计了一个辅助控制系统,以补偿输入非线性.
- 辐射基函数神经网络 (RBFNNs) 用于近似未知的非线性动态.
- 制定了自适应更新控制规则,以估计未知的参数.
- 一级低通波器被整合到动态表面控制 (DSC) 设计中,以减轻复杂性.
主要成果:
- 拟议的基于NN的自适应动态表面跟踪控制器成功实现了轨迹跟踪.
- 追踪错误被证明汇聚到一个小的零邻里.
- 闭环系统内的所有信号都被证明是有界的.
- 通过两个模拟示例验证了控制器的有效性.
结论:
- 开发的控制器有效地处理具有输入非线性和未知动态的非线性系统.
- 将RBFNNs和DSC与过器集成,为跟踪控制提供了一个强大的解决方案.
- 控制器保证了稳定的系统性能和准确的轨迹.
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