事件触发的自适应代学习规定的高阶严格反非线性系统的性能控制
Leyan Fang1, Mingzhe Hou1, Xindi Xu1
1Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China.
ISA transactions
|November 27, 2025
概括
本研究介绍了非线性系统的事件触发的自适应代学习规定的性能控制 (PPC),在通信约束下改善跟踪性能. 这种新的方法确保了边界误差和增强的参数估计,以实现可靠的控制.
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
- 机器人技术 机器人技术 机器人技术
背景情况:
- 现有的自适应代学习控制方法经常使用一级系统,忽视网络通信限制.
- 高级严格反非线性系统由于不确定性和复杂的动态存在挑战.
研究的目的:
- 为不确定的高阶严格反非线性系统开发事件触发的自适应代学习规定的性能控制 (PPC).
- 通过结合网络通信限制和提高控制性能来解决现有方法的局限性.
主要方法:
- 设计了一个由事件触发的自适应代学习PPC算法,使用非线性映射和动态表面控制.
- 采用了改进的参数估计错误重建机制和投影操作员.
- 在性能信封中构建了一个预设的错误轨迹,以管理初始条件和跟踪错误.
主要成果:
- 追踪错误和参数估计错误的证明统一的最终边界性.
- 证明跟踪错误在每个代中满足规定的短暂性能.
- 通过对单环弹性关节机器人的模拟来验证该方法.
结论:
- 拟议的事件触发自适应代学习 (PPC) 对于不确定的高阶非线性系统是有效的.
- 该方法增强了参数估计,避免了控制法奇点.
- 成功地解决了网络通信限制,同时确保规定的性能.
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