对于具有状态约束和执行器歇斯底里的非线性时间延迟系统的有限时间规定的性能跟踪控制
Kexin Lu1, Huanqing Wang1, Fu Zheng2
1The School of Mathematical Sciences, Bohai University, Jinzhou 121000, China.
ISA transactions
|August 8, 2024
概括
本研究介绍了对时间延迟系统的自适应神经网络控制,以确保跟踪错误在不违反状态约束的情况下达到性能限制. 该方法使用辐射基函数网络和屏障Lyapunov函数进行强大的控制.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性动力学是一种非线性动力学.
背景情况:
- 时间延迟系统由于固有的系统滞后而带来了重大控制挑战.
- 确保满足状态约束,同时实现精确的跟踪对于许多应用程序至关重要.
- 适应性控制是必要的,以处理系统动态中的不确定性.
研究的目的:
- 为非严格的反时间延迟系统开发一种自适应神经网络控制策略.
- 为了保证系统状态的规定的性能跟踪.
- 确保系统状态在整个控制过程中保持在预定义的边界内.
主要方法:
- 使用辐射基函数 (RBF) 神经网络来近似未知系统函数.
- 采用后退设计与屏障莱普诺夫函数 (BLF) 结合,用于执行状态约束.
- 整合Lyapunov-Krasovskii函数来分析系统稳定性和在存在时间延迟时的趋同.
主要成果:
- 拟议的控制方案确保追踪错误汇聚到预定义的区域.
- 系统状态保证保持在指定的极限内,满足全状态约束.
- 适应性控制有效地弥补了系统的不确定性和时间延迟.
结论:
- 开发的自适应神经网络控制方案对具有状态约束的非严格反时间延迟系统有效.
- RBF NNs,BLF 和 Lyapunov-Krasovskii 函数的组合为规定的性能跟踪提供了一个强大的框架.
- 模拟结果验证了拟议的控制策略的性能和约束满足.
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