动态增益神经网络基于观察者的规定的性能后退滑动模式控制不确定的非线性系统的控制
Linping Chan1, Haiping Du1, Chengxin Huo1
1School of Electrical, Computer & Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia.
ISA transactions
|December 16, 2025
概括
这项研究为面临未知干扰的非线性系统引入了一个新的控制框架. 它使用动态增益神经网络观察器和集成非单元快速终端滑动模式控制来提高性能和可靠性.
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.
- 在工程领域的人工智能.
背景情况:
- 非线性系统往往面临着未知的干扰和无法测量的状态的挑战,阻碍了精确的控制.
- 传统的观察者需要手动调整,这对于具有时间变化的不确定性系统来说是不切实际的.
- 实现特定的瞬态和稳定状态性能极限对于可靠的现实应用至关重要.
研究的目的:
- 为具有未知的干扰和不确定性的非线性系统开发一个强大的控制框架.
- 通过确保跟踪错误符合预先规定的性能要求,提高系统可靠性.
- 提高国家观察员对非线性系统的适应能力.
主要方法:
- 设计了一个规定的性能后退的滑动模式控制 (SMC) 框架.
- 纳入了一个动态增益神经网络观察器来估计无法测量的状态并处理不确定性.
- 一个完整的非单元快速终端SMC (INFTSMC) 策略与规定的性能控制 (PPC) 集成.
- 利亚普诺夫理论被用于稳定性分析.
主要成果:
- 动态增益观察器在实时中自适应地调整其增益,消除了精确调整的需要.
- 集成的INFTSMC和PPC战略确保追踪错误满足定义的过渡和稳定状态要求.
- 建议的控制方法有效地管理系统动态,而观察者则补偿非线性.
- 模拟结果验证了开发的控制框架的有效性和稳定性.
结论:
- 拟议的控制框架为具有未知干扰的非线性系统提供了强大而可靠的解决方案.
- 动态增益神经网络观察器和INFTSMC与PPC的组合显著提高了控制性能.
- 该方法通过确保遵守性能规范和系统稳定性来证明其实际适用性.
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