基于神经网络的适应性故障耐受性控制,用于具有不确定性的非线性系统
1Department of Computer and Control Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt; Information Technology Department, Faculty of Computing and Information, Al-Baha University, Al-Baha 65779, Saudi Arabia.
ISA transactions
|July 5, 2024
概括
本研究引入了一种使用神经网络 (NN) 进行非线性系统实时不确定性估计的新故障耐受性控制 (FTC) 方案. 该方法提高了系统的稳定性和性能,尽管复杂的动态和测量不确定性.
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:
- 非线性系统由于输入,状态和输出的复杂动态,对控制具有重大挑战.
- 测量不确定性和非线性使强大的控制系统的设计变得复杂.
- 现有的容错控制 (FTC) 方法在这种环境中经常与实时不确定性估计作斗争.
研究的目的:
- 为非线性系统开发一种新的故障耐受性控制 (FTC) 方案,使其能够实时估计不确定性.
- 在输出反框架内应对非线性动态和测量不确定性所带来的挑战.
- 为了确保系统的稳定性,并在存在不确定性时实现非对称状态跟踪.
主要方法:
- 建议基于神经网络 (NN) 描述符的观察者同时估计系统状态和传感器不确定性,能够处理无限的不确定性.
- 神经网络被用作通用近似器来建模系统的复杂非线性动态.
- 一个强大的模型参考跟踪控制器利用观察者的估计来保持所需的系统性能和稳定性.
主要成果:
- 提出的基于NN描述符的观察器有效地估计了非线性系统中的系统状态和传感器不确定性.
- 强大的控制器保证了系统稳定性和非对称状态跟踪,即使估计的不确定性.
- 联邦贸易委员会计划通过理论验证和应用到现实世界案例研究来证明其有效性.
结论:
- 开发的FTC方案为具有挑战性的非线性系统的实时不确定性估计提供了强大的解决方案.
- 基于NN的观察员和强大的控制器的集成为提高系统性能和可靠性提供了一个强大的框架.
- 该方法在复杂的工程系统中得到了实际应用的验证.
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