基于RBF神经网络的强大的自适应控制,用于随机电磁悬挂系统.
Hamidreza Javanmardi1, Pooria Rookhand2, Alireza Hamedi3
1Department of Power and Control Engineering, Shiraz University, Shiraz, Iran.
ISA transactions
|August 12, 2025
概括
本研究介绍了一种强大的自适应神经网络控制器,用于不稳定的电磁悬浮系统. 新的方法确保稳定性和高性能,尽管模型的不确定性和噪音.
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:
- 电磁悬挂系统由于非线性动力学和对干扰的敏感性而表现出固有的不稳定性.
- 在这些系统中设计可靠的稳定性和高性能控制器是一个重大挑战.
- 模型不确定性和随机噪声进一步使控制器设计复杂化.
研究的目的:
- 为电磁悬浮系统提出一个强大的自适应神经网络控制器.
- 为了应对模型不确定性和随机干扰所带来的挑战.
- 为了在电磁悬挂系统中实现强大的稳定性和高性能.
主要方法:
- 利用辐射基函数神经网络来近似未知的系统参数.
- 使用随机局限稳定定理来管理随机噪声.
- 集成的命令过技术与最小的学习参数方法来简化设计和减少计算负载.
主要成果:
- 拟议的控制器有效地减轻了随机干扰的影响.
- 在存在模型不确定性的情况下表现出强大的稳定性.
- 在电磁悬挂系统中实现了高性能.
结论:
- 强大的自适应神经网络控制器对电磁悬挂系统有效.
- 集成命令过和最小的学习参数可以降低计算复杂性.
- 控制器在不确定性和干扰下确保了强大的稳定性和高性能.
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