根据非线性卢恩伯格状态观察者模型的探器模型的自适应动态表面控制
Mahdi Kamali Dolatabadi1, Marzieh Kamali1, Farzaneh Shayegh1
1Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
International journal of neural systems
|April 2, 2025
概括
这项研究为Epileptor模型引入了一种新的自适应动态表面控制器和Luenberger状态观察器,提高了发作模拟的准确性. 综合系统有效地跟踪参考值,改善计算性研究.
科学领域:
- 神经学 神经学
- 计算神经科学是一种神经科学.
- 控制系统工程 控制系统工程
背景情况:
- 是一种神经系统疾病,以反复发作为特征,通常使用像Epileptor这样的计算模型进行研究.
- 型模型模拟了动态,但由于其非线性,非严格的反性质和固有的不确定性,提出了挑战.
- 准确的状态估计对于控制和理解虫模型至关重要,特别是当只有局部场潜力 (LFP) 可测量时.
研究的目的:
- 为Epileptor模型开发和验证一个自适应动态表面控制器.
- 设计一个非线性Luenberger状态观察器,用于估计Epileptor模型中无法测量的状态.
- 在观察者-控制器框架内集成辐射基神经网络 (RBNNs) 来进行非线性动态估计.
主要方法:
- 为非线性Epileptor模型设计了一个自适应动态表面控制器.
- 一个非线性Luenberger状态观察器,利用RBNNs进行非线性动态近似,被开发用于估计从LFP信号的系统状态.
- 闭环系统 (控制器和观察器) 的稳定性通过数学分析严格证明.
- 通过模拟来评估性能,展示状态和输出跟踪能力.
主要成果:
- 拟议的自适应动态表面控制器和Luenberger状态观察器成功估计了Epileptor模型的状态.
- 综合系统证明了对状态和输出都具有可接受的错误率的参考值的有效跟踪.
- 模拟结果证实了新型控制和观察策略的稳定性和性能.
结论:
- 开发的自适应动态表面控制器和Luenberger状态观察器代表了控制和模拟Epileptor模型的重大进步.
- 这种方法为计算性研究中的状态估计和系统控制提供了一个强大的方法,利用RBNNs提高准确性.
- 这些发现为更复杂的建模和基于计算动态的的潜在治疗干预铺平了道路.
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