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对于未知非线性系统的基于深度神经网络的自适应控制的加速渐变方法
概括
本研究介绍了一种使用加速梯度方法的深度神经网络 (DNN) 适应控制器,以改善未知不确定性的非线性系统的轨迹跟踪. 与以前的方法相比,新方法可以提高复杂系统的性能.
科学领域:
- 适应性控制系统适应性控制系统
- 神经网络在控制工程中的应用.
- 非线性系统动力学 非线性系统动力学
背景情况:
- 最近的自适应控制研究与内斯特罗夫的加速梯度方法相连,产生了新的实时适应规律.
- 之前在加速梯度自适应控制器上的工作假设有线性参数 (LIP) 不确定性,限制了它们的应用.
- 之前的基于神经网络 (NN) 的非LIP不确定性的控制器仅限于单隐层架构.
研究的目的:
- 开发一种通用深度神经网络 (DNN) 架构,用于对具有非LIP不确定性的非线性系统进行自适应控制.
- 创建一个基于DNN的新型加速梯度适应方案,用于实时估计DNN重量.
- 为了保证全球非线性跟踪错误的融合,一般的非线性控制与未知动态和干扰的亲系系统.
主要方法:
- 一个通用的深度神经网络 (DNN) 架构的开发.
- 实施基于DNN的新型加速梯度适应方案,用于实时重量估计.
- 应用基于Lyapunov的分析来确保控制系统的稳定性和性能.
主要成果:
- 开发的基于梯度的加速DNN适应方案实现了全球非对称跟踪错误的融合.
- 该方法有效地处理未知的非LIP漂移动态和非线性系统中的外源性干扰.
- 对各种系统的模拟研究表明,增强了跟踪和函数近似性能.
结论:
- 拟议的基于DNN的加速梯度自适应控制方案为复杂的非线性系统中的轨迹跟踪提供了强大的解决方案.
- 一般化的DNN架构和适应方案优于以前的方法,特别是在处理非LIP的不确定性方面.
- 这项工作通过将加速梯度方法扩展到深度神经网络架构以改善现实世界的应用性来推进自适应控制领域.
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