虚拟目标导向神经学习,用于对离散的严格反系统进行强大的最佳跟踪控制.
IEEE transactions on neural networks and learning systems
|September 26, 2025
概括
本研究介绍了一种新的层次神经学习算法,用于对非线性系统进行最佳的跟踪控制. 该方法有效地处理未知的动态和干扰,提高控制精度和减少工作量.
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:
- 非线性严格反系统 (SFSs) 由于未知的动态和不匹配的干扰 (uMDs) 存在重大控制挑战.
- 对于SFS的传统离散时间控制方法,往往存在非因果关系问题.
- 基于模型的控制需要准确的系统识别,这往往是不可行的.
研究的目的:
- 开发一个数据驱动的层次神经学习 (HNL) 算法,以实现非线性SFS的最佳跟踪控制 (OTC).
- 为应对无匹配干扰 (uMDs) 和未知的系统动态所带来的挑战.
- 在离散时间SFS控制中消除非因果问题.
主要方法:
- 提出了一个虚拟目标 (VT) 建设方案,利用SFS的递归结构.
- 采用时间变化的亲属汉密尔顿 - 雅各比 - 艾萨克斯 (HJI) 配方,将辅助控制与干扰联系起来.
- 采用了自适应动态编程 (ADP) 框架与新的跟踪网络 (T网络) 来从输入输出数据中进行控制器合成.
主要成果:
- 在不需要精确的工厂模型的情况下,HNL算法实现了非线性SFS的最佳跟踪控制.
- 拟议的T网络通过合并梯度信息和未来的跟踪错误来加强政策更新.
- 模拟显示出出色的性能,对MUD的稳定性,以及对显著模型不确定性的耐受性.
结论:
- 开发的HNL算法为复杂非线性系统的OTC提供了强大的和有效的解决方案.
- 数据驱动的方法消除了对精确系统模型的需求,使其广泛适用.
- 增强的神经架构确保同时降低控制力度和提高跟踪精度.
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