自动触发的神经跟踪控制对离散时间非线性系统通过自适应的批评学习
Lingzhi Hu1, Ding Wang1, Gongming Wang1
1School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China; Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing 100124, China; Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China.
概括
一种新的自触发的最佳跟踪控制方法,用于离散时间非线性系统的在线行动关键学习. 这种方法通过仅在必要时触发控制操作来减少计算,从而提高效率和性能.
科学领域:
- 控制系统工程 控制系统工程
- 非线性系统分析 非线性系统分析
- 人工智能在控制中
背景情况:
- 传统的基于事件的控制方法需要持续监控触发条件,从而增加计算负载.
- 为离散时间非线性系统设计最佳的跟踪控制提出了重大挑战.
- 实时适应和最佳的政策调整对于先进的控制应用至关重要.
研究的目的:
- 为离散时间非线性系统开发一种新的自我触发的最佳跟踪控制方法.
- 为了减少与传统基于事件的控制策略相关的计算负担.
- 为了提高触发性能和整体控制政策优化.
主要方法:
- 通过整合系统状态和参考轨迹来构建一个增强的工厂.
- 一个自取样函数,依赖于跟踪错误,确定触发时刻.
- 在线行动关键学习与模型,批评和行动神经网络被用于实时政策调整.
主要成果:
- 拟议的方法通过消除连续触发条件评估,有效地降低了计算负担.
- 自动触发控制策略显示出出色的触发性能.
- 通过实时调整控制政策来实现最佳的跟踪控制.
结论:
- 开发的自触发的最佳跟踪控制方法为离散时间非线性系统提供了高效和有效的解决方案.
- 这种方法确保了系统稳定性,同时优化了控制性能.
- 实验验证证了在线自动触发追踪控制策略的有效性.
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