自动触发的自适应动态编程基于经验重播和光谱自适应定律.
Yuteng Tian1, Xuemei Ren1, Yongfeng Lv2
1School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
ISA transactions
|December 16, 2025
概括
本研究介绍了一种自动触发的自适应动态编程 (ADP) 框架,使用经验重复 (ER) 和光谱自适应定律 (SPAL) 进行高效,强大的未知非线性系统的最佳控制,减少通信需求.
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
- 机器学习 机器学习
背景情况:
- 对未知的非线性系统进行最佳控制具有重大挑战.
- 现有的自适应动态编程 (ADP) 方法通常需要持续监测,并与一般化作斗争.
- 事件触发方法需要对控制触发进行持续状态观察.
研究的目的:
- 提出一个新的自触发自适应动态编程 (ADP) 框架.
- 为未知非线性系统增强ADP的稳定性和效率.
- 通过自动触发机制来减少控制系统中的通信开销.
主要方法:
- 在ADP框架内整合经验重复 (ER) 和光谱适应法 (SPAL).
- 基于SPAL的新关键神经网络 (NN) 重量更新规律的开发,以提高概括性.
- 实现一个自我触发机制来预测下一个触发时刻,避免连续状态监控.
主要成果:
- 拟议的基于SPAL的NN重量更新法增强了实现最佳控制的概括能力.
- 基于ER的ADP方法,使用基于SPAL的NN系统标识符,证明了改进的稳定性.
- 与事件触发方法相比,自触发机制有效地保护了通信.
结论:
- 新的自触发ADP框架有效地实现了对未知的非线性系统的最佳控制.
- 集成ER和SPAL带来了更高的稳定性和效率.
- 拟议的方法显著减少了通过模拟验证的通信要求.
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