基于数据的最佳交换和控制,可接受性保证 Q-Learning
概括
本研究介绍了一种基于数据的方法,用于使用近似动态编程 (ADP) 进行非线性交换系统的最佳控制. 新的两阶段ADP算法确保了可接受的混合控制政策,用于实际应用.
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:
- 交换系统由于其混合性质而存在独特的控制挑战.
- 基于数据的控制设计对于模型不确定或复杂的系统至关重要.
- 大致动态编程 (ADP) 为解决复杂的控制问题提供了一个强大的框架.
研究的目的:
- 为离散时间非线性开关系统开发基于数据的最佳开关和控制代码设计方法.
- 确保设计的混合控制政策的可接受性,以便实际实施.
- 为拟议的算法提供严格的融合证明.
主要方法:
- 采用了两阶段的近似动态编程 (ADP) 算法,涉及离线政策改进和评估.
- 系统输入/输出数据被用来代地确定最佳的混合控制策略.
- 一个具有多个NN的演员关键神经网络 (NN) 结构接近子系统的Q功能和控制策略.
主要成果:
- 拟议的两阶段ADP算法保证了最佳混合控制政策设计的融合.
- 导出并应用一个可接受性标准,以确保控制政策的实际可行性.
- 数字模拟证明了开发的基于数据的控制策略的有效性.
结论:
- 基于数据的最佳交换和控制代码设计通过两阶段的ADP是有效的非线性交换系统.
- 开发的方法确保了可接受的混合控制政策,这对于现实世界的应用至关重要.
- 关键行为体的NN实施有助于实际实现拟议的控制战略.
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