用有限维度记忆进行分散的随机控制:一种记忆限制方法
Takehiro Tottori1, Tetsuya J Kobayashi1,2,3,4
1Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8654, Japan.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
存储器有限的去中心化随机控制 (ML-DSC) 为存储器有限的控制器提供了一个实用的解决方案. 这种新的框架通过压缩观测历史来优化控制策略,克服传统DSC的局限性.
科学领域:
- 控制理论 控制理论
- 优化优化 优化优化
- 随机系统 随机系统 随机系统
背景情况:
- 分散的随机控制 (DSC) 涉及多个控制器,其系统和控制器间观察能力有限.
- 传统的DSC面临的挑战是,控制器需要无限内存,并且不可能将复杂的估计减少到有限维的过器,即使是在LQG问题中.
研究的目的:
- 引入一个新的理论框架,内存有限的DSC (ML-DSC),解决传统DSC的实际局限性.
- 为了使控制器能够共同优化观察历史压缩和控制在有限内存约束的控制确定.
主要方法:
- 为控制器制定具有明确有限维内存的ML-DSC.
- 开发一种优化方法,控制器将无限维的观测历史压缩成有限维的记忆.
- 将ML-DSC应用于线性-正方形-高斯式 (LQG) 问题,以证明其有效性.
主要成果:
- ML-DSC为内存受限控制器提供了一个实用的配方.
- 拟议的框架成功地解决了LQG问题,与传统的DSC相比,控制器交互更为普遍.
- 与传统的DSC不同,ML-DSC不仅限于具有独立或部分嵌套控制器信息的场景.
结论:
- ML-DSC代表了去中心化随机控制的重大进步,使其适用于具有内存限制的真实世界系统.
- 该框架通过适应复杂的控制器间依赖关系来扩大可解决的LQG问题的范围.
- ML-DSC为分散控制优化提供了一种可行和更普遍的方法.
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