通过模型预测控制在线性朗格温系统中的最小信息变量
Adrian-Josue Guel-Cortez1, Eun-Jin Kim1, Mohamed W Mehrez2
1Centre for Fluid and Complex Systems, Coventry University, Priory St, Coventry CV1 5FB, UK.
Entropy (Basel, Switzerland)
|April 26, 2024
概括
我们开发了一种用于复杂系统的新控制方法,使用模型预测控制和信息几何学. 这种方法在系统动力学中最大限度地减少了"几何信息变化",通过Ornstein-Uhlenbeck和Kramers方程进行验证.
科学领域:
- 复杂系统动力学 复杂系统动力学
- 统计物理 统计物理
- 控制理论 控制理论
背景情况:
- 控制复杂系统中概率分布的时间演变是很困难的.
- 应用包括控制中观测系统.
- 现有的方法可能无法充分解决系统动态的几何性质.
研究的目的:
- 为复杂系统提出一种新的控制方法.
- 为了最大限度地减少信息长度随着时间的推移而偏离地理测量仪的偏差.
- 为了确保具有最小几何信息变量的动态.
主要方法:
- 将模型预测控制 (MPC) 与信息几何理论结合起来.
- 应用MPC的在线优化来确定系统输入.
- 专注于线性朗格温系统.
主要成果:
- 验证了奥恩斯坦-乌伦贝克过程和克莱默斯方程的方法.
- 证明了拟议的控制方法的可行性.
- 分析了Ornstein-Uhlenbeck过程对产量和率的影响.
结论:
- 建议的控制策略有效地减少了几何信息的变化.
- 提供对控制对产生的效应的物理理解.
- 为控制复杂系统动态提供了一个有前途的方向.
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