滚动时域控制与耗散性——最优控制、博弈与不确定性
Receding Horizon Control and Dissipativity - Optimal Control, Games and Uncertainty
- ETH Zürich（苏黎世联邦理工学院）
- University of Bayreuth（拜罗伊特大学）
- Delft University of Technology（代尔夫特理工大学）
- Hamburg University of Technology（汉堡工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文综述了滚动时域控制在确定性、随机、多智能体及博弈场景中的应用，以耗散性和转折点性质为统一框架，对比最优控制与博弈均衡，并总结数值方法趋势与开放挑战。
AI中文摘要：
我们提供了关于滚动时域控制在确定性、随机、多智能体和博弈论环境中的教程性概述。我们对比了最优控制问题的极小化器与博弈的均衡在成本和约束处理方面的差异，以激发涉及多个智能体/参与者的MPC方案的不同之处。有趣的是，耗散性理论和转折点性质已被证明是所有这些方案中的统一主线与系统理论支柱。本文首次给出了结果的概述，总结了基本见解，绘制了相似之处，并强调了在经济、博弈论和随机环境中耗散性与转折点分析的技术差异。我们还探讨了博弈数值方法的最新趋势。最后，我们指出了一系列开放性挑战。
英文摘要：
We provide a tutorial overview of receding horizon control across deterministic, stochastic, multi-agent, and game-theoretic settings. We contrast the minimizer of an optimal control problem with the equilibrium of a game in terms of cost and constraint handling to motivate the difference of MPC schemes involving multiple agents/players. Interestingly, dissipativity theory and the turnpike property have turned out to be a unifying thread and system-theoretic backbone across all these schemes. This paper is the first to give an overview of results, summarizing fundamental insights, drawing parallels, and highlighting technical differences in dissipativity and turnpike analysis across economic, game theoretic, and stochastic settings. We also explore recent trends in numerical methods for games. We conclude by pointing to a list of open challenges.
