Online Optimization and Ambiguity-Based Learning of Distributionally Uncertain Dynamic Systems

• 成果类型: Article
• 署名作者: Li, Dan; Fooladivanda, Dariush; Martinez, Sonia
• 署名单位: University of California System; University of California San Diego; University of California System; University of California Berkeley
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3396378
• 发表日期: 2024
• 页码: 6153-6168
• 关键词: Optimization; uncertainty; dynamical systems; Probabilistic logic; Heuristic algorithms; vectors; data models; adaptive learning; data-driven modeling; optimization under distributional uncertainties

摘要

This article proposes a novel approach to construct data-driven online solutions to optimization problems (P) subject to a class of distributionally uncertain dynamical systems. The introduced framework allows for the simultaneous learning of distributional system uncertainty via a parameterized, control-dependent ambiguity set using a finite historical dataset, and its use to make online decisions with probabilistic regret function bounds. Leveraging the merits of machine learning, the main technical approach relies on the theory of distributional robust optimization (DRO), to hedge against uncertainty and provide less conservative results than standard robust optimization approaches. Starting from recent results that describe ambiguity sets via parameterized, and control-dependent empirical distributions as well as ambiguity radii, we first present a tractable reformulation of the corresponding optimization problem while maintaining the probabilistic guarantees. We then specialize these problems to the cases of 1) optimal one-stage control of distributionally uncertain nonlinear systems, and 2) resource allocation under distributional uncertainty. A novelty of this work is that it extends DRO to online optimization problems subject to a distributionally uncertain dynamical system constraint, handled via a control-dependent ambiguity set that leads to online-tractable optimization with probabilistic guarantees on regret bounds. Further, we introduce an online version of the Nesterov's accelerated-gradient algorithm, and analyze its performance to solve this class of problems via the dissipativity theory.