Information Aggregation for Constrained Online Control

作     者：Li, Tongxin Chen, Yue Sun, Bo Wierman, Adam Low, Steven H. 

作者机构：CALTECH Pasadena CA 91125 USA Tsinghua Univ Beijing Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China 

出 版 物：《PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF COMPUTING SYSTEMS》 (Proc. ACM Meas. Anal. Comput. Syst.)

年 卷 期：2021年第5卷第2期

页      面：1–35页

核心收录：

基　　金：National Science Foundation (NSF) [CCF 1637598, ECCS 1931662, CPS ECCS 1932611] Hong Kong Research Grant Council (RGC) General Research Fund NSF [AitF-1637598, CNS-1518941] PIMCO Amazon AWS 

主　　题：online control closed-loop control model predictive control regret analysis electric vehicle charging 

摘      要：This paper considers an online control problem involving two controllers. A central controller chooses an action from a feasible set that is determined by time-varying and coupling constraints, which depend on all past actions and states. The central controller s goal is to minimize the cumulative cost;however, the controller has access to neither the feasible set nor the dynamics directly, which are determined by a remote local controller. Instead, the central controller receives only an aggregate summary of the feasibility information from the local controller, which does not know the system costs. We show that it is possible for an online algorithm using feasibility information to nearly match the dynamic regret of an online algorithm using perfect information whenever the feasible sets satisfy a causal invariance criterion and there is a sufficiently large prediction window size. To do so, we use a form of feasibility aggregation based on entropic maximization in combination with a novel online algorithm, named Penalized Predictive Control (PPC) and demonstrate that aggregated information can be efficiently learned using reinforcement learning algorithms. The effectiveness of our approach for closed-loop coordination between central and local controllers is validated via an electric vehicle charging application in power systems.