Generalized Actor-Critic Learning Optimal Control in Smart Home Energy Management

Abstract

This article is concerned with a new generalized actor-critic learning (GACL) optimal control method. It aims at the optimal energy control and management for smart home systems, which is expected to minimize the consumption cost for home users. In the present GACL optimal control method, it is the first time that three iteration processes, which are global iteration, local iteration, and interior iteration, respectively, are established to obtain the optimal energy control law. The main contribution of the developed method is to establish a common iteration structure for both value and policy iterations in adaptive dynamic programming based on a control law sequence in each iteration for periodic time-varying systems, instead of a single control law, and simultaneously accelerates the convergence rate. The monotonicity, convergence, and optimality of the iterative value function for the GACL optimal control method are proven. Finally, numerical results and comparisons are displayed to show the superiority of the developed method.