Data-based decentralized learning scheme for nonlinear systems with mismatched interconnections

Abstract

In this paper, the decentralized learning scheme for nonlinear systems with mismatched interconnections is developed by using the off-policy integral reinforcement leaning algorithm. First, the decentralized control of the overall system is transformed into the optimal control of each subsystem by introducing an auxiliary control. In order to relax the knowledge of system dynamics, a model-free policy iteration algorithm is derived based on the off-policy integral reinforcement learning. Then, the model-free policy iteration algorithm is used to solve the related Hamilton–Jacobi-Bellman equations, where only the collected system data is required. For implementation purpose, neural networks are employed to approximate the optimal cost functions and the optimal control policies, respectively. Moreover, the least squares method and the experience replay technique are combined to learn neural network weights. Finally, a mismatched interconnected system and a photovoltaic power system are presented to verify the effectiveness of the proposed algorithm.