Optimal tracking control of switched systems applied in grid-connected hybrid generation using reinforcement learning

Abstract

The paper presents a reinforcement learning approach for optimal tracking control of switched systems with application to a grid-tied hybrid generation system. To enhance interaction with the irregular environment, reference trajectory is learned via controller from states to optimal control. The main issue is to solve the optimal tracking control problem for a hybrid generation system consisting of multiple switched subsystems, and reinforcement learning can seek the globally optimal solution well without knowing accurate system dynamics. The investigated learning algorithm is used to generate an optimum map based on the learned ultimate value without knowledge of system parameters and obtains the optimal control law via deriving of algebraic Riccati equation (ARE) with unnecessary knowing of command generator dynamics. The optimal control solution can converge the online learning algorithm well based on policy iteration as verification in the simulation.