A Reinforcement Learning Automata Optimization Approach for Optimum Tuning of PID Controller in AVR System

In this paper, an efficient optimization method based on reinforcement learning automata (RLA) for optimum parameters setting of conventional proportional-integral-derivative (PID) controller for AVR system of power synchronous generator is proposed. The proposed method is Continuous Action Reinforcement Learning Automata (CARLA) which is able to explore and learn to improve control performance without the knowledge of the analytical system model. This paper demonstrates the full details of the CARLA technique and compares its performance with Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) as two famous evolutionary optimization methods. The simulation results show the superior efficiency and performance of the proposed method in regard to other ones.