Parameter optimization of PID controllers by reinforcement learning

This paper focuses on implementing a reinforcement learning algorithm for solving parameter optimization problems of Proportional Integral Derivative (PID) controllers. Function Optimization by Reinforcement Learning (FORL) remarkably outperforms a number of population-based intelligent algorithms when executed on benchmark functions in high-dimension circumstances. Therefore, this paper aims at examining the performance of FORL when optimizing parameters of PID controllers in a low-dimension space. According to the experiment studies in this paper, FORL is able to optimize the PID parameters with advantage over GA and PSO in terms of convergence speed.