Adaptive Controller of PEMFC Output Voltage Based on Ambient Intelligence Large-Scale Deep Reinforcement Learning

Abstract

In this article, an adaptive Proportion integration (PI) controller for varying the output voltage of a proton exchange membrane fuel cell (PEMFC) is proposed. The PI controller operates on the basis of ambient intelligence large-scale deep reinforcement learning. It functions as a coefficient tuner based on an ambient intelligence exploration multi-delay deep deterministic policy gradient (AIEM-DDPG) algorithm. This algorithm is an improvement on the original deep deterministic police gradient (DDPG) algorithm, which incorporates ambient intelligence exploration. The DDPG algorithm serves as the core, and the AIEM-DDPG algorithm runs on a variety of deep reinforcement learning algorithms, including soft actor-critic (SAC), deep deterministic policy gradient (DDPG), proximal policy optimization (PPO) and double deep Q-network (DDQN) algorithms, to attain distributed exploration in the environment. In addition, a classified priority experience replay mechanism is introduced to improve the exploration efficiency. Clipping multi-Q learning, policy delayed updating, target policy smooth regularization and other methods are utilized to solve the problem of Q-value overestimation. A model-free algorithm with good global searching ability and optimization speed is demonstrated. Simulation results show that the AIEM-DDPG adaptive PI controller attains better robustness and adaptability, as well as a good control effect.