Design and application of adaptive PID controller based on asynchronous advantage actor–critic learning method

To address the problems of the slow convergence and inefficiency in the existing adaptive PID controllers, we propose a new adaptive PID controller using the asynchronous advantage actor–critic (A3C) algorithm. Firstly, the controller can train the multiple agents of the actor–critic structures in parallel exploiting the multi-thread asynchronous learning characteristics of the A3C structure. Secondly, in order to achieve the best control effect, each agent uses a multilayer neural network to approach the strategy function and value function to search the best parameter-tuning strategy in continuous action space. The simulation results indicate that our proposed controller can achieve the fast convergence and strong adaptability compared with conventional controllers.