Research Article
The Adaptive PID Controlling Algorithm Using Asynchronous Advantage Actor-Critic Learning Method
@INPROCEEDINGS{10.1007/978-3-030-32216-8_48, author={Qifeng Sun and Hui Ren and Youxiang Duan and Yanan Yan}, title={The Adaptive PID Controlling Algorithm Using Asynchronous Advantage Actor-Critic Learning Method}, proceedings={Simulation Tools and Techniques. 11th International Conference, SIMUtools 2019, Chengdu, China, July 8--10, 2019, Proceedings}, proceedings_a={SIMUTOOLS}, year={2019}, month={10}, keywords={Deep Reinforcement Learning Asynchronous Advantage Actor-Critic Adaptive PID control}, doi={10.1007/978-3-030-32216-8_48} }- Qifeng Sun
Hui Ren
Youxiang Duan
Yanan Yan
Year: 2019
The Adaptive PID Controlling Algorithm Using Asynchronous Advantage Actor-Critic Learning Method
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-32216-8_48
Abstract
To address the problems of the slow convergence and inefficiency in the existing adaptive PID controllers, we proposed a new adaptive PID controller using the Asynchronous Advantage Actor-Critic (A3C) algorithm. Firstly, the controller can parallel train the multiple agents of the Actor-Critic (AC) structures 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 indicated that our proposed controller can achieve the fast convergence and strong adaptability compared with conventional controllers.
