Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

作     者：Jehangir Arshad Ayesha Khan Mariam Aftab Mujtaba Hussain Ateeq Ur Rehman Shafiq Ahmad Adel M.Al-Shayea Muhammad Shafiq 

作者机构：Department of Electrical&Computer EngineeringCOMSATS University IslamabadLahore Campus54000Pakistan Department of Electrical EngineeringGovernment College UniversityLahore54000Pakistan Industrial Engineering DepartmentCollege of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication EngineeringYeungnam UniversityGyeongsan38541Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第4期

页      面：1153-1169页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the King Saud University in Riyadh Saudi Arabia through the Researchers Supporting Project Number(RSP-2021/387). 

主　　题：Feedback control systems reinforcement learning artificial intelligence 

摘      要：Controlling feedback control systems in continuous action spaces has always been a challenging problem.Nevertheless,reinforcement learning is mainly an area of artificial intelligence(AI)because it has been used in process control for more than a decade.However,the existing algorithms are unable to provide satisfactory results.Therefore,this research uses a reinforcement learning(RL)algorithm to manage the control system.We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient(DDPG).The actor-critic scenario using DDPG is implemented to build the RL agent.In addition,a framework has been created for traditional feedback control systems to make RL implementation easier for control systems.The RL algorithms are robust and proficient in using trial and error to search for the best strategy.Our proposed algorithm is a deep deterministic policy gradient,in which a large amount of training data trains the agent.Once the system is trained,the agent can automatically adjust the control parameters.The algorithm has been developed using Python 3.6 and the simulation results are evaluated in the MATLAB/Simulink environment.The performance of the proposed RL algorithm is compared with a proportional integral derivative(PID)controller and a linear quadratic regulator(LQR)controller.The simulation results of the proposed scheme are promising for the feedback control problems.