Title :
Application of reinforcement learning to improve control performance of plant
Author :
Shadi, M. ; Sargolzaei, M.
Author_Institution :
Sch. of Eng., Azad Univ. of Mashhad, Mashhad
Abstract :
This paper is concerned with the development of an online Reinforcement Learning (RL) technique that significantly improves the control systems behavior. The reinforcement learner is based on Q-learning and the final controller is an artificial neural network whose weights are tuned by on line learning. In order to speed up the learning processes and prevent the plant from the instability, initially a PID is utilized as an augmented controller until the reinforcement learning becomes capable of keep the system stable and prevent the system from undesirable behavior. Example of use is presented and the effectiveness of the proposed approach is shown.
Keywords :
control system synthesis; learning (artificial intelligence); learning systems; neurocontrollers; stability; three-term control; PID controller; Q-learning; artificial neural network; control design; control system behavior; online learning; plant control performance; plant stability; reinforcement learning; Artificial neural networks; Computational intelligence; Control design; Control systems; Learning; Mathematical model; Performance analysis; Performance evaluation; Table lookup; Three-term control;
Conference_Titel :
Computational Intelligence for Measurement Systems and Applications, 2008. CIMSA 2008. 2008 IEEE International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-2305-7
Electronic_ISBN :
978-1-4244-2306-4
DOI :
10.1109/CIMSA.2008.4595837