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An Adaptive Controller using Radial Basis Function Neural Network with Reinforcement Learning

Niusha Shafiabady, M. A. Nima Vakilian, Dino Isa Published in Artificial Intelligence

Communications on Applied Electronics
Year of Publication: 2015
© 2015 by CAE Journal

Niusha Shafiabady, Nima M a Vakilian and Dino Isa. Article: An Adaptive Controller using Radial Basis Function Neural Network with Reinforcement Learning. Communications on Applied Electronics 1(7):7-13, May 2015. Published by Foundation of Computer Science, New York, USA. BibTeX

	author = {Niusha Shafiabady and M.a. Nima Vakilian and Dino Isa},
	title = {Article: An Adaptive Controller using Radial Basis Function Neural Network with Reinforcement Learning},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {1},
	number = {7},
	pages = {7-13},
	month = {May},
	note = {Published by Foundation of Computer Science, New York, USA}


A self-tuning PID control strategy using reinforcement learning is given to deal with conventional tracking control problems. Actor-Critic learning is used to tune PID parameters in an adaptive way to take advantage of the reinforcement learning properties. This policy is model-free and RBF neural network is used to approximate the parameters of PID controller. The critic part is designed to evaluate the actor part's efficiency and compensate its disabilities producing TD error that is calculated by the temporal difference of the value function between successive states in the state transition. The inputs of RBF network are system error, as well as the first and the second order differences of error. Both PSO and gradient descent are used to train the network's parameters and the controller has had a good performance being applied on the four plants.


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Reinforcement learning, Particle Swarm Optimization, Gradient Descent, Adaptive controller, Radial Basis Function, Fuzzy Neural Network