Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

Abstract

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (N_i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.

Keywords::

• ant colony optimization
• antlion optimization
• bat algorithm
• brushless DC motor
• genetic algorithm
• particle swarm optimization
• recurrent fuzzy neural network controller

Additional information

Notes on contributors

Kamaraj Premkumar

Kamaraj Premkumar received the B.E. degree from Anna University Chennai, Tamil Nadu, India, in 2005, and M.E. Degree from Anna University Chennai, Tamil Nadu, India, in 2007, all in faculty of electrical and electronics engineering. He obtained his Ph.D. degree from Anna University, Chennai, in the year 2015. Also, he is working as an Associate Professor in the Department of Electrical and Electronics Engineering of Rajalakshmi Engineering College, Chennai, Tamil Nadu, India. His current research interests include designing of speed and current controllers based on PID controller, fuzzy logic controller, ANFIS controller, and CANFIS controller for the special electrical machines.

Bairavan Veerayan Manikandan

Bairavan Veerayan Manikandan obtained his B.E. degree in Electrical and Electronics Engineering during 1990 and M.E. degree in Power Systems Engineering during 1992 from Madurai Kamaraj University. He obtained his Ph.D. degree from Anna University, Chennai, in the year 2010. Presently, he is working as Professor in the Electrical and Electronics Engineering department of Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India. His special fields of interest include power system restructuring issues, FACTS controllers, special machines and drives & controls.

Chellappan Agees Kumar

Chellappan Agees Kumar received the B.E. degree from Anna University, Chennai, Tamil Nadu, India, and M.E. Degree from Anna University, Chennai, Tamilnadu, India, all in faculty of electrical and electronics engineering. He obtained his Ph.D. degree from Anna University, Chennai. Also, he is working as Professor in the Department of EEE, Arunachala College of Engineering for Women, Vellichanthai, Tamil Nadu, India. His current research interests include power electronics, electrical drives, and soft computing.