A New Multi-objective Artificial Bee Colony Algorithm for Optimal Adaptive Robust Controller Design

Abstract

Artificial bee colony algorithm as a recent meta-heuristic algorithm, inspired from the foraging behavior of honey bees, can be considered as a proper technique to handle optimization problems. In this paper, a multi-objective artificial bee colony algorithm is introduced in which an archive is defined to store non-dominated solutions. Furthermore, to reduce computations and to have evenly distributed solutions, the archive is pruned using a technique based on neighborhood radius concepts. A group of bees that is responsible for improvement of the solutions chooses and exploits one solution of the archive. Because of the definition of neighborhood radius and retaining adjacent solutions, the remaining solutions have an equal chance to be selected by onlooker bees. In order to examine the proposed algorithm, some benchmark functions are used and the results are compared with true Pareto fronts. Moreover, the algorithm is utilized to optimize the coefficients of a new combined controller applied to a ball-beam system. In fact, the proposed controller is a combination of robust decoupled sliding mode and adaption laws based on the gradient decent method. The objective functions are considered as the integral time of absolute of errors of the ball position and the beam angle that should be minimized with a constraint on the control effort. To evaluate and validate the suggested approach, the obtained time responses of the ball-beam system are compared with those of other recently reported controllers.

KEYWORDS:

• Artificial bee colony
• Multi-objective optimization algorithm
• Robust decoupled sliding mode control
• Adaptive gradient decent laws
• Ball-beam system

Acknowledgments

The authors would like to thank Ms Mahdis Bisheban (PhD Candidate in the Department of Mechanical and Aerospace Engineering at the George Washington University) for her valuable suggestions that enhance the technical and scientific quality of this paper.

Additional information

Notes on contributors

Mohammad Javad Mahmoodabadi

Mohammad Javad Mahmoodabadi received his BSc and MSc degrees in mechanical engineering from Shahid Bahonar University of Kerman, Iranin 2005 and 2007, respectively. He received his PhD degree in mechanical engineering from the University of Guilan, Rasht, Iran in 2012. He worked for 2 years in the Iranian textile industries. During his research, he was a scholar visitor with Robotics and Mechatronics Group, University of Twente, Enchede, the Netherlands for 6 months. Now, he is an assistant professor of Mechanical Engineering at the Sirjan University of Technology, Sirjan, Iran. He has published about 100 scientific articles in international journals and conference proceedings. His research interests include optimization algorithms, non- linear and robust control, and computational methods. Corresponding author. Email: [email protected]; [email protected]

Mohammad Mehdi Shahangian

Mohammad Mehdi Shahangian received his BSc degree in mechanical engineering from Islamic Azad University, Iran in 2012 and his MSc degree in mechanical engineering from Sirjan University of Technology, Iran in 2015. His research interests include optimal control, robust control and evolutionary algorithms. Email: [email protected]