Optimum Design of an Adaptive Fuzzy Controller as Active Suspension for a Quarter-Car Model

Abstract

This research introduces an optimal adaptive fuzzy controller consisting of two fuzzy systems each including 1 output, 2 inputs, and 25 fuzzy rules. The designed fuzzy sytems, in turn, comprises Gaussian membership functions and applies the singleton fuzzifier, center average defuzzifier, and product inference engine. Moreover, the Gravitational Search Algorithm (GSA) is utilized to ascertain the optimum parameters of the controller. The body acceleration and the relative displacement between the tire and the sprung mass are utilized to form a proper objective function in the optimization procedure. Results show the dominance of the proposed approach over the conventional controllers.

KEYWORDS:

• Active suspension system
• Adaptive control
• Fuzzy control
• Gravitational search algorithm
• Optimal control
• Quarter-car model

Additional information

Notes on contributors

M. J. Mahmoodabadi

Mohammad Javad Mahmoodabadi received his BSc and MSc degrees in mechanical engineering from Shahid Bahonar University of Kerman, Iran in 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 over 100 scientific articles in international journals and conference Proceedings. His research interests include optimization algorithms, non- linear and robust control, and computational methods.

M. Javanbakht

Mohammad Javanbakht received his BSc degree in mechanical engineering from Islamic Azad University, Iran in 2012, and 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]