Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Noshadi, Amin; Shi, Juan; Lee, Wee; Shi, Peng; Kalam, Akhtar
• Is Part Of: Neural computing & applications. Volume 27:Issue 7(2016)
• Page Start: 2031
• Page End: 2046
• Publication Date: 2016-10
• Subjects: PID-type fuzzy logic controller -- Active magnetic bearing system -- Genetic algorithm -- Particle swarm optimization -- Grey wolf optimization -- Imperialist competitive algorithm; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1996-7
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract The performance of the fuzzy controllers depends highly on the proper selection of some design parameters which is usually tuned iteratively via a trial and error process based primarily on engineering intuition. With the recent developments in the area of global optimization, it has been made possible to obtain the optimal values of the design parameters systematically. Nevertheless, it is well known that unless a priori knowledge is available about the optimization search-domain, most of the available time-domain objective functions may result in undesirable solutions. It is consequently important to provide guidelines on how these parameters affect the closed-loop behavior. As a result, some alternative objective functions are presented for the time-domain optimization of the fuzzy controllers, and the design parameters of a PID-type fuzzy controller are tuned by using the proposed time-domain objective functions. Finally, the real-time application of the optimal PID-type fuzzy controller is investigated on the robust stabilization of a laboratory active magnetic bearing system. The experimental results show that the designed PID-type fuzzy controllers provide much superior performances than the linear on-board controllers while retaining lower profiles of control signals.