Authors: D. Ganeshaperumal; N. Muthukumar; K. Ramkumar; Seshadhri Srinivasan; B. Subathra
Addresses: Department of EEE, Kalasalingam University, Srivilliputtur 626126, India ' Electric Vehicle Engineering and Robotics (EVER) Labs, SASTRA University, Thanjavur 613401, India ' Electric Vehicle Engineering and Robotics (EVER) Labs, SASTRA University, Thanjavur 613401, India ' International Research Centre, Kalasalingam University, Srivilliputtur 626126, India ' Department of Instrumentation and Control, Kalasalingam University, Srivilliputtur 626126, India
Abstract: This investigation presents an optimisation-driven fractional order PID (FOPID) controller design methodology for brushless direct current (BLDC) motor speed control for electric vehicle applications. Though the introduction of fractional terms provides additional flexibility, their optimal selection is important for achieving desired performance. For this purpose, this investigation uses two evolutionary optimisation approaches-real coded genetic algorithm (RGA) and bio-geography based optimisation (BBO). In order to illustrate the improvements provided by FOPID, its performance is compared with conventional PID controller. Our results demonstrate that the FOPID controller tuned by BBO algorithm provides up to 50% improvements in transient response over the PID controller.
Keywords: BLDC; brushless direct current motors; BBO; bio-geography based optimisation; evolutionary optimisation; FOPID; fractional order PID; proportional integral derivative controller; RGA; real coded genetic algorithms; optimal design; controller design; electric drivetrains; electric vehicles; motor speed control; tuning.
International Journal of Electric and Hybrid Vehicles, 2016 Vol.8 No.4, pp.335 - 350
Received: 22 Aug 2016
Accepted: 29 Aug 2016
Published online: 02 Dec 2016 *