Backstepping control and revamped recurrent fuzzy neural network with mended ant colony optimization applied in SCRIM drive system

Article type: Research Article

Authors: Lin, Chih-Hong^{; *}

Affiliations: Department of Electrical Engineering, National United University, Miaoli, Taiwan

Correspondence: [*] Corresponding author. Chih-Hong Lin, Department of Electrical Engineering, National United University, Miaoli 360, Taiwan. E-mail: jhlin@nuu.edu.tw

Abstract:  A six-phase copper rotor induction motor (SCRIM) drive system causes a lot of nonlinear effects such as nonlinear uncertainties. To obtain better performance, the backstepping control system using switching function is firstly proposed for controlling the SCRIM drive system. To reduce chattering in control effort, the backstepping control system using revamped recurrent fuzzy neural network (RFNN) with mended ant colony optimization (ACO) is secondly proposed for controlling the SCRIM drive system to raise robustness of system. Furthermore, four variable learning rates of the weights in the revamped RFNN are adopted by using mended ACO to speed-up parameter’s convergence. Finally, comparative performances through some experimental results are verified that the proposed backstepping control system by means of revamped RFNN with mended ACO has better control performances than the other methods for the SCRIM drive system.

Keywords: Ant colony optimization, backstepping control, Lyapunov stability, recurrent fuzzy neural network, six-phase copper rotor induction motor

DOI: 10.3233/JIFS-181201

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3447-3459, 2019