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Article type: Research Article
Authors: Khooban, Mohammad Hassan | Niknam, Taher* | Sha-Sadeghi, Mokhtar
Affiliations: Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
Correspondence: [*] Corresponding author. Taher Niknam, Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran. Tel.: +98 711 7264121; Fax: +98 711 7353502; E-mail: niknam@sutech.ac.ir.
Abstract: In this paper, in order to control a class of nonlinear uncertain power systems, a new simple indirect adaptive general type-II fuzzy sliding mode controller (IDAGT2FSMC) is proposed. For handling dynamic uncertainties, the proposed controller utilizes the advantages of general type-2 fuzzy logic systems (GT2FLS) to approximate unknown nonlinear actions and noisy data. Implementing general type-2 fuzzy systems is computationally costly; therefore, to decrease computational burden, the proposed method uses a recently introduced α-plane representation so that GT2FLS can be seen as a composition of several interval type-2 fuzzy logic systems (IT2FLS) with a corresponding level of α for each. The globally asymptotic stability of the closed-loop system is mathematically proved. To evaluate the superiority of the proposed controller, performance of the proposed method is compared with those of Indirect Adaptive type-1 Fuzzy Sliding Mode (IDAFSM) controller, Indirect Adaptive Interval Type-II Fuzzy Sliding Mode (IDAT2FSM) controller, conventional Sliding Mode controller (SMC) and PID controller results which are all among the most recent methods applied to the issue in question. Finally, the proposed method is applied to an uncertainly chaotic power system as a case study. Simulation indicates the effectiveness of the proposed controller while facing of dynamic uncertainties and external disturbances.
Keywords: Mathematical modeling, chaotic power systems, general type-II fuzzy, adaptive control, sliding mode control, nonlinear robust control
DOI: 10.3233/IFS-151796
Journal: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 5, pp. 2927-2937, 2016
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