Quasi continuous sliding mode control with fuzzy switching gain for an induction motor

Article type: Research Article

Authors: Panchade, V.M.^a | Chile, R.H.^b | Patre, B.M.^{b; *}

Correspondence: [*] Corresponding author. B. M. Patre, Department of Instrumentation Engineering, S.G.G.S. Institute of Engineering and Technology, Nanded, India. E-mail: [email protected].

Abstract: This manuscript investigates speed and flux tracking control method based on quasi-continuous sliding mode control (SMC) for induction motor (IM). To overcome the problem of conventional SMC, a quasi-continuous second-order sliding mode controller (QC2SMC) is first designed. QC2SMC produces continuous control and less chattering. Chattering exists during the control action of QC2SMC when the system remains on the sliding manifold. However, it is difficult to select sliding mode (SM) controller gain that minimizes the reaching time on sliding manifold. So as to reduce the chattering, fuzzy-adapted QC2SMC (FQC2SMC) is designed, in which the fuzzy system is employed to adaptively tune the SM controller gain. Moreover, with the intention of reduce chattering and achieve high tracking accuracy, quasi-continuous third-order sliding mode controller (QC3SMC) is investigated. However, the transient response of QC3SMC is very slow. This is the main constraint of QC3SMC and overcome by applying a fuzzy-adapted QC3SMC (FQC3SMC) is intended. Control structure incorporates a state observer based on a super twisting algorithm to estimate the rotor flux and torque from stator currents and rotor speed. By using robust exact differentiator, estimation of the sliding surface derivative and designing of an observer for load torque are not needed. The convergence of controllers can be guaranteed and analyzed by using Lyapunov function. Here, simulation results are included to validate the effectiveness of the proposed strategy.

Keywords: quasi continuous sliding mode, sliding mode observer (SMO), chattering phenomenon, robustness, induction motor (IM)

DOI: 10.3233/JIFS-17548

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 2, pp. 1837-1854, 2019