Stator flux estimation and hybrid sliding mode torque control of an induction motor

Agmuasie Belay (), Ayodeji Olalekan Salau (), Habitamu Endalamaew Kassahun () and Joy Nnenna Eneh ()
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Agmuasie Belay: University of Gondar
Ayodeji Olalekan Salau: Afe Babalola University
Habitamu Endalamaew Kassahun: University of Gondar
Joy Nnenna Eneh: University of Nigeria

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 6, No 42, 2553 pages

Abstract: Abstract Induction motors are used in a wide range of industrial applications, especially as variable speed drives and for motion control. These motors have complex dynamic performance and nonlinear characteristics. The torque ripples in steady state operation, particularly at low speeds limits their application. Although the field orientation control principle provides an effective algorithm but due to its complexity, a novel direct torque control (DTC) approach is proposed in this paper. The control approach’s simplicity and novel control characteristics were minimally negatively affected by significant torque and flux ripples. There are a wide range of applications for space vector modulation with the proposed DTC approach. Though some of the drawbacks of the conventional DTC have been addressed, which include issues such as robustness to parameter variation and load disturbance still remain unresolved. As a result, to mitigate these disadvantages, sliding mode DTC was applied to an induction motor control to reduce torque ripple. Therefore, in this paper, a novel space vector modulation (SVM) based fractional order sliding mode control (FOSMC) of induction motor is proposed. For this purpose, a fractional order proportional integral sliding surface was designed. To improve the control performance of the system, the speed control loop was implemented using super twisting sliding mode control (SMC). Furthermore, to estimate the stator flux, torque, and flux angle, neural networks has been employed. The proposed controller’s stability was examined using the Lyapunov stability analysis. The simulation results confirm the proposed control method’s effectiveness and validity. The sliding mode torque (SMT) control and estimators were found to be unaffected by parameter variation and load disturbance. A super twisting algorithm-based speed SMC, in conjunction with a fractional order proportional integral sliding surface was found to effectively reduce the torque ripples. Furthermore, chattering was reduced by using a super twisting algorithm for speed control. The torque ripple band was modified by ±6 Nm using the fractional SMC and the flux ripple band was modified by ±0.04 Wb.

Keywords: Neural network; Sliding mode control; Direct torque control; Space vector modulation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-024-02275-1

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