Second-order sliding mode-direct torque control and load torque estimation for sensorless model reference adaptive system–based induction machine

Ammar, Abdelkarim

doi:10.1177/0959651820935694

Cited by 10 publications

(8 citation statements)

References 34 publications

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“…Here, sgn (si) is a sign function that returns 1 if si is greater than 0, -1 if si is less than 0, and 0 otherwise. Also, λ11, λ22 and λa1, λb1, λa23, λb23 are positive gains used for the sliding surfaces and super twisting algorithm, respectively; 0 < ρ ≤ 0.5 is a fractional coefficient [52]. From (25), after assuming that the disturbance d is bounded such as |d |≤ δ, where δ is a known positive constant, the closed loop system can be given as…”

Section: Sliding Mode Control Strategymentioning

confidence: 99%

Circle Search Algorithm-Based Super Twisting Sliding Mode Control for MPPT of Different Commercial PV Modules

Ghazi,

Al-Ammar,

Hasanien

et al. 2024

IEEE Access

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The efficiency of grid-connected photovoltaic (PV) systems can be improved using a suitable maximum power point tracking (MPPT) control. Many MPPT techniques have been developed. However, when atmospheric conditions change rapidly, the PV modules respond nonlinearly and the performance of these methods especially the conventional ones is significantly reduced. Therefore, to successfully pursue the maximum power point (MPP) in PV systems, a robust MPPT integrated with sliding mode control (SMC) is employed. This paper proposes a circle search algorithm based super twisting SMC (CSA-STSMC) for tracking the MPP for 100-kW of different commercial grid-connected PV modules coupled with an energy storage system that used to smooth their output fluctuations. Moreover, the STSMC and PI controllers' gains are optimized using the CSA and Grey Wolf Optimizer (GWO) methods and the performance of proposed method is demonstrated through comparisons with CSA based Proportional-Integral controller (CSA-PI), GWO-STSMC, and Incremental Conductance based PI controller (InC-PI) methods. The optimization and simulation analysis are executed in A MATLAB/Simulink environment. It is observed that the proposed method outperforms the considered methods in terms of robustness, tracking speed, best convergence to the minimum error's value and better tracking efficiency in the maximizing the extracted power from the introduced PV systems.INDEX TERMS Circle search algorithm, Grey wolf optimizer, Maximum power point tracking, Photovoltaic system, PI controller, Super twisting sliding mode controller.

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Section: Sliding Mode Control Strategymentioning

confidence: 99%

Circle Search Algorithm-Based Super Twisting Sliding Mode Control for MPPT of Different Commercial PV Modules

Ghazi,

Al-Ammar,

Hasanien

et al. 2024

IEEE Access

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“…In the online identification of the parameters of the permanent magnet synchronous motor, the adjustable model is expressed as follows [29][30]:…”

Section: Adaptive Parameter Identification Model Designmentioning

confidence: 99%

Sensorless Control of Permanent Magnet Synchronous Motor Based on Adaptive Back-EMF Observer

Ye¹,

Cao²,

Duan³

et al. 2022

Preprint

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Aiming at the problem that the change of motor parameters in actual operation will significantly reduce the control accuracy of the motor, this paper proposes a sensorless control method of permanent magnet synchronous motor based on an adaptive back-EMF observer. In the sensorless vector control algorithm, the mathematical model of the permanent magnet synchronous motor based on the coordinate axis transformation is established, the flux linkage of the permanent magnet synchronous motor is identified online, and the motor model is updated with the specified parameters, to realize the analysis of the rotor position and the permanent magnet synchronous motor. Accurate control of rotational speed. The back-EMF observer and the adaptive back-EMF observer are verified in the simulation environment. The simulation results show that the adaptive back-EMF observer can quickly converge to the actual value and track the motor parameters well. Enabling highly accurate rotor speed and position estimation. Finally, the permanent magnet synchronous motor control platform is built in the laboratory. Under the condition of the parameter change of the permanent magnet synchronous motor, the online identification of the parameters and the accurate control of the speed of the permanent magnet synchronous motor can be realized.

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“…Various estimation techniques for the rotor speed have been suggested in the literature in order to estimate the rotor speed in a closed loop utilizing the measured stator currents and voltages, like the full-order observer, 40 the extended Kalman filter, [41][42][43] the SMO, [44][45][46][47][48] the Luenberger observer 49,50 and the model reference adaptive system (MRAS). [51][52][53][54][55][56][57][58][59][60][61] Mostly, sensorless algorithms have proved their good performance at high-and mediumspeed ranges. However, there are lacks in terms of robustness and accuracy, especially at a very low speed and a locked rotor.…”

Section: Introductionmentioning

confidence: 99%

“…42 Among the suggested speed estimation methods, the MRAS speed estimator can be considered as the most commonly utilized method thanks to its simplicity of implementation. [51][52][53][54][55][56][57][58][59][60][61] The MRAS is based on two models and an adaptation mechanism. The first one is called the reference model which consists in estimating the rotor or the stator fluxes.…”

Section: Introductionmentioning

confidence: 99%

“…For the DTC method, the reference model-based stator flux can be more suitable in order to avoid the step of the RF estimation and reduce the observer complexity. However, the reference model is an open-loop stator flux estimator (OLSFE), as in Ammar, 59 which is sensitive to the stator resistance variations, especially at a low-speed operation, and it is based on a pure integration which does not work well in practice since the flux estimate will drift away from the origin. Pal et al 60 presented an improved RF-MRAS speed estimator utilizing a voltage reference vector model to calculate the RF vector in the reference model.…”

Section: Introductionmentioning

confidence: 99%

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Design and Xilinx Virtex-field-programmable gate array for hardware in the loop of sensorless second-order sliding mode control and model reference adaptive system–sliding mode observer for direct torque control of induction motor drive

Krim

Mimouni

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article aims first to propose an improved space vector modulation–direct torque control in order to enhance the induction motor performance using: (1) a super-twisting controller and (2) a novel model reference adaptive system based on a sliding mode observer. Second, due to the complexity of the suggested control algorithm, this article deals also with a hardware implementation on a field-programmable gate array board. Indeed, the field-programmable gate array is mainly chosen to reduce the execution time, thanks to its parallel processing which significantly improves the quality of the control system by reducing the sampling period, and consequently the delays in the control loop. Besides, the super-twisting controller is proposed to enhance the speed regulation loop which is a second-order sliding mode control technique that uses a continuous control law to prevent the chattering phenomenon induced by the first-order sliding mode control technique. Moreover, the high performance control requires information about the rotor speed which can be obtained by a sensor. Generally, the use of the speed sensor increases the system cost and size, and reduces its system reliability. Therefore, a combination between a model reference adaptive system observer and a sliding mode observer is suggested for speed estimation and overcoming the sensitivity of the classical model reference adaptive system observer against uncertainties and stator resistance variations. The performance of the proposed space vector modulation--direct torque control--super-twisting controller with the model reference adaptive system--sliding mode observer algorithm of an induction motor is verified through simulation, hardware co-simulation and experimental validation utilizing a field-programmable gate array Virtex-5-ML507 board. The performance of the suggested sensorless control method is compared also with other recent published schemes.

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Second-order sliding mode-direct torque control and load torque estimation for sensorless model reference adaptive system–based induction machine

Cited by 10 publications

References 34 publications

Circle Search Algorithm-Based Super Twisting Sliding Mode Control for MPPT of Different Commercial PV Modules

Circle Search Algorithm-Based Super Twisting Sliding Mode Control for MPPT of Different Commercial PV Modules

Sensorless Control of Permanent Magnet Synchronous Motor Based on Adaptive Back-EMF Observer

Design and Xilinx Virtex-field-programmable gate array for hardware in the loop of sensorless second-order sliding mode control and model reference adaptive system–sliding mode observer for direct torque control of induction motor drive

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