Advanced speed sensorless control strategy for induction machine based on neuro-MRAS observer

Merrassi, Weam El; Abounada, Abdelouahed; Ramzi, Mohamed

doi:10.1016/j.matpr.2021.03.081

Cited by 13 publications

(10 citation statements)

References 33 publications

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“…e sensorless scheme based on EEMF can reverse motor speed without any position sensor. In addition to the SMO, the other observers such as Luenberger observer (LO) [35], extended Kalman filter (EKF) [36][37][38], and model reference adaptive system (MRAS) [39] are also employed for position estimation. In [39], a speed observer that combines artificial intelligence and MRAS is proposed, which is associated with the control scheme as sensorless algorithms for rotor speed and flux estimation under lowspeed regions for induction motors.…”

Section: Related Researchmentioning

confidence: 99%

Speed Sensorless Control Based on Initial Rotor Position Detection for EESM

Cai

Sun

2022

Mathematical Problems in Engineering

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Electrically excited synchronous motor (EESM) is widely used in many large equipment drives because of its strong overload capacity, high efficiency, and adjustable power factor. The research and development of a high-performance EESM control system can realize the high combination of energy-saving speed regulation and green environmental protection and has a high social effect and economic value. In this paper, the signal injection method is used to obtain the initial rotor position information of EESM. Sliding Fourier transform is used to improve the initial position angle detection method based on the rotor signal injection method, and the improved method is compared with the traditional voltage integration method. Rotor high-frequency signal injection method was used to detect the rotor position information of the motor during operation, and the influence of the damping winding on the rotor signal injection method was analyzed. On the premise that the damping winding had no influence on the method, a method of obtaining the rotor position information of EESM without a speed sensor was designed. Finally, the speed sensorless regulation system using the initial rotor position detection method is simulated, which verifies the accuracy of the proposed speed sensorless control scheme.

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Section: Related Researchmentioning

confidence: 99%

Speed Sensorless Control Based on Initial Rotor Position Detection for EESM

Cai

Sun

2022

Mathematical Problems in Engineering

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“…Khan and Verma [21] have implemented MRAS for speed estimation of direct controlled switched reluctance motor. Merrassi et al [22] have combined MRAS and neural network to provide an observer for estimating the speed of three phase induction motor.…”

Section: Introductionmentioning

confidence: 99%

Low cost high performance self-starting sensorless single phase induction motor drive

Anagreh

Al-Ibbini

2023

IJPEDS

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<p><span lang="EN-US">The present paper is directed to achieve a low-cost high-performance self-starting single phase induction motor (SPIM) drive system. A phase shifted pulse width modulation (PWM) trains feeding the motor will replace the starting and running capacitors. Adaptive sliding mode control, enhance with model reference adaptive control (MRAC), is implemented to achieve high performance sensorless SPIM drive. The obtained results confirm the feasibility of the proposed system in starting and fast tracking the reference speed with nearly zero percentage overshoot and zero steady-state error. Moreover, the proposed SPIM drive system is robust to external load torque disturbances and insensitive to system parameter variations. Extensive simulations have been conducted to confirm the validity of the proposed system.</span></p>

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“…In recent several decades, NN-based techniques have gained great attention. The NNs are used as observers in [27], [28], adaptive NN is used to approximate unknown uncertainties in the system's dynamics [7], [21], [29], [30], and a variety of different types of nonlinear systems have been explored by using NNs-based adaptive backstepping techniques to deal with unknown nonlinearities [31]- [35].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

Guo

Liu

2023

IEEE Access

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An adaptive neural network command-filtered backstepping design algorithm is proposed for the underactuated translational oscillator with a rotating actuator (TORA). The system dynamics are transformed into a nonlinear cascade system through a global change of coordinates. Considering the weak sinusoid-type nonlinear interaction and affine-free appearance in the cascade model, the TORA is looked at as a pure feedback system. Two neural networks are used to approximate the unknown functions and a command filter is used to produce the virtual control and its first and second-order derivatives to overcome the explosion of complexity problems in backstepping. A filter error compensation dynamic system is designed to overcome the error influence on control performance. Taking full account of the cascade model structure of the underactuated TORA, each backstepping step is designed for a second-order subsystem to reduce the design steps and simplify the design process. The system stability is proved through the Lyapunov stability theorem and verified by numerical simulations.

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Advanced speed sensorless control strategy for induction machine based on neuro-MRAS observer

Cited by 13 publications

References 33 publications

Speed Sensorless Control Based on Initial Rotor Position Detection for EESM

Speed Sensorless Control Based on Initial Rotor Position Detection for EESM

Low cost high performance self-starting sensorless single phase induction motor drive

Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

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