MRAC based online stator resistance and rotor time constant estimation scheme in sensorless field oriented controlled induction motor drives

Morey, Meghraj; Virulkar, Vasudeo B.; Dhomane, G. A.

doi:10.1109/iceeot.2016.7755340

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…Hence, the development of speed estimation FVC IM drives in place of conventional FVC IM drives (rotor position sensor types) is required. In the literature, speed estimation methods for FVC IM drives have been presented: speed identification by an adaptive control system, (2)(3)(4) speed estimation by the application of a neural network or fuzzy logic control approach, (5,6) speed adjustment by flux estimation, (7)(8)(9)(10) and speed determination from an extended Kalman filter. (11)(12)(13) However, an adaptive control system easily traps a chattering effect with large control variables; a neural network or fuzzy logic control approach requires trial-and-error training procedures, iterative computations, a large amount of training data, network parameter assignment, and fuzzy rules; flux estimation requires the construction of an accurate plant model; and an extended Kalman filter requires a large amount of computation and memory.…”

Section: Introductionmentioning

confidence: 99%

Speed Estimation of Adaptive Stator-flux-vector-controlled Induction Motor Drive Based on Particle Swarm Optimization Algorithm

Luo¹,

Pei²,

Kuo³

et al. 2020

Sensors and Materials

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An adaptive synchronous speed estimation scheme is proposed for the speed estimation of a stator-flux-vector-controlled (SFVC) induction motor (IM) drive. An SFVC IM drive was established according to the current and flux of the stator, and the stator current was obtained from an IM by using Hall effect current sensors. A model reference adaptive system (MRAS) was utilized to design the synchronous speed identification scheme based on the reactive power, and the estimated rotor speed was obtained by subtracting the slip speed from the estimated synchronous speed. The adaptation mechanism of the MRAS was designed using the particle swarm optimization (PSO) algorithm. The available operation speed was extended to the constant-power mode by applying the field-weakening technique. The MATLAB\Simulink ® toolbox was used to simulate this system, and all the control algorithms were realized using a Texas Instruments 6713-and-F2812 DSP card to generate pulse-width modulation signals for the power stage to actuate the IM. Both the simulation and experiment results (including the estimated rotor speed, stator current, electromagnetic torque, and stator flux locus) confirm the effectiveness of the proposed system and validate the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Speed Estimation of Adaptive Stator-flux-vector-controlled Induction Motor Drive Based on Particle Swarm Optimization Algorithm

Luo¹,

Pei²,

Kuo³

et al. 2020

Sensors and Materials

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show abstract

“…Hence, the development of speed-identification schemes to replace the shaft position sensor is required. Several speedidentification schemes of FVC IMs have been published, including speed adjustment through an extended Kalman filter, (6)(7)(8) speed estimation derived from an adaptive control system scheme, (9,10) speed determination according to a flux estimator or the electromotive force of an IM, (11)(12)(13) and speed estimation from a neural network or fuzzy logic control. (14,15) In this study, model reference adaptive control (MRAC) was utilized to develop a synchronous speedidentification scheme based on the reactive power of an IM, and the estimated rotor speed was obtained by subtracting the slip speed from the estimated synchronous speed.…”

Section: Introductionmentioning

confidence: 99%

“…An inspection of Eq (9). shows that the second and third terms on the left side are the coupling components in relation to the q-axis stator current and d-axis air-gap flux, respectively.…”

mentioning

confidence: 99%

Adaptive Speed Identification Air-gap Flux Vector-controlled Induction Motor Drive Based on Firefly Algorithm

Yung-Chang¹,

Lin²,

Pu³

2021

Sensors and Materials

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An adaptive speed-identification scheme based on the firefly algorithm (FA) is presented for an air-gap flux vector-controlled (AGFVC) induction motor (IM) drive. The AGFVC IM drive was established using a stator current and air-gap flux. The stator current was acquired from an IM using a Hall effect sensing element. The Hall effect element was employed as an electromagnetic sensor to detect the stator current while the AGFVC IM was rotating under different reversible steady-state speed commands. Model reference adaptive control (MRAC) was utilized to develop a synchronous speed-identification scheme based on the reactive power of the motor, and the rotor speed was estimated by subtracting the slip speed from the estimated synchronous speed. The MRAC adaptation mechanism was designed using the FA. The MATLAB/Simulink © toolbox was used to simulate the proposed AGFVC IM drive system, and all the control algorithms were implemented using a TI DSP 6712-and-F2812 control card to generate pulse width modulation signals through the power stage to actuate the motor. Both simulation and experimental results verified the effectiveness of the proposed system.

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“…The FOC has some control deficiencies such as the need for performing co-ordinates transformation between different reference frames [8][9][10], specifically between stationary and synchronous rotating frames, which consequently makes the system more complicated. In addition, both control methods; FOC and DTC utilize the flux as one of the main two control variables, which is estimated and consequently can be influenced by parameters variation, especially the stator resistance [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…A lot of studies have been developed to eliminate the effect of parameters variation on the performance of FOC and DTC techniques [14][15], the main idea of these studies is dedicated for performing an on-line estimation for the parameters, which consequently increases the complexity of the control systems adds to the co-ordinate transformation computing effort.…”

Section: Introductionmentioning

confidence: 99%

Model predictive instantaneous power control for a sensorless induction motor drive

Mossa

Bolognani

2017

2017 Nineteenth International Middle East Power Systems Conference (MEPCON)

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This paper presents an innovative model predictive instantaneous power control (MP IPC) technique for an induction motor (IM) drive. The proposed control approach is designed as a preference control solution to the classic techniques such as field oriented control (FOC) and direct torque control (DTC). The proposed MP IPC has a simple implementation procedure, in addition to the absence of the PI current controllers and pulse width modulation (PWM), which are usually used in such classic techniques. An effective sensorless approach based on model reference adaptive system (MRAS) observer is used for the purpose of speed estimation, which depends on regulating the error between the measured and estimated values of instantaneous reactive power. A dSpace 1104 control board is used to implement experimentally the control procedure. Obtained results affirm the feasibility of proposed method in achieving decoupled control of the instantaneous active and reactive powers of IM drive, and consequently decoupled control of torque and flux values is achieved.

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MRAC based online stator resistance and rotor time constant estimation scheme in sensorless field oriented controlled induction motor drives

Cited by 5 publications

References 12 publications

Speed Estimation of Adaptive Stator-flux-vector-controlled Induction Motor Drive Based on Particle Swarm Optimization Algorithm

Speed Estimation of Adaptive Stator-flux-vector-controlled Induction Motor Drive Based on Particle Swarm Optimization Algorithm

Adaptive Speed Identification Air-gap Flux Vector-controlled Induction Motor Drive Based on Firefly Algorithm

Model predictive instantaneous power control for a sensorless induction motor drive

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