A simplified MRAS based sensorless vector control method of induction motor

Tsuji, Mineo; Umesaki, Y.; Nakayama, Ryoichi; Izumi, K.

doi:10.1109/pcc.2002.998124

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…The model reference adaptive system (MRAS) based method is a representative to construct the sensorless system [1] [2]. We proposed a current observer-based speed sensorless vector control system in a synchronously rotating reference frame [3]. Furthermore we developed the system by taking into account iron loss [4].…”

Section: Proposed Methodsmentioning

confidence: 99%

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Characteristics of MRAS based induction motor sensorless vector control system taking into account iron loss

Tsuji

Tsuruda

et al. 2009

2009 International Conference on Electrical Machines and Systems

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We have proposed a current observer-based speed sensorless vector control system taking into account both iron loss and stator resistance identification. The method is constructed in a synchronously rotating reference frame. The effectiveness of the proposed system has been investigated by digital simulation and experimentation. Furthermore important control parameters such as stator resistance, observer gain and filter of speed estimation are studied by comparing the computed result with experimental one.

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Section: Proposed Methodsmentioning

confidence: 99%

“…We have proposed a MRAS based sensorless scheme in a synchronously rotating reference frame [3] [4]. Fig.2 shows the proposed sensorless vector control system.…”

Section: Proposed Methodsmentioning

confidence: 99%

Characteristics of MRAS based induction motor sensorless vector control system taking into account iron loss

Tsuji

Tsuruda

et al. 2009

2009 International Conference on Electrical Machines and Systems

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“…Nowadays, a number of estimation techniques are available for speed and flux calculation. The standard speed estimators are Extended Kalman Filter (EKF) [1]- [6], Luenberger observer [7]- [9] and Model Reference Adaptive System (MRAS) [10]. The initial selection of noise covariance matrices is not easy in EKF and subsequently the algorithm is complicated.…”

Section: Introductionmentioning

confidence: 99%

Speed Sensorless Vector Control of Induction Motor Drive with PI and Fuzzy Controller

Gunabalan¹,

Subbiah

2015

IJPEDS

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This paper directed the speed-sensorless vector control of induction motor drive with PI and fuzzy controllers. Natural observer with fourth order state space model is employed to estimate the speed and rotor fluxes of the induction motor. The formation of the natural observer is similar to and as well as its attribute is identical to the induction motor. Load torque adaptation is provided to estimate the torque and rotor speed is estimated from the load torque, rotor fluxes and stator currents. There is no direct feedback in natural observer and also observer gain matrix is absent. Both the induction motor and the observer are characterized by state space model. Simple fuzzy logic controller and conventional PI controllers are used to control the speed of the induction motor in closed loop. MATLAB simulations are made with PI and fuzzy controllers and the performance of fuzzy controller is better than PI controller in view of torque ripples. The simulation results are obtained for various running conditions to exhibit the suitability of this method for sensorless vector control. Experimental results are provided for natual observer based sensorless vector control with conventional PI controller. Keyword:Fuzzy INTRODUCTIONInduction motors are preferred for most of the industry applications because of the limitations of commutation and rotor speed in DC drives. The induction motor is in fact 'brushless' and can operate with simple control methods not requiring a shaft position transducer. With no shaft position feedback, the motor remains stable only as long as the load torque does not exceed the breakdown torque. At low speeds it is possible for oscillatory instabilities to develop. To overcome these limitations 'field-oriented' or 'vector' control has been developed in which the phase and magnitude of the stator currents are regulated so as to maintain the optimum angle between stator mmf and rotor flux. This control is based on transforming a three phase time and frequency dependent system into a two co-ordinate (d and q axes) time invariant system. These projections lead to a structure similar to that of a separately excited DC motor control. Field orientation, however, requires either a shaft position encoder or an in-built control model whose parameters are specific to the motor.Generally, two types of field oriented control schemes are available. 1. Direct field oriented control 2. Indirect field oriented control. In the direct scheme, the instantaneous position of rotor flux (θ e ) has to be measured using flux sensors. This adds to the cost and complexity of the drive system. In the indirect scheme, a model of the induction motor is required to calculate the reference angular slip frequency that has to be added to the measured rotor speed. The sum is integrated to calculate the instantaneous position of the

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“…A survey of sensorless vector control of induction motor is described the conference of Speedam [3]. On the other hand, we have proposed the speed sensorless vector control systems in a synchronously rotating reference frame [4][5] [6]. In our methods, the variables are DC quantities and the linear state equations are obtained easily.…”

Section: Introductionmentioning

confidence: 99%

“…The effectiveness of the proposed system has been studied analytically and experimentally. Figure 1 shows the proposed speed sensorless vector control system [6]. In this system, the d-q axis is chosen such that the d axis is on the computed rotor flux vector and the iron loss is taken into account.…”

Section: Introductionmentioning

confidence: 99%

Characteristic improvement of a novel current observer based induction motor speed sensorless system

Tsuji

Tsuruda

et al. 2010

Speedam 2010

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We have proposed a current observer-based induction motor speed sensorless vector control system which is composed in a rotating reference frame. The characteristic improvement of this system is discussed by taking into account iron loss, stator resistance identification, current low-pass filter and speed estimation low-pass filter. The stability of the system has been investigated by the trajectories of poles and zeros. The effectiveness of the proposed system has been investigated by digital simulation and experimentation.

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A simplified MRAS based sensorless vector control method of induction motor

Cited by 10 publications

References 4 publications

Characteristics of MRAS based induction motor sensorless vector control system taking into account iron loss

Characteristics of MRAS based induction motor sensorless vector control system taking into account iron loss

Speed Sensorless Vector Control of Induction Motor Drive with PI and Fuzzy Controller

Characteristic improvement of a novel current observer based induction motor speed sensorless system

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