Real-Time Implementation of Bi Input-Extended Kalman Filter-Based Estimator for Speed-Sensorless Control of Induction Motors

Barut, Murat; Demir, Rıdvan; Zerdali, Emrah; İnan, Remzi

doi:10.1109/tie.2011.2178209

Cited by 131 publications

(151 citation statements)

References 25 publications

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“…• the estimations demonstrated in this study are in harmony with those presented in [18]. However, differently from [18], this study also presentsγ T .…”

Section: Real-time Experimental Resultssupporting

confidence: 86%

“…In our simulations, the proposed estimation algorithm and the speed-sensorless drives shown in Figures 2 and 3 were tested with the rated parameters in the Table. The values in the Table are the same as in [14,17,18], so that a reasonable comparison between the results in this study and those in [14,17,18] could be made.…”

Section: Simulation Resultsmentioning

confidence: 96%

“…For this aim, the extended models in discrete form, proposed in this study as Model 1 [14,15,17,18,20] and Model 2 for the R r and γ T estimations, can be given (as referred to the stator stationary frame) in the following general form:…”

Section: Development Of Extended Im Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Novel version of bi input-extended Kalman filter for speed-sensorless control of induction motors with estimations of rotor and stator resistances, load torque, and inertia

Zerdali¹,

Barut²

2016

Turk J Elec Eng & Comp Sci

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This study aims to develop a novel version of bi input-extended Kalman filter (BI-EKF)-based estimationtechnique in order to increase the number of state and parameter estimations required for speed-sensorless direct vector control (DVC) systems, which perform velocity and position controls of induction motors (IMs). For this purpose, all states required for the speed-sensorless DVC systems, besides the stator resistance Rs , the rotor resistance Rr , the load torque tL including the viscous friction term, and the reciprocal of total inertia 1/jT , are simultaneously estimated by the novel BI-EKF algorithm using the measured phase currents and voltages. The effectiveness of the proposed speed-sensorless DVC systems is tested by simulations under the challenging variations of Rs , Rr , tL , jT , and velocity/position reference. Later, the state and parameter estimations of the novel BI-EKF algorithm are confirmed with real-time experiments in a wide speed range. Finally, in both transient and steady states, a satisfactory estimation and control performance that make this study unique are achieved.

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“…• the estimations demonstrated in this study are in harmony with those presented in [18]. However, differently from [18], this study also presentsγ T .…”

Section: Real-time Experimental Resultssupporting

confidence: 86%

Section: Simulation Resultsmentioning

confidence: 96%

See 1 more Smart Citation

Novel version of bi input-extended Kalman filter for speed-sensorless control of induction motors with estimations of rotor and stator resistances, load torque, and inertia

Zerdali¹,

Barut²

2016

Turk J Elec Eng & Comp Sci

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“…The extended IM model used for t L and R s estimation. The matrices associated with this model [17,[19][20][21], such as, x e1 , A e1 , B e1 and u e are as follows:…”

Section: Extended Mathematical Models Of the Immentioning

confidence: 99%

“…However, both switching and braided EKF require 2 separate EKF algorithms; this is not desired from the real-time implementation point of view since the cost of hardware platforms such as microprocessors, digital signal processors, and field programmable gate arrays increase with the increasing memory size/area required for embedding the software algorithm. A significant step forward in this sense is the method in [20,21], where the introduced bi input-EKF (BI-EKF) technique merges the consecutively operated 2 separate EKF algorithms into a single EKF algorithm by successively switching its input/terms associated with the 2 IM models; thus, the BI-EKF technique considerably reduces the memory requirement of both switching and braided EKF. Moreover, a novel version of the BI-EKF technique was recently introduced in [22], which estimates the total inertia, j T , together with i sα , i sβ , φ rα , φ rβ , ω m , R s , R r , and t L by using the measured stator phase currents and voltages; therefore, it increases the number of estimated states and parameters compared to [20,21].…”

Section: Introductionmentioning

confidence: 99%

Bi input-extended Kalman filter-based speed-sensorless control of an induction machine~capable of working in the field-weakening region

İnan¹,

Barut²

2014

Turk J Elec Eng & Comp Sci

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This study introduces a novel bi input-extended Kalman filter (BI-EKF)-based speed-sensorless direct vector control (DVC) of an induction motor (IM). The proposed BI-EKF-based estimator includes online estimations of the stator stationary axis components of the stator currents, isα and i sβ ; stator stationary axis components of the rotor flux, φrα and φ rβ ; rotor angular velocity, ωm ; stator resistance, Rs ; rotor resistance, Rr ; and load torque tL , as well as the magnetizing inductance, Lm , by only supposing that the stator phase currents and voltages are measured. Thus, the speed-sensorless DVC of the IM with the inclusion of the proposed estimator is able to be perfectly operated at a wide speed range, varying from zero speed to beyond the rated/based speed under the extreme variations in Rs , Rr , tL , and Lm . The simulations confirm the effectiveness of the proposed BI-EKF-based estimator and, consequently, the speed-sensorless DVC of the IM.

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Real‐time implementation of sensorless indirect field‐oriented control of three‐phase induction motor using a Kalman smoothing‐based observer

Syamkumar

Jayanand

2019

Int Trans Electr Energ Syst

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Summary This paper proposes an indirect vector control scheme of an induction motor, using a Kalman smoothing‐based observer to estimate speed. The observer is used to estimate the stator currents, rotor currents, and rotor mechanical speed. Observers based on conventional extended Kalman filters (CEKFs) depend on past output measurements of a system to predict its state variables at the next instant. In a smoothing‐based observer, some future output measurements are also used to obtain a smoothed estimate of a past instant. The smoothed estimate, thus obtained, is used to predict and correct the states of the next instant. The performance of a CEKF‐based observer largely depends on the proper determination of its measurement and process error covariance matrices. A trial and error method is usually engaged to arrive at these matrices. Smoothing helps to obtain a better state estimate compared with CEKF, with the same covariance matrices used in CEKF, which is obtained by trial and error. The improvement in estimation is mainly in the transient region. Estimates of low and zero speeds also show good improvement over those of CEKF. This betterment accomplished is without much increase in computational load. Experiments are conducted to compare the performance of a speed sensorless indirect vector control system with the observer, based on CEKF and smoothing for the same values of noise covariance matrices. The experiments conducted used various reference speeds, including low and zero speeds. Results show the superiority of the smoothing‐based Kalman observer over CEKF‐based observers.

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Real-Time Implementation of Bi Input-Extended Kalman Filter-Based Estimator for Speed-Sensorless Control of Induction Motors

Cited by 131 publications

References 25 publications

Novel version of bi input-extended Kalman filter for speed-sensorless control of induction motors with estimations of rotor and stator resistances, load torque, and inertia

Novel version of bi input-extended Kalman filter for speed-sensorless control of induction motors with estimations of rotor and stator resistances, load torque, and inertia

Bi input-extended Kalman filter-based speed-sensorless control of an induction machine~capable of working in the field-weakening region

Real‐time implementation of sensorless indirect field‐oriented control of three‐phase induction motor using a Kalman smoothing‐based observer

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