A comparison of speed-sensorless induction motor control with torque compensation

Gastaldini, Cristiane Cauduro; Azzolin, Rodrigo Zelir; Gründling, Hilton Abílio

doi:10.1109/iemdc.2009.5075398

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…The estimated speed is produced by the proportional integral adaptation mechanism error of both models, and an MRAS system can be drawn as in Fig. 3 This algorithm is customary for speed estimation and simple to implement in fixed point DSP, such as in (Gastaldini & Grundling, 2009;Orlowska-Kowalska & Dybkowski, 2010;. The SVF blocks are state variable filters and are explained in greater detail in Section 7.…”

Section: Speed Estimation -Mras Algorithmmentioning

confidence: 99%

Electric Machines and Drives

Filizadeh¹

2013

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Miroslav Chomat attained his M.Sc. degree in Electrical Power Engineering from the Czech Technical University in Prague in 1988 and his Ph.D. degree in Electrical Engineering from the Institute of Electrical Engineering of the Academy of Sciences of the Czech Republic in 1994. He is currently a senior researcher and the head of the Department of Electrical Machines, Drives, and Power Electronics at the Institute of Thermomechanics AS CR, v.v.i. He is also an assistant professor at the Department of Electric Drives and Traction of the Czech Technical University in Prague. Between 1999 and 2000, he spent one year at the University of Wisconsin-Madison, USA working as a honorary research fellow. His research interests include numerical modeling and control of electrical machines, drives, power units and electronic converters.

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Section: Speed Estimation -Mras Algorithmmentioning

confidence: 99%

Electric Machines and Drives

Filizadeh¹

2013

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“…This control strategy also uses PI controllers for the current loop. IM speed sensorless feedback linearization control based on a current/flux and speed tracking controller is proposed in [16]. Using a cascade control structure, the feedback linearization controller is designed to control the inner current loop and, respectively, the speed and flux in the outer loop.…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Improved Vector Control Using Predictive and Model-Free Algorithms Together with Homotopy-Based Feedback Linearization

Costin,

Lazar

2024

Energies

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Vector control of an induction machine (IM) is typically performed by using cascade control structures with conventional linear proportional–integral (PI) controllers, the inner loop being designed for current control and the outer loop for rotor flux and speed control. In this paper, starting with the dq model of the IM, advanced control algorithms are proposed for the two control loops of the cascade structure. For the current inner loop, after the decoupling of the two dq currents, predictive control algorithms are employed to independently control the currents, considering the constraints imposed by the electrical signal physics limitations. Since the outer loop has a nonlinear affine multivariable plant model, a homotopy-based variant of feedback linearization is used to obtain a nonsingular decoupling matrix of the feedback transformation even when the rotor flux is zero at the start-up of the motor. During the continuous variation in the homotopy parameter, the plant model is variable and, for this reason, model-free algorithms are used to control the flux and speed of the IM due to their capabilities to manage complex dynamics from data without requiring knowledge of the plant model. The performances of the proposed cascade control strategy with advanced algorithms in the two loops were tested by simulation and compared with those obtained with conventional PI controllers, resulting in better dynamic behavior for predictive and model-free control.

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“…The estimated speed is produced by the proportional integral adaptation mechanism error of both models, and an MRAS system can be drawn as in Fig.3 This algorithm is customary for speed estimation and simple to implement in fixed point DSP, such as in (Gastaldini & Grundling, 2009;Orlowska-Kowalska & Dybkowski, 2010;Vieira et al, 2009). The SVF blocks are state variable filters and are explained in greater detail in Section 7.…”

Section: Wwwintechopencommentioning

confidence: 99%

“…FLC provides rotor speed regulation, rotor flux amplitude decoupling and torque compensation. Although the strategy presented by (Marino et al, 1990) was not a sensorless control strategy, fundamental principles of FLC follow servomechanism design of sensorless control strategies, such as (Gastaldini & Grundling, 2009;Marino et al, 2004;Montanari et al, 2007;. The purpose of this chapter is to present the development of two FLC control strategies in the presence of torque disturbance or load variation, especially under low rotor speed conditions.…”

Section: Introductionmentioning

confidence: 99%