Sensorless Speed Control of Synchronous Reluctance Motors Using Model Predictive control associated with Model Reference Adaptive System

Miloud, Gati; Serhoud, Hicham; Bekakra, Youcef

doi:10.1109/mi-sta52233.2021.9464507

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…On the other hand, DTC boasts of a simpler control structure, faster dynamic response, and robustness against parameter variations. Recent advancements have witnessed the integration of sensorless control techniques, primarily based on model reference adaptive systems [7], [8] and sliding mode observers [9], [10], to further enhance the efficiency and reliability of the SynRM control. With the advent of modern computational tools and wide band-gap semiconductor devices, it is also possible to implement more sophisticated control algorithms that can adapt to real-time changes and improve overall motor performance.…”

Section: Introductionmentioning

confidence: 99%

Enhanced State and Unknown Input Estimation for Synchronous Reluctance Motor Using Takagi-Sugeno Fuzzy Proportional Multi-Integral Observer

Hamdi,

Saadi,

Hammoudi

et al. 2024

IEEE Access

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This study introduces a novel approach to enhance state and unknown input estimation for the synchronous reluctance motor, utilizing the Takagi-Sugeno fuzzy representation. A proportional multiintegral observer structure is introduced, capable of capturing a wider range of unknown input dynamics compared to the previous proportional integral observer utilized in prior research. Stability conditions, obtained using the quadratic Lyapunov function, are formulated as linear matrix inequalities to guarantee the asymptotic convergence of the estimation error. To evaluate the effectiveness of the proposed observer, different cases were considered, including scenarios with slow and fast variations of the unknown input. Through hardware-in-the-loop validation, the results clearly demonstrate the superiority of the proposed method over the earlier approach. This advancement signifies a significant improvement in the estimation accuracy and reliability of the Synchronous Reluctance Motor.INDEX TERMS Hardware in the loop validation, synchronous reluctance motor, proportional multiintegral observer, Takagi-Sugeno fuzzy systems, unknown input observer.

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Section: Introductionmentioning

confidence: 99%

Enhanced State and Unknown Input Estimation for Synchronous Reluctance Motor Using Takagi-Sugeno Fuzzy Proportional Multi-Integral Observer

Hamdi,

Saadi,

Hammoudi

et al. 2024

IEEE Access

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“…The next group of methods is based on using a plant model while a closed-loop system is Information technology in management ISSN 2663-0176 (Print) ISSN 2663-7731 (Online) being designed [25,26]. Various methods of plant modeling can be used: state space equations [27], transfer functions [28,29], and so on.…”

Section: Introductionmentioning

confidence: 99%

Information support to solve direct dynamic problem for the previouslydisturbed electromechanicalsystems

Voliansky,

Sadovoi,

Tolochko

et al. 2024

HAIT

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The article is devoted to the creation of methodological foundations for solving a direct problem of dynamics for linear dynamic systems, the motion of which is described by ordinary differential equations with nonzero initial conditions. Consideration of the motions of linear dynamic systems allows to simplify the mathematical apparatus used and to solve motion determination problems by using a known approach based on transfer functions. However, due to the fact that the classical definition of transfer functions does not involve taking into account non-zero initial conditions, which are caused by the presence of initial deviations of the coordinates of the control object from their desired values, in our work we use the Laplace-Carson transformation to find the corresponding images and write the equations of motion in operator form. This approach, in contrast to the generally acceptedone, led to the introduction of information about the initial conditions of motion in the right-hand side of the corresponding operator differential equations and necessitated the generalization of the vector of control signals by including in it components that take into account the initial conditions of motion of the system under consideration. Such transformations made it possible to generalize the concept of a matrix transfer function as a matrix linear dynamic operator, which consists of two components that define disturbed free and controlled forced movements. The use of such an operator makes it possible to study the dynamics of the considered linear system both separately for each of the components of the generalized vector of controlling influences, and in the complex, thus solving the direct problem of the dynamics of linear systems.

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“…MPC have been widely used in various fields due to its good applicability and robustness, scholars from all over the world have produced a variety of MPC. According to the structural model, MPC can be roughly divided into non-parametric model prediction [2], [3], predictive control based on adaptive control theory [4]− [6], and predictive control based on structural design [7]− [10]. With the development of science, the increasingly complex controlled objects make it difficult for traditional MPC to accurately control nonlinear and timevarying uncertain system.…”

mentioning

confidence: 99%

A Model Predictive Control Algorithm Based on Biological Regulatory Mechanism and Operational Research

Yang,

Zhang,

Yildirim

et al. 2023

IEEE/CAA J. Autom. Sinica

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Sensorless Speed Control of Synchronous Reluctance Motors Using Model Predictive control associated with Model Reference Adaptive System

Cited by 8 publications

References 7 publications

Enhanced State and Unknown Input Estimation for Synchronous Reluctance Motor Using Takagi-Sugeno Fuzzy Proportional Multi-Integral Observer

Enhanced State and Unknown Input Estimation for Synchronous Reluctance Motor Using Takagi-Sugeno Fuzzy Proportional Multi-Integral Observer

Information support to solve direct dynamic problem for the previouslydisturbed electromechanicalsystems

A Model Predictive Control Algorithm Based on Biological Regulatory Mechanism and Operational Research

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