Normalized-Model Reference System for Parameter Estimation of Induction Motors

Véliz-Tejo, Adolfo; Travieso-Torres, Juan Carlos; Peters, Andrés A.; Mora, Andrés; Leiva-Silva, Felipe

doi:10.3390/en15134542

Cited by 8 publications

(8 citation statements)

References 34 publications

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“…The work [37] has recently improved some tuning issues for adaptive passivity-based control (APBC). Moreover [38], extends this result for parameters estimate using model reference adaptive systems (MRAS), while this paper develops it for MRAC.…”

Section: Introductionmentioning

confidence: 76%

“…The following MRAC ensures the output of the system (5) tends asymptotically to its desired value [34] (Sections 3.3.2 and 8.3.3) [37,38]:…”

Section: Basis Of Mrac For Nonlinear Systemsmentioning

confidence: 99%

“…3×3 is the adaptive law fixed-gain, according to [38]. Here, the control information vector is The generated power of PVP and the FC depends on their rated variables, cha istic curves, environmental conditions, and dynamic behavior.…”

Section: Basis Of Mrac For Nonlinear Systemsmentioning

confidence: 99%

“…These last only define positive controller gains adjusted via trial-and-error. Our proposal expands the adjustments proposed for MRAS in [37,38] for the proposed cascade MRAC. As a result, it decreases commissioning time, minimizing the trial-and-error adjustment.…”

mentioning

confidence: 97%

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Robust Cascade MRAC for a Hybrid Grid-Connected Renewable Energy System

Travieso-Torres,

Ricaldi-Morales,

Véliz-Tejo

et al. 2023

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Hybrid grid-connected renewable energy systems have gained significant importance in sustainably responding to an increased electrical energy demand. These are time-varying nonlinear dynamical plants, where the value of their parameters depends on changing weather conditions and the alternating grid voltage with randomly fluctuating amplitude. This paper proposes a robust cascade MRAC for nonlinear plants representing a class of these systems, which includes n renewable energy converts and a DC/AC single-phase full bridge inverter. The proposal reduces commissioning time by avoiding linearization and knowledge of the plant parameters. Moreover, it includes specific formulas for tuning the controller parameters that decrease their adjustments based on trial and error. Finally, it uses a direct adaptive method with adaptive laws having modification and an inner loop at least five times faster than the outer loop. The proposition validation includes the theoretical stability proof based on the Lyapunov stability method and Barbalat’s Lemma. Furthermore, it presents comparative simulation results with quoted cascade PI controllers for a monophasic system, including two renewable energy sources and injection. Both techniques effectively track setpoint changes of the energy sources’ currents and direct current bus voltage, showing the proposal similar or reduced ripple. At the same time, both ensure robustness against decreased photovoltage panels irradiance, increased fuel cells voltage, and grid voltage amplitude random fluctuations. However, the proposal does these things while avoiding prior linearization and unknowing the plant parameters.

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Section: Introductionmentioning

confidence: 76%

“…The following MRAC ensures the output of the system (5) tends asymptotically to its desired value [34] (Sections 3.3.2 and 8.3.3) [37,38]:…”

Section: Basis Of Mrac For Nonlinear Systemsmentioning

confidence: 99%

Section: Basis Of Mrac For Nonlinear Systemsmentioning

confidence: 99%

mentioning

confidence: 97%

See 2 more Smart Citations

Robust Cascade MRAC for a Hybrid Grid-Connected Renewable Energy System

Travieso-Torres,

Ricaldi-Morales,

Véliz-Tejo

et al. 2023

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“…parameters, which can be obtained from diverse methods [10], such as offline algorithms [11,12], offline tests [13,14], and self-commissioning tests [15,16].…”

Section: Of 20mentioning

confidence: 99%

Robust Combined Adaptive Passivity-Based Control for Induction Motors

Travieso-Torres,

Ricaldi-Morales,

Aguila-Camacho

2024

Preprint

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The need for industrial and commercial machinery to maintain high torque while accurately following a variable angular speed is increasing. To meet this demand, induction motors (IMs) are commonly used with variable speed drives (VSDs) that employ a field-oriented control (FOC) scheme. Over the last thirty years, IMs have been replacing independent connection direct current motors due to their cost-effectiveness, reduced maintenance needs, and increased efficiency. However, IMs and VSDs exhibit nonlinear behavior, uncertainties, and disturbances. This paper proposes a robust combined adaptive passivity-based control (CAPBC) for this class of nonlinear systems that applies to angular rotor speed and stator current regulation inside an FOC scheme for IMs´ VSDs. It uses general Lyapunov-based design energy functions and adaptive laws with σ-modification to assure robustness after combining control and monitoring variables. Lyapunov’s second method and Barbalat Lemma prove that the control and identification error tends to be zero over time. Moreover, comparative experimental results with standard Proportional Integer controller (PIC) and direct APBC, show the proposed CAPBC effectiveness and robustness under normal and changing conditions

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Speed estimation of the spindle motor of the grinding head for a high-speed precision roll grinder based on the extended Kalman filter

Cao

Zhao

et al. 2023

Australian Journal of Mechanical Engineering

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Normalized-Model Reference System for Parameter Estimation of Induction Motors

Cited by 8 publications

References 34 publications

Robust Cascade MRAC for a Hybrid Grid-Connected Renewable Energy System

Robust Cascade MRAC for a Hybrid Grid-Connected Renewable Energy System

Robust Combined Adaptive Passivity-Based Control for Induction Motors

Speed estimation of the spindle motor of the grinding head for a high-speed precision roll grinder based on the extended Kalman filter

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