LPV Induction Motor Control with MRAS Speed Estimation

Khamari, D.; Benlaloui, I.; Ouchen, Sabir; Makouf, A.; Drid, Saïd; Chrifi‐Alaoui, Larbi; Ouriagli, M.

doi:10.1109/icsc47195.2019.8950563

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…This control law (23) does not involve the mechanical speed Ω. Through the simplification of coupling terms (18), the perturbation of φ r by the term (ω s − pΩ) no longer exists. The regulation of φ 2 r by the approach (23) is then totally independent of the operating point in the torque-speed plant.…”

Section: Slow Sub Systemmentioning

confidence: 99%

“…The temporal evolution of φ rd is directly linked to the variation of the operating point of the slow subsystem defined by the torque amplitude (C m ). In this case, the dynamic controllers based on the LPV system theory taking into account the variation of the rotor flux, rotor speed and rotor resistance, respectively, for torque control and flux estimation [16][17][18] is an alternative to robust control with fixed controller parameters in induction motor control [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Linear parameter varying sensorless torque control for singularly perturbed induction motor with torque and flux observers

Khamari¹,

Benlaloui²,

Ouchen³

et al. 2020

Electr Eng

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In this paper, a new approach being different from the concept of DTC and IFOC for a robust torque control design for induction motor is addressed. The design is based on the framework of singularly perturbed system theory and linear varying parameter systems. In these systems, the rotor flux is considered to be a time-varying parameter in order to guarantee a robust torque control with LPV flux observer with respect to the speed and resistance variations. In fact, this observer is designed to estimate the rotor flux as well as an MRAS observer is introduced to estimate the mechanical speed and rotor resistance. The main feature of this proposed structure is the enhancement of robustness with flux, speed and rotor resistance variation. This improvement leads to a considerable decrease of the torque ripples and ensures the stability for the entire operating range. The obtained simulations and experimental results are used to validate the effectiveness of the proposed control strategy. Keywords H ∞ LPV controller • Singularly perturbed system • LMI • MRAS observer • Induction motor Abbreviations Rotor speed (rd/s) ω s Stator current frequency (rd/s) ωr Induced rotor current frequency (rd/s) ωc Injected rotor current frequency (rd/s) J In Inertia f Coefficient of viscous C m Maximal electromagnetic torque ∼ Symbol indicating measured valuê Symbol indicating the estimated value B Sabir Ouchen

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Section: Slow Sub Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Linear parameter varying sensorless torque control for singularly perturbed induction motor with torque and flux observers

Khamari¹,

Benlaloui²,

Ouchen³

et al. 2020

Electr Eng

View full text Add to dashboard Cite

show abstract

“…A quasi-LPV strategy is adopted for induction machine control in [17]. An amalgam of model reference adaptive system (MRAS) observer with robust LPV scheme is implemented to address only the rotor parameters uncertainity [18]. The variation in the stator variables of induction motor based LPV system is catered through an interval state observer in [19].…”

Section: Introductionmentioning

confidence: 99%

Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms

et al. 2021

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Automotive applications often experience conflicting-objective optimization problems focusing on performance parameters that are catered through precisely developed cost functions. Two such conflicting objectives which substantially affect the working of traction machine drive are maximizing its speed performance and minimizing its energy consumption. In case of an electric vehicle (EV) powertrain, drive energy is bounded by battery dynamics (charging and capacity) which depend on the consumption of drive voltage and current caused by driving cycle schedules, traffic state, EV loading, and drive temperature. In other words, battery consumption of an EV depends upon its drive energy consumption. A conventional control technique improves the speed performance of EV at the cost of its drive energy consumption. However, the proposed optimized energy control (OEC) scheme optimizes this energy consumption by using robust linear parameter varying (LPV) control tuned by genetic algorithms which significantly improves the EV powertrain performance. The analysis of OEC scheme is conducted on the developed vehicle simulator through MATLAB/Simulink based simulations as well as on an induction machine drive platform. The accuracy of the proposed OEC is quantitatively assessed to be 99.3% regarding speed performance which is elaborated by the drive speed, voltage, and current results against standard driving cycles.

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“…A combination of MRAS observer with LPV control has been presented in Ref. [16] but it considers only the rotor parameters variation. An LPVO catering only rotor resistance and load torque uncertainties has been given in Ref.…”

Section: Introductionmentioning

confidence: 99%

Thermally degraded speed estimation of traction machine drive in electric vehicle

Ali

Hossain

Wang

et al. 2021

IET Electric Power Appl

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The speed of an induction machine drive (IMD) in the electrified powertrain of an electric vehicle (EV) suffers from thermal degradation caused by EV loading, driving cycle schedules, EV operating conditions, traffic state and temperature. It is necessary to estimate this thermal degradation in order to design appropriate control methodologies to address this significant issue that directly affects the EV performance. This study proposes a robust linear parameter varying (LPV) observer to estimate this degradation in IMD as well as EV speed under various thermal and loading conditions in steady state and during large transients. The stability and robustness of LPV methodology is ensured by optimal gains of H ∞ control and linear matrix inequalities using convex optimisation techniques. The weighting functions in LPV design are optimised by genetic algorithms. The proposed observer performance is compared with that of conventional sensorless field-oriented control and sliding mode observer. An improved speed performance during EV operation is also presented to validate the robustness of the proposed LPV observer against New European Driving Cycle. The performance analysis is conducted through NI myRIO 1900 controller-based electrical drive set-up. K E Y W O R D S electric vehicle, genetic algorithms, induction machine drive, linear parameter varying control, thermally degraded speedThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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LPV Induction Motor Control with MRAS Speed Estimation

Cited by 5 publications

References 11 publications

Linear parameter varying sensorless torque control for singularly perturbed induction motor with torque and flux observers

Linear parameter varying sensorless torque control for singularly perturbed induction motor with torque and flux observers

Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms

Thermally degraded speed estimation of traction machine drive in electric vehicle

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