Maximum Torque per Ampere Control of Permanent Magnet Assisted Synchronous Reluctance Motor: An Experimental Study

Nadjai, Yasmine Ihcene; Ahmed, Hafiz; Takorabet, Noureddine; Haghgooei, Peyman

doi:10.31763/ijrcs.v1i4.451

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Theorem 1: Taking account for the PMASynRM drive system, which is depicted in (14), the suggested ICTCRLFNN will acquire absolute asymptotical stability once the parameters below are achieved: 1) The ICTCRLFNN control is created as depicted in (25); 2) The RLFNN's adaptation law is created as depicted in (34)-(38); 3) The compensators equipped with an adaption law are created as depicted in (39) and (40).…”

Section: B Design Of Ictcrlfnnmentioning

confidence: 99%

“…Furthermore, a machine learning approach was used to design the MTPA and flux-weakening control for an IPMSM in [13]. In addition, there are already some research for the MTPA controlled PMASynRM drive to improve its control characteristics in the constant torque region [14], [15]. However, the stator resistance and PM flux linkage are varying with motor temperature [15] and the d-q axis inductances are much influenced by the operating conditions of the motor drives.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor

et al. 2023

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The goal of this research is to develop an intelligent controlled permanent-magnet assisted synchronous reluctance motor (PMASynRM) drive system by utilizing an intelligent computed torque control with recurrent Legendre fuzzy neural network (ICTCRLFNN), in order to adjust the nonlinear and time-varying control specifications of the motor. The team first proposes an ANSYS Maxwell-2D dynamic model that contains a maximum torque per ampere (MTPA) control PMASynRM drive. A lookup table (LUT) is composed of the finite element analysis (FEA) results, which bring about the current angle of command within the MTPA. Subsequently, the team designs a computed torque control (CTC) system to control the speed reference command. Creating a working CTC for practical applications is quite complex because the detailed system dynamics, which includes the unpredictability of the PMASynRM drive system, is not available beforehand. Thus, this study suggests that a recurrent Legendre fuzzy neural network (RLFNN) can act as a close substitute for the CTC to resolve its existing complications. Furthermore, the team modifies an adaptive compensator to proactively adjust for the potential calculated deviance of the RLFNN. Asymptotical stability is assured by using the Lyapunov stability method, which generates the RLFNN's online learning algorithms. This study concludes that certain experimental results verify the effective and robust qualities of the suggested ICTCRLFNN controlled PMASynRM drive.INDEX TERMS Permanent-magnet assisted synchronous reluctance motor (PMASynRM), computed torque control (CTC), intelligent computed torque control using recurrent Legendre fuzzy neural network (ICTCRLFNN), maximum torque per ampere (MTPA).

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Section: B Design Of Ictcrlfnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor

et al. 2023

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“…As a result, there are as many control strategies as there are criteria. The vector control law comprises a loop for speed regulation in addition to two current loops, one for each axis [26], [27].…”

Section: State-space Mathematical Model Of Synrmmentioning

confidence: 99%

Synchronous Reluctance Motor Performance Improvement Using MTPA Control Strategy and Five-Level Inverter Topology

Zahraoui

Moutchou

Tayane

et al. 2022

Journal of Robotics and Control

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An improved vector control method is presented in this study to enhance synchronous reluctance motor (SynRM) performance. The maximum torque per ampere (MTPA) technique has demonstrated good dynamic properties since the torque control is closely tied to the current control. The selection of the control approach is primarily influenced by how the reference current values will be defined. Additionally, a five-level neutral-point-clamped (NPC) inverter replaces the traditional two-level inverter. Only eight voltage vectors can be produced by a two-level inverter, whereas one hundred twenty-five voltage vectors can be generated by a five-level inverter. The goal is to produce an output voltage vector that closely resembles the reference voltage vector in order to ensure a quick response on the one hand and enhance dynamic performance on the other. An exact comparison of the suggested vector control strategy's properties is made once it has been simulated in MATLAB/Simulink. The acquired findings are satisfactory and high performance is attained in terms of response time, torque ripple reduction, and current waveform improvement.

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An Online Parameter Estimation Using Current Injection with Intelligent Current-Loop Control for IPMSM Drives

et al. 2021

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An online parameter estimation methodology using the d-axis current injection, which can estimate the distorted voltage of the current-controlled voltage source inverter (CCVSI), the varying dq-axis inductances, and the rotor flux, is proposed in this study for interior permanent magnet synchronous motor (IPMSM) drives in the constant torque region. First, a d-axis current injection-based parameter estimation methodology considering the nonlinearity of a CCVSI is proposed. Then, during current injection, a simple linear model is developed to model the cross- and self-saturation of the dq-axis inductances. Since the d-axis unsaturated inductance is difficult to obtain by merely using the recursive least square (RLS) method, a novel tuning method for the d-axis unsaturated inductance is proposed by using the theory of the maximum torque per ampere (MTPA) with the combination of the RLS method. Moreover, to improve the bandwidth of the current loop, an intelligent proportional-integral-derivative (PID) neural network controller with improved online learning algorithm is adopted to replace the traditional PI controller. The estimated the dq-axis inductances and the rotor flux are adopted in the decoupled control of the current loops. Finally, the experimental results at various operating conditions of the IPMSM in the constant torque region are given.

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Maximum Torque per Ampere Control of Permanent Magnet Assisted Synchronous Reluctance Motor: An Experimental Study

Cited by 3 publications

References 21 publications

Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor

Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor

Synchronous Reluctance Motor Performance Improvement Using MTPA Control Strategy and Five-Level Inverter Topology

An Online Parameter Estimation Using Current Injection with Intelligent Current-Loop Control for IPMSM Drives

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