Speed Control of Series-Connected Five-Phase Two PMSM using Sliding Mode Control

Mehedi, Fayçal; Nezli, L.; Mahmoudi, M.O.

doi:10.1109/cistem.2018.8613341

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…This transformation allows for the determination of motor parameter values over time. In field-oriented control, measurements are taken in the rotor reference frame, replacing the 3-phase frame with the 2-axis rotating d-q rotor frame to transfer observed stator currents[13] [14]. The α-β to d-q transformation, also known as the Park transformation, is then applied.…”

Section: Proposed Methodsmentioning

confidence: 99%

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

sime,

Aluvada,

Habtamu

et al. 2024

Preprint

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This study presents a novel approach to enhance the speed control performance of Permanent Magnet Synchronous Motor (PMSM) drives in Electric Vehicles (EVs) through the implementation of a Genetic Algorithm (GA)-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFOPID) controller. PMSM technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in EV drive systems. However, the inherent non-linearity, dynamics, and uncertainties of PMSMs pose significant control challenges. The proposed GA-AFFOPID controller, tuned using a genetic algorithm, exhibits superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances. Comparative analysis with traditional control methods demonstrates the exceptional performance of the GA-AFFOPID controller, achieving a 1.796% lower overshoot, 0.97% faster rise time, 4.25% lower steady-state error, and 0.35% faster settling time compared to the adaptive fuzzy fractional order PID controller. These results highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications, paving the way for improved energy efficiency and overall performance of electric vehicle propulsion systems.

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Section: Proposed Methodsmentioning

confidence: 99%

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

sime,

Aluvada,

Habtamu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Listwan and Pieńkowski [23] proposed a new switching table for classical DTC of the five-phase IPMSM drives; this proposed switching table is given in Table 1. On the other hand, the modeling of a 5-phase IPMSM is expressed in a rotary frame d-q-x-y as [25,26]:…”

Section: Introductionmentioning

confidence: 99%

Direct Torque Fuzzy Controlled Drive for Multi-phase IPMSM Based on SVM Technique

Mehedi¹,

Yahdou²,

Djilali³

et al. 2020

JESA

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This work presents an improved Direct Torque Control (DTC) method for a 5-phase interior permanent magnet synchronous motor (5P-IPMSM) based on space vector modulation (SVM) and fuzzy logic (FL). In this proposed DTC control, we replaced the traditional proportional-integral (PI) by the FL controller. The main goal is to improve system performance by minimizing the ripple of the stator flux and the torque ripples. The proposed control technique applied on the 5P-IPMSM is verified by Matlab/Simulink software followed by an analyzed and compared to verify the effectiveness of the proposed method. Many improvements have been made to the stator flux ripples, torque ripples, rise time and the robustness to disturbance.

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Fuzzy Sliding Mode Control of Permanent Magnet Synchronous Motor Based on the Integral Sliding Mode Surface

Long

Huang

Zheng

et al. 2019

2019 22nd International Conference on Electrical Machines and Systems (ICEMS)

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Speed Control of Series-Connected Five-Phase Two PMSM using Sliding Mode Control

Cited by 3 publications

References 12 publications

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

Direct Torque Fuzzy Controlled Drive for Multi-phase IPMSM Based on SVM Technique

Fuzzy Sliding Mode Control of Permanent Magnet Synchronous Motor Based on the Integral Sliding Mode Surface

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