High Performance Hybrid FOC-Fuzzy-PI Controller for PMSM Drives

Djelamda, Imene; Bouchareb, Ilhem; Lebaroud, Abdesselam

doi:10.18280/ejee.230403

Cited by 6 publications

(5 citation statements)

References 8 publications

(11 reference statements)

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“…The ANFIS system technique was proposed by Jang. It integrates both artificial neural network and fuzzy inference system [19]. The latter provides a fuzzy logic process founded on rules generated during the model's training process.…”

Section: Development Of the Anfis Systemmentioning

confidence: 99%

A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry

Chakhrit,

Djelamda,

Bougofa

et al. 2024

Preprint

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Failure mode and effects analysis (FMEA) is a systematic and structured method employed across diverse industries to proactively identify and evaluate potential failure modes. In a traditional FMEA, for all failure modes, three criticality parameters: severity, detection, and frequency, are assessed to evaluate criticality. Nevertheless, it frequently has certain flaws. Therefore, in this work, a fuzzy risk proposed model is used to improve the use of the FMEA methodology. The new model uses a fuzzy inference technique in place of the conventional criticality calculation. Fuzzy logic technique is used where the various factors are given as members of a fuzzy set fuzzified by employing adequate membership functions to evaluate the risk and then ranking failure modes and preferring measures to control the risks of undesired events. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is suggested as a dynamic, intelligently proposed model to improve and validate the results acquired by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. Finally, an automotive parts industry case is presented to show the potential of the suggested model. This analysis offers a different ranking of failure modes and improves the decision-making by providing a “preventive –corrective plan. A comparison with existing approaches is presented to demonstrate the efficiency of the suggested approach.

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Section: Development Of the Anfis Systemmentioning

confidence: 99%

A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry

Chakhrit,

Djelamda,

Bougofa

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to the physical constraints of the motor and inverter, limits must be placed on the system control inputs to protect the system [13]. When the controller output is limited by saturation due to an increase in the accumulation of the integral term (as can happen when there is a step response or sudden changes in load), a phenomenon known as Windup occurs [14][15][16]. This leads to a decline in the performance of the closed-loop system (e.g., a larger overshoot, a longer regulation time, and even the system losing stability).…”

Section: Introductionmentioning

confidence: 99%

A Composite Variable Structure PI Controller for Sensorless Speed Control Systems of IPMSM

Feng

Bai

Zhang³

et al. 2022

Energies

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In the speed control system of an Interior Permanent Magnet Synchronous Motor (IPMSM) without a speed sensor, PI controllers using only a fixed set of parameters cannot achieve accurate tracking of the estimated speed in a wide speed domain and also suffer from step response overshoot. This paper proposes a Compound Variable Structure PI (CVSPI) controller to improve the system control performance. It can choose whether to include an integral term according to the size of the system deviation to speed up the response. It also introduces a Model Reference Adaptive System (MRAS) speed observer in the controller to estimate the speed and adaptively adjust the size of the anti-integration saturation gain to improve the dynamic response following performance and immunity of the system. A feed-forward link is added for a given input differential to achieve an accurate answer to time-varying inputs. As the linear compensation matrix of the conventional MRAS is a unit matrix, the speed can only be accurately observed in a specific speed range. In this paper, a new linear compensation matrix is designed, and a new speed adaptive law is derived, allowing the improved MRAS to measure speed over a wide range accurately. Simulation results validate the excellent control performance of the CVSPI and the accuracy of the enhanced MRAS over a wide speed range.

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Section: Introductionmentioning

confidence: 99%

Application of Composite Variable Structure PI in the IPMSM Speed Sensorless Control System

Feng¹,

Bai²,

Zhang³

et al. 2022

Preprint

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In the speed control system of Interior Permanent Magnet Synchronous Motor (IPMSM) without a speed sensor, the speed under the traditional PI control suffers from poor tracking performance and step response overshoot. This paper proposes a Compound Variable Structure PI (CVSPI) controller to improve the system control performance. It can choose whether to include an integral term according to the size of the system deviation to speed up the response. It also introduces a Model Reference Adaptive System (MRAS) speed observer in the controller to estimate the speed and adaptively adjust the size of the anti-integration saturation gain to improve the dynamic response following performance and immunity of the system. A feed-forward link is added for a given input differential to achieve an accurate answer to time-varying inputs. As the linear compensation matrix of the conventional MRAS is a unit matrix, the speed can only be accurately observed in a specific speed range. In this paper, a new linear compensation matrix is designed, and a new speed adaptive law is derived, allowing the improved MRAS to measure speed over a wide range accurately. Simulation results validate the excellent control performance of the CVSPI and the accuracy of the enhanced MRAS over a wide speed range.

show abstract

High Performance Hybrid FOC-Fuzzy-PI Controller for PMSM Drives

Cited by 6 publications

References 8 publications

A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry

A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry

A Composite Variable Structure PI Controller for Sensorless Speed Control Systems of IPMSM

Application of Composite Variable Structure PI in the IPMSM Speed Sensorless Control System

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