Improved performance of permanent magnet synchronous motor by using particle swarm optimization techniques

Wahsh, S.; Elwer, A.S.

doi:10.1109/robio.2007.4522492

Cited by 10 publications

(4 citation statements)

References 4 publications

(7 reference statements)

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“…This has triggered extensive research on the possibilities and potential of the so-called adaptive PI-controllers. Loosely defined, adaptive PIcontrollers avoid time-consuming manual tuning by providing optimal PI-controller settings automatically as the system dynamics or operating points change [12]; there are various conventional methods used for tuning of PI-controller such as [13][14]…”

Section: Methods Of Tuning the Pi-controllermentioning

confidence: 99%

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Optimal GainsTuning of Speed Controller in Induction Motor Drives Using Particle SWARMOptimization

Wally

Azazi

El-Khouly

2014

ERJ. Engineering Research Journal

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The last decade has witnessed a great interest in using evolutionary algorithms (EAs), such as genetic algorithms (GA), evolutionary strategies and particle swarm optimization (PSO), for multivariate optimization. This paper presents a modern approach of speed control for three-phase induction motor (IM) using PSO algorithm to optimize the parameters of the proportional integral (PI) and Fuzzy-PI controllers. Comparison between different controllers is achieved, using PI and fuzzy-PI controllers which are tuned by two methods, firstly manually and secondly using PSO technique. Hybrid of FL and PI controller PSO-based for the speed control of given motor is also performed to eliminate the drawbacks of PI controller (overshoot, undershoot) and FL controller (steady-state error), which has a minimum number of fuzzy rules and membership functions (MFs). The overall system is simulated under various operating conditions and experimental results are prepared.

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Section: Methods Of Tuning the Pi-controllermentioning

confidence: 99%

“…The implementation of PSO program is very easy and takes a few lines in the program, so PSOreduces the time of the whole program. The steps of the PSO program is described in [14,27]…”

Section: Repetition Of Steps 2 -5 Until Termination Criteriamentioning

confidence: 99%

Optimal GainsTuning of Speed Controller in Induction Motor Drives Using Particle SWARMOptimization

Wally

Azazi

El-Khouly

2014

ERJ. Engineering Research Journal

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“…In 2005 Haibing Hu, Qingbo Hu, Zhengyu Lu, Dehong Xu discussed optimal PID controller design in PMSM servo system via Particle Swarm Optimization [8]. In 2007 Said Wahsh and Ayman Elwer discussed improved performance of permanent magnet synchronous motor by using particle swarm optimization techniques [9]. In 2014 Mehmet Baskin and Bulent Caglar discussed a modified design of PID controller for permanent magnet synchronous motor drives using particle swarm optimization [10].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Permanent Magnet Synchronous Motor Drive using Particle Swarm Optimization Technique

Yadav¹,

Verma²

2017

J. Energ. Technol. Res.

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Permanent magnet synchronous motor (PMSM), a nonlinear speed controller design based on Particle Swarm Optimization (PSO) technique is presented in this paper. For tuning the PID controller, the speed control for PMSM was analyzed using PSO algorithm to optimize the parameters in terms of proportional gain (Kp), integral gain (Ki), and derivative gain (Kd). Moreover, the overall system is evaluated under various operating conditions such as starting, braking, load application and load removal. Besides this, comparison between speed control of PMSM using Ziegler-Nichols (Z-N) method and speed control of PMSM with PSO technique has been carried out. These analyses are estimated in terms of dynamic and static response. The transient response is examined in terms of settling time (ts), rise time (tr), peak time (tp), and peak overshoot (Mp). Overall, PID speed controller with PSO technique shows that the proposed method has improved the performance under the various operating conditions.

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“…The best ranges of membership functions, the best shapes of membership functions and the best fuzzy inference rules are dug out at the same time. Simulation results are given to show the effectiveness of FLC-Swarm controller [11].…”

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confidence: 99%

Induction Motor Control Using PSO-ANFIS

Mahapatra

Daniel²,

Dey

et al. 2015

Procedia Computer Science

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The speed of the Induction motor can be adjusted to a great extent so as to provide easy control and high performance. In this paper an effort is made to develop a prototype model to control the speed of an induction motor using PSO-ANFIS hybrid technique. We describe the use of Particle Swarm Optimization (PSO) and ANFIS for designing an optimal fuzzy logic controller of an Induction Motor. We have used two input parameters like speed, torque and output is firing angle. PSO-ANFIS speed controller obtains better dynamic behavior and superior performance of the Induction motor speed control. Similar approach can be correlated to the control of plasma column and which can be implemented in fusion reactor to control the plasma column. Plasma position and shape control is very crucial for the overall performance of the fusion reactor such as tokamak. The quality of the discharge in the Saskatchewan TORus-Modified (STOR-M) tokamak is strongly related to the position of the plasma column within the discharge vessel. If the plasma column approaches too near the wall, then either minor or complete disruption occurs. Consequently it is necessary to be able to control dynamically the position of the plasma column throughout the entire discharge. A comparison analysis of PSO-ANFIS and Fuzzy Back Propagation algorithm is taken in our work to control the speed of induction motor, where the PSO-ANFIS gives better result in terms of fast computing.

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Improved performance of permanent magnet synchronous motor by using particle swarm optimization techniques

Cited by 10 publications

References 4 publications

Optimal GainsTuning of Speed Controller in Induction Motor Drives Using Particle SWARMOptimization

Optimal GainsTuning of Speed Controller in Induction Motor Drives Using Particle SWARMOptimization

Performance Analysis of Permanent Magnet Synchronous Motor Drive using Particle Swarm Optimization Technique

Induction Motor Control Using PSO-ANFIS

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