Genetic algorithm based current optimization for torque ripple reduction of interior PMSMs

Lai, Chunyan; Guo, Feng; Iyer, Lakshmi Varaha; Mukherjee, Kaushik; Kar, Narayan C.

doi:10.1109/icelmach.2016.7732655

Cited by 15 publications

(8 citation statements)

References 25 publications

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“…Genetic algorithms are stochastic based search algorithms, widely used as optimization method for solving constrained/unconstrained, nondifferentiable, discontinuous or nonlinear problems [7], [9], [10], [17], [18]. The GA algorithm is inspired on the mechanics of natural selection, the process that drives biological evolution.…”

Section: Ga Based Optimum Current Search Methods For Torque Ripple Mitigationmentioning

confidence: 99%

“…Several publications have appeared over the last decades documenting methods to define optimum feeding strategy for torque ripple mitigation in PMSM drives. In [9], a genetic algorithm (GA) based method is employed to optimize the magnitude and phase of selected current harmonics in direct and quadrature axis in a PMSM with nonsinusoidal back electromotive force (back-EMF). In [10], a GA method is used to search an optimized current considering the zero sequence back-EMF harmonics to contribute in torque production.…”

Section: Introductionmentioning

confidence: 99%

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GA-FEM Method for Optimum Current Search for Torque Ripple Mitigation in Nonsinusoidal PMSM and Analysis of On-Load Parameters

Castro¹,

Filho²,

Jesus³

et al. 2021

Proceedings of the 13th Seminar on Power Electronics and Control (SEPOC 2021)

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Permanent magnet synchronous motors with spatial harmonics can develop significant torque ripple under sinusoidal stator current feeding strategy. Usual approaches to reduce torque ripple aim the design of stator current harmonics, based on the machine electrical parameters, to induce compensating torque pulsations. However, the machine parameters are highly dependent on the electric load and the current harmonic content. Thus, these dependencies impose a challenge on the search for optimum stator currents. To overcome this drawback, this paper presents a genetic algorithm (GA) optimum current search procedure based on the machine design assisted by the Finite Element Method (FEM). Additionally, the variation of the onload estimated electrical parameters is analyzed considering different current feeding strategies. The results demonstrate that, according to FEM analysis, a null torque ripple with an increased torque per Ampère ratio is achieved and the parameter analysis demonstrates their different variation according to the current strategy.

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Section: Ga Based Optimum Current Search Methods For Torque Ripple Mitigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GA-FEM Method for Optimum Current Search for Torque Ripple Mitigation in Nonsinusoidal PMSM and Analysis of On-Load Parameters

Castro¹,

Filho²,

Jesus³

et al. 2021

Proceedings of the 13th Seminar on Power Electronics and Control (SEPOC 2021)

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show abstract

“…where T 0 is the fundamental component, and T 6 and T 12 are the 6th and 12th harmonic torque amplitudes, respectively. Equation (9) indicates that the 6th and 12th torque harmonics produced mainly owing to the nonsinusoidal flux distribution are periodic in nature. Therefore, the harmonic torque can also be considered as a function of the mechanical angle.…”

Section: Analysis Of Torque Ripplesmentioning

confidence: 99%

“…The focus was on using additional control measures to compensate for these periodic torque ripples. Many composite methods are proposed by researchers to deal with the torque ripple and simultaneously improve the performance robustness of the control system [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Speed Ripple Minimization of Permanent Magnet Synchronous Motor Based on Model Predictive and Iterative Learning Controls

Fei

Deng

et al. 2019

IEEE Access

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Permanent magnet synchronous motors (PMSMs) are widely used in the field of industrial servo control, especially in high-precision applications. Owing to the periodic torque ripple caused by the cogging torque, flux harmonics, and current offsets, the speed output of the system has a periodic ripple, which affects the control accuracy of the servo system. The conventional proportional-integral controllers cannot reject torque ripple and are highly dependent on motor parameters. This limits the control performance when a PMSM is used as a high-precision servo system. Thus, this paper proposes a combination of model predictive control (MPC) and iterative learning control (ILC) to not only speed up the response time of the system but also effectively reduce the speed ripples. MPC updates the predictive model in real time through feedback and evaluates the system output and control rate according to the cost function. It obtains an optimal control sequence for the next moment and has good parameter robustness and fast response. ILC records the speed of ripple signals over an entire cycle and then uses those signals to compensate for the control signal in the next cycle. It is capable of reducing the periodic speed ripples. The experimental verification of the schemes was conducted on a digital signal processor-field programmable gate-array-based platform. The experimental results obtained confirm the effectiveness of the proposed MPC-ILC scheme. INDEX TERMS Iterative learning control, model predictive control, PMSM control, speed ripple.

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“…In [11] the optimal stator current is obtained from the analysis of measured speed undulations. In [12] the optimal current harmonics are designed numerically at a given operation condition based on genetic algorithm. Despite the effectiveness, these approaches are computationally complex since they involve iterative computations to converge to a optimized solution.…”

Section: Introductionmentioning

confidence: 99%

Finite Control-Set Model Predictive Torque Control of Nonsinusoidal PMSM: a Generalized Approach for Torque Ripple Mitigation and MTPA Operation

Castro¹,

Guazzelli²,

Santos³

et al. 2021

Proceedings of the 13th Seminar on Power Electronics and Control (SEPOC 2021)

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Permanent Magnet Synchronous Motors (PMSMs) may present spatial harmonics depending on the design guidelines or imprecisions on the manufacturing process. The interaction of conventional sinusoidal current feeding strategies with these spatial harmonics can produce a considerable torque ripple. This paper deals with a modified Finite Control-Set Model Predictive Torque Control (FCS-MPTC) loop as an active torque ripple minimization solution for PMSMs with spatial harmonics. The proposed approach designs a novel cost function, based on the cross product reactive instantaneous power theory. The benefits of the proposed generalized approach include providing smooth torque production and the Maximum Torque per Ampère (MTPA) operation on PMSMs with a number of spatial harmonic sources, including those on zero-sequence. The effectiveness of the presented control strategy is demonstrated comparatively to conventional sinusoidal current feeding strategy on a PMSM drive employing a three-phase four-leg two level voltage source inverter under both steady and transient state.

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Genetic algorithm based current optimization for torque ripple reduction of interior PMSMs

Cited by 15 publications

References 25 publications

GA-FEM Method for Optimum Current Search for Torque Ripple Mitigation in Nonsinusoidal PMSM and Analysis of On-Load Parameters

GA-FEM Method for Optimum Current Search for Torque Ripple Mitigation in Nonsinusoidal PMSM and Analysis of On-Load Parameters

Speed Ripple Minimization of Permanent Magnet Synchronous Motor Based on Model Predictive and Iterative Learning Controls

Finite Control-Set Model Predictive Torque Control of Nonsinusoidal PMSM: a Generalized Approach for Torque Ripple Mitigation and MTPA Operation

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