Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm

Knypiński, Łukasz; Kuroczycki, Sebastian; Márquez, Fausto Pedro García

doi:10.3390/electronics10182299

Cited by 21 publications

(15 citation statements)

References 56 publications

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“…where: j is the randomly selected cuckoo, x b is the position of the best cuckoo, λ is the step size scaling factor [30], and σ is the distribution probability density function for non-negative random variables (Levy flight coefficient).…”

Section: Hybrid Cuckoo Search Algorithmmentioning

confidence: 99%

A novel hybrid cuckoo search algorithm for optimization of a line-start PM synchronous motor

Knypiński

2023

Bulletin of the Polish Academy of Sciences Technical Sciences

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The paper presents a novel hybrid cuckoo search (CS) algorithm for the optimization of the line-start permanent magnet synchronous motor (LSPMSM). The hybrid optimization algorithm developed is a merger of the heuristic algorithm with the deterministic Hooke-Jeeves method. The hybrid optimization procedure developed was tested on analytical benchmark functions and the results were compared with the classical cuckoo search algorithm, genetic algorithm, particle swarm algorithm and bat algorithm. The optimization script containing a hybrid algorithm was developed in Delphi Tiburón. The results presented show that the modified method is characterized by better accuracy. The optimization procedure developed is related to a mathematical model of the LSPMSM. The multi-objective compromise function was applied as an optimality criterion. Selected results were presented and discussed.

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Section: Hybrid Cuckoo Search Algorithmmentioning

confidence: 99%

A novel hybrid cuckoo search algorithm for optimization of a line-start PM synchronous motor

Knypiński

2023

Bulletin of the Polish Academy of Sciences Technical Sciences

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“…All optimization procedures were developed by the authors. The coefficients for each optimization were selected on the basis of many trial calculations during the investigation in the previous research works (Knypiński and Nowak, 2013; Knypiński et al , 2017; Knypiński, 2017; Knypiński et al , 2021). Optimization procedures containing each method (PSO, GA, BA, CS and OBI) were repeated 20 times.…”

Section: Convergence Analysis Of Selected Metaheuristic Algorithms Us...mentioning

confidence: 99%

Performance analysis of selected metaheuristic optimization algorithms applied in the solution of an unconstrained task

Knypiński

2021

COMPEL

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Purpose The purpose of this paper is to execute the efficiency analysis of the selected metaheuristic algorithms (MAs) based on the investigation of analytical functions and investigation optimization processes for permanent magnet motor. Design/methodology/approach A comparative performance analysis was conducted for selected MAs. Optimization calculations were performed for as follows: genetic algorithm (GA), particle swarm optimization algorithm (PSO), bat algorithm, cuckoo search algorithm (CS) and only best individual algorithm (OBI). All of the optimization algorithms were developed as computer scripts. Next, all optimization procedures were applied to search the optimal of the line-start permanent magnet synchronous by the use of the multi-objective objective function. Findings The research results show, that the best statistical efficiency (mean objective function and standard deviation [SD]) is obtained for PSO and CS algorithms. While the best results for several runs are obtained for PSO and GA. The type of the optimization algorithm should be selected taking into account the duration of the single optimization process. In the case of time-consuming processes, algorithms with low SD should be used. Originality/value The new proposed simple nondeterministic algorithm can be also applied for simple optimization calculations. On the basis of the presented simulation results, it is possible to determine the quality of the compared MAs.

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“…In [13], [14] PI controller is tuned based on optimization algorithms in order to get fast response under load and speed variations. Optimization algorithms like particle swarm optimization PSO [15]− [18], bacterial foraging BF [19], [20], differential evolution DE [21], teaching learning-based optimization TLBO [22], cuckoo search [23], nonlinear sine cosine algorithm [24], deep reinforcement learning with shallow controllers [25], a piecewise affine PI controller [26] and tuning inspired by Ziegler and Nichols [27], [28] were introduced to tune the controller to regulate the torque and speed of BLDC drive. In PSO, the random variables in velocity equation yields to the optimal value.…”

Section: Introductionmentioning

confidence: 99%

Design of optimal PI controller for torque ripple minimization of SVPWM-DTC of BLDC motor

Kumar

Satheesh

Sujatha

2023

IJPEDS

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Conventional direct torque control (CDTC) for brushless DC motor (BLDCM) using proportional integral (PI) controller suffers from torque ripple minimization and speed regulation issues. A novel method in fusion with space vector pulse width modulation (SVPWM) with DTC using optimal PI controller is developed to minimize these issues. In this method, SVPWM replaces switching table and hysteresis controllers in CDTC. To get better performance in steady state, conventional PI controllers are preferred in SVPWM-DTC for BLDCM. However, uncertainty arises due to PI controller tuning as well as load variations. In such a case, optimal PI controller tuned properly can minimize these uncertainties. Here, JAYA algorithm is used to tune controller gain parameters. Simulations of proposed PI controller of SVPWM-DTC for BLDCM are carried away in Simulink. To appreciate the performance of proposed optimal controller of SVPWM-DTC for BLDCM, the simulation results are compared with Conventional PI controller and particle swarm optimization (PSO) technique based tuned PI controller. This proposed technique reduces the toque ripple by 63.1% when compared to conventional PI and 58.5% when compared to PSO-PI controller. It also improves the settling time by 43.9% when compared to conventional PI and 46.79% when compared to PSO-PI controller.

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Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo Search Algorithm

Cited by 21 publications

References 56 publications

A novel hybrid cuckoo search algorithm for optimization of a line-start PM synchronous motor

A novel hybrid cuckoo search algorithm for optimization of a line-start PM synchronous motor

Performance analysis of selected metaheuristic optimization algorithms applied in the solution of an unconstrained task

Design of optimal PI controller for torque ripple minimization of SVPWM-DTC of BLDC motor

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