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2023
DOI: 10.1109/access.2023.3241854
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Metaheuristic Optimization Techniques Used in Controlling of an Active Magnetic Bearing System for High-Speed Machining Application

Abstract: Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, and efficient performance of an in-house fabricated active magnetic bearing (AMB) system. In this manuscript, three pre-eminent population-based metaheuristic optimization techniques: Genetic algorithm (GA), Particle swarm optimization (PSO), and Cuckoo search algorithm (CSA) are implemented one by one, to calculate optimized gain param… Show more

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Cited by 9 publications
(3 citation statements)
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References 51 publications
(49 reference statements)
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“…The objective function is determined in advance for the design of optimization techniques based on the desired specifications and constraints [72]. The objective function used to optimize the controller parameters is typically chosen based on performance criteria that are dependent on system response [73]. The desired specification in a time domain system is the value of overshoot, rise time, settling time, and steady-state error [74].…”
Section: Index Criterionmentioning
confidence: 99%
“…The objective function is determined in advance for the design of optimization techniques based on the desired specifications and constraints [72]. The objective function used to optimize the controller parameters is typically chosen based on performance criteria that are dependent on system response [73]. The desired specification in a time domain system is the value of overshoot, rise time, settling time, and steady-state error [74].…”
Section: Index Criterionmentioning
confidence: 99%
“…The accuracy and efficiency of SM is closely related to the quality and quantity of the dataset used for training [12][13][14]. For optimal design, the training dataset must be representative of the entire input space to avoid bias and allow the surrogate model to generalize well [15][16][17]. Despite the critical role of dataset selection, comprehensive discussions on optimizing the data selection strategy for efficiency and fast convergence are lacking in past literature on electric machine design.…”
Section: Introductionmentioning
confidence: 99%
“…From a control point of view, the researcher implemented classical and advanced controllers to control the levitated position. BP neural network-based control strategy is used to control the six-pole machine [16], flux density feedback control is used for the flywheel energy storage application [17], and metaheuristic optimization techniques are used to control the AMB for high-speed applications [18]. In comparison to these, control strategies proposed two loop-control that is simple and efficient in controlling the desired stable position and is cost-effective.…”
Section: Introductionmentioning
confidence: 99%