2023
DOI: 10.1109/access.2023.3241854
|View full text |Cite
|
Sign up to set email alerts
|

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

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
1
0

Year Published

2023
2023
2025
2025

Publication Types

Select...
7
1

Relationship

1
7

Authors

Journals

citations
Cited by 15 publications
(3 citation statements)
references
References 51 publications
0
1
0
Order By: Relevance
“…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%