Metaheuristic Optimization Techniques Used in Controlling of an Active Magnetic Bearing System for High-Speed Machining Application

Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, efficient 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 (PSO), Cuckoo search (CSA) implemented one by one, calculate optimized gain parameters PID the proposed closed-loop Performance indices or, objective functions on which these techniques executed integral absolute error (IAE), square (ISE), time multiplied (ITAE), (ITSE). The significance technique function can obtain only implementing it. As result, several comparisons made based statistical performance, domain, frequency response behavior, execution time. Finally, applicability strategies in addition is determined with aid comparative analysis. That could assist choosing suitable along index application