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This paper proposes a closed-loop and implements some metaheuristic optimization approaches to regulate an unstable active magnetic bearing (AMB) system. First of all, a hardware model of an AMB is fabricated in the laboratory. Mathematical analysis is carried out and a linearized open-loop transfer function is obtained for an equilibrium point of operation, using the parameters of the hardware model. Then, a closed loop is proposed for this AMB system, which comprises a PID controller, power amplifier, and position sensor. Further, three different metaheuristic nature-inspired hunting-based optimization algorithms i.e., Ant lion optimization (ALO), Grey wolf optimization (GWO), and Whale optimization algorithm (WOA) are implemented individually to calculate the gain parameters of the PID controller. Separately, the performance of these optimization algorithms is evaluated and observed on four different performance indices: integral of absolute error (IAE), integral of squared error (ISE), and an integral of time multiplied absolute error (ITAE) and integral of time multiplied squared error (ITSE). For a stable, efficient, and reliable bearing operation, it is vital to perform an analysis of the performance of optimization techniques with different objective functions for the proposed system. Therefore, few comparisons are conducted, first based on data obtained from statistical analysis. The second is based on data obtained from transient state performance and phase margin. Third on the scale of algorithm execution time. Finally, with the assistance of observed data effectiveness of each optimization technique to control the proposed AMB system is concluded which can serve as theoretical and experimental foundations for the continued use of AMB in high-speed applications.

Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing System