Advancements in Optimization Techniques for Active Magnetic Bearing Systems: Current Trends and Future Directions
Active Magnetic Bearings (AMBs) are revolutionizing high-speed, contactless operations across industries like aerospace, energy, and precision manufacturing. However, their complex dynamics—marked by nonlinear behavior, sensitivity to external disturbances, and intensive computational req...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11018331/ |
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| Summary: | Active Magnetic Bearings (AMBs) are revolutionizing high-speed, contactless operations across industries like aerospace, energy, and precision manufacturing. However, their complex dynamics—marked by nonlinear behavior, sensitivity to external disturbances, and intensive computational requirements—pose significant challenges. This review delves into cutting-edge optimization techniques for AMBs, from time-tested methods like PID control to innovative approaches such as metaheuristic algorithms, multi-objective optimization, and AI-powered strategies including reinforcement learning and iterative learning control. The emergence of hybrid optimization, adaptive fuzzy controllers, and machine learning-enhanced models is pushing the boundaries of AMB performance, offering substantial gains in stability, efficiency, fault tolerance, and vibration suppression. Through extensive simulations and real-world experiments, we highlight these advancements’ practical benefits in reducing energy consumption, combating harmonic vibrations, and ensuring resilient operation under dynamic conditions. We also explore key challenges such as enhancing power density, lowering computational overhead, and boosting long-term system reliability, while outlining exciting future directions like data-driven methods, real-time adaptive control systems, and novel material innovations. This review emphasizes the pivotal role of optimization in unlocking the full potential of AMBs, meeting the ever-growing demands of high-performance industrial applications. |
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| ISSN: | 2169-3536 |