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<p>This study introduces a novel Barrier Function Adaptive-Based Super-Twisting Algorithm (BF-STA) for controlling rotor equilibrium in Active Magnetic Bearings (AMBs), addressing key challenges such as system nonlinearities, unknown disturbances, and chattering effects commonly encountered in traditional control strategies. While many control approaches have been proposed in literature–including PID, classical Sliding Mode Control (SMC), and Terminal Sliding Mode Control (TSMC)–they typically require prior knowledge of disturbance bounds, suffer from fixed gain limitations, or exhibit excessive chattering that degrades performance. The BF-STA was selected specifically because it combines the robustness of second-order sliding mode control with an adaptive gain tuning mechanism driven by a barrier function, which enables real-time adjustment without requiring disturbance bounds, and ensures smooth control action even under significant uncertainties. The integration of this adaptive barrier-based mechanism significantly enhances stability, precision, and disturbance rejection, making it highly effective in maintaining rotor position under varying operational conditions. The control strategy was initially developed for a simplified Single Degree of Freedom (SDOF) model and later extended to a full Three Degrees of Freedom (3-DOF) system, with both simulation and experimental validations confirming its effectiveness and scalability. Results demonstrate superior tracking accuracy, reduced overshoot, and improved robustness compared to conventional controllers. The main contribution lies in the development and real-world validation of a scalable, adaptive, and chattering-free control strategy suitable for sensitive applications such as wind turbine systems, where fluctuating loads and environmental conditions are prevalent. The practical implication of this work is its potential to enhance the operational efficiency and reliability of AMB-equipped systems in demanding environments. Socially, it contributes to the advancement of sustainable energy technologies through improved performance and resilience of wind energy systems. However, this study is limited to laboratory-scale validation; future research should explore its application in full-scale industrial setups and its integration with intelligent supervisory control frameworks.</p>

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Robust Adaptive Barrier Function Super-Twisting Control for Multi-DOF Active Magnetic Bearings

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Godspower S. Bruno,
  • Diaa-Eldin A. Mansour,
  • Ayman Nada,
  • Tamer F. Megahed

摘要

This study introduces a novel Barrier Function Adaptive-Based Super-Twisting Algorithm (BF-STA) for controlling rotor equilibrium in Active Magnetic Bearings (AMBs), addressing key challenges such as system nonlinearities, unknown disturbances, and chattering effects commonly encountered in traditional control strategies. While many control approaches have been proposed in literature–including PID, classical Sliding Mode Control (SMC), and Terminal Sliding Mode Control (TSMC)–they typically require prior knowledge of disturbance bounds, suffer from fixed gain limitations, or exhibit excessive chattering that degrades performance. The BF-STA was selected specifically because it combines the robustness of second-order sliding mode control with an adaptive gain tuning mechanism driven by a barrier function, which enables real-time adjustment without requiring disturbance bounds, and ensures smooth control action even under significant uncertainties. The integration of this adaptive barrier-based mechanism significantly enhances stability, precision, and disturbance rejection, making it highly effective in maintaining rotor position under varying operational conditions. The control strategy was initially developed for a simplified Single Degree of Freedom (SDOF) model and later extended to a full Three Degrees of Freedom (3-DOF) system, with both simulation and experimental validations confirming its effectiveness and scalability. Results demonstrate superior tracking accuracy, reduced overshoot, and improved robustness compared to conventional controllers. The main contribution lies in the development and real-world validation of a scalable, adaptive, and chattering-free control strategy suitable for sensitive applications such as wind turbine systems, where fluctuating loads and environmental conditions are prevalent. The practical implication of this work is its potential to enhance the operational efficiency and reliability of AMB-equipped systems in demanding environments. Socially, it contributes to the advancement of sustainable energy technologies through improved performance and resilience of wind energy systems. However, this study is limited to laboratory-scale validation; future research should explore its application in full-scale industrial setups and its integration with intelligent supervisory control frameworks.