AI-Enhanced Model Predictive and Active Disturbance Rejection Control for High-Performance Permanent Magnet Synchronous Motor Drives.
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| Title: | AI-Enhanced Model Predictive and Active Disturbance Rejection Control for High-Performance Permanent Magnet Synchronous Motor Drives. |
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| Authors: | Bahar, Saif Talal1 (AUTHOR), Wang, Weilin2 (AUTHOR), Qiu, Hao1,2 (AUTHOR) qh_zju@sina.com |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 11, p2574. 24p. |
| Subject Terms: | *Motor drives (Electric motors), *Predictive control systems, *Adaptive control systems, *Parameter estimation, *Torque control, *Permanent magnet motors |
| Abstract: | Permanent magnet synchronous motors (PMSMs) suffer performance degradation under parameter uncertainties and external load disturbances, reducing the effectiveness of conventional proportional-integral and field-oriented control (FOC) schemes. This paper presents an artificial intelligence (AI) enhanced hybrid controller that combines finite-control-set model predictive control (FCS-MPC) and active disturbance rejection control (ADRC). The FCS-MPC optimizes inverter switching states by minimizing a cost function through predicted current trajectories. Additionally, the ADRC employs an extended state observer to estimate and compensate for aggregated disturbances. A lightweight radial basis function neural network is utilized, whose centers and widths are initialized offline based on k-means clustering on representative data, while its output weights are updated online via a Lyapunov-based adaptive law. This network dynamically adjusts the MPC cost function weights and ADRC observer bandwidth according to real-time operating conditions, while enabling online identification of key motor parameters. MATLAB/Simulink R2024a simulations under step load torque conditions verify that the proposed method achieves a speed deviation within 3% of the rated value, an over 90% reduction in torque ripple compared to FOC, and a settling time of less than 5 ms. Although it incurs a moderate computational cost, the proposed controller exhibits improved tracking accuracy and enhanced robustness under simulated conditions. Consequently, the AI-enhanced MPC-ADRC strategy shows strong potential for high-performance applications, subject to future experimental validation. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Permanent magnet synchronous motors (PMSMs) suffer performance degradation under parameter uncertainties and external load disturbances, reducing the effectiveness of conventional proportional-integral and field-oriented control (FOC) schemes. This paper presents an artificial intelligence (AI) enhanced hybrid controller that combines finite-control-set model predictive control (FCS-MPC) and active disturbance rejection control (ADRC). The FCS-MPC optimizes inverter switching states by minimizing a cost function through predicted current trajectories. Additionally, the ADRC employs an extended state observer to estimate and compensate for aggregated disturbances. A lightweight radial basis function neural network is utilized, whose centers and widths are initialized offline based on k-means clustering on representative data, while its output weights are updated online via a Lyapunov-based adaptive law. This network dynamically adjusts the MPC cost function weights and ADRC observer bandwidth according to real-time operating conditions, while enabling online identification of key motor parameters. MATLAB/Simulink R2024a simulations under step load torque conditions verify that the proposed method achieves a speed deviation within 3% of the rated value, an over 90% reduction in torque ripple compared to FOC, and a settling time of less than 5 ms. Although it incurs a moderate computational cost, the proposed controller exhibits improved tracking accuracy and enhanced robustness under simulated conditions. Consequently, the AI-enhanced MPC-ADRC strategy shows strong potential for high-performance applications, subject to future experimental validation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19112574 |
