Model-free predictive control for PMSM based on a nonlinear autoregressive exogenous model and an adaptive recursive
1School of Artificial Intelligence, Anshan Normal University, Anshan, Liaoning, China.
Plos One
|July 27, 2026
Summary
A new control strategy for permanent magnet synchronous motor (PMSM) drives improves performance by adapting to motor parameter changes. This advanced method significantly reduces current harmonics and enhances stability, even with system inaccuracies.
Area of Science:
- Electrical Engineering
- Control Systems
- Power Electronics
Background:
- Permanent magnet synchronous motor (PMSM) drives are crucial in modern applications.
- Conventional model predictive control (MPC) suffers performance degradation due to motor parameter mismatches.
- Robust control strategies are needed to maintain performance under varying operating conditions.
Purpose of the Study:
- To propose a novel nonlinear autoregressive with exogenous input-based adaptive recursive least squares model-free predictive control (NARX-ARLS-MFPC) strategy for PMSM drives.
- To enhance the robustness and performance of PMSM control against inevitable motor parameter uncertainties.
- To reduce current total harmonic distortion (THD) and improve dynamic response.
Main Methods:
- Integration of a nonlinear autoregressive with exogenous input (NARX) model with an adaptive recursive least squares (ARLS) algorithm.
- Utilizing a variable forgetting factor in the ARLS algorithm for dynamic tracking of system changes.
- Comprehensive simulation studies to validate the proposed control strategy's effectiveness.
Main Results:
- The NARX-ARLS-MFPC strategy demonstrated superior robustness against significant inductance and flux linkage mismatches.
- Achieved reductions in current THD by 28.3% and 12.3% compared to finite-control-set model predictive control (FCS-MPC) and a baseline method, respectively.
- Showcased stable performance under moderate sensor noise and significant THD reductions during load transients with parameter mismatches.
Conclusions:
- The synergistic integration of the nonlinear model and adaptive identification effectively suppresses current harmonics caused by model inaccuracies.
- The proposed NARX-ARLS-MFPC strategy enhances dynamic performance and robustness in PMSM drives.
- This advanced control method offers a promising solution for improving the reliability and efficiency of PMSM systems.
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