Residual Physics-Informed Neural Networks for High-Fidelity BLDC Motor Modeling

• URL: http://arxiv.org/abs/2607.09136v1
• Date: Fri, 10 Jul 2026 06:47:02 GMT
• Title: Residual Physics-Informed Neural Networks for High-Fidelity BLDC Motor Modeling
• Abstract summary: This paper presents a Physics-Informed Neural Network (PINN) that learns a continuous-time surrogate of the full six-state BLDC motor dynamics.<n>Once trained, PINN inference achieves latencies of 0.1--22, mu s per query, up to 118x faster than conventional ODE solvers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate dynamics modeling of Brushless DC (BLDC) motors is fundamental to high-performance robotic joint control. This paper presents a Physics-Informed Neural Network (PINN) with a deep residual (ResNet) backbone that learns a continuous-time surrogate of the full six-state BLDC motor dynamics. Given simulation time, applied three-phase voltages, and excitation parameters as inputs, the network directly predicts all motor state variables -- rotor angle, angular velocity, three-phase currents, and winding temperature -- while simultaneously satisfying the governing electromechanical and thermal ODEs through a composite physics-data loss. A curriculum scheduling strategy gradually activates the physics penalty to prevent premature convergence. Training runs are completed in under two minutes on a standard CPU. Crucially, once trained, PINN inference achieves latencies of 0.1--22, mu s per query, up to 118x faster than conventional ODE solvers, making it suitable for real-time observer and control applications.

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