Neural Network Replacement of Stator Flux Observer in Motor Direct Torque Control

Direct Torque Control (DTC) for motors is a high-performance control strategy that achieves rapid dynamic response by directly regulating torque and flux. In traditional DTC systems, the stator flux observer serves as a critical component, but its performance is susceptible to motor parameter variations and measurement noise. In recent years, neural networks have been introduced as an alternative solution due to their powerful nonlinear approximation capabilities and adaptive characteristics.

The core concept of replacing conventional stator flux observers with neural networks involves learning motor dynamic characteristics through training data, directly mapping measurable signals such as voltage and current to flux relationships. Compared to model-dependent observers, this data-driven approach demonstrates stronger parameter robustness and can automatically adapt to changes in motor operating conditions. In practical implementations, feedforward neural networks or recurrent neural network architectures are typically employed, achieving high-precision flux estimation through offline training and online fine-tuning. Key implementation aspects include designing the network input layer to process normalized voltage/current measurements and configuring the output layer to generate flux magnitude and angle estimates.

This improvement not only simplifies the system structure but also enhances control performance, particularly in scenarios with time-varying motor parameters or unmodeled dynamics. Looking forward, integrating more complex network architectures like deep learning holds potential for further breakthroughs beyond the theoretical limitations of traditional observers. Code implementation would typically involve neural network toolbox functions for initialization, training with backpropagation algorithms, and real-time inference loops synchronized with PWM cycles.