Vector Control Decoupling Model Comparative Simulation

In motor control systems, vector control is a widely used high-performance control strategy. Its core principle involves decoupling the motor model to simplify the control structure. Currently, the main vector control decoupling models include feedback decoupling, feedforward decoupling, and cross decoupling, each with distinct advantages and limitations.

Feedback decoupling eliminates coupling effects by introducing dynamic compensation components. This approach features an intuitive structure and ease of implementation. However, due to its reliance on feedback signals, it is susceptible to noise and delays, resulting in relatively limited dynamic performance. In code implementation, this typically involves adding real-time compensation terms based on current feedback measurements using PI controllers.

Feedforward decoupling incorporates feedforward compensation terms into the control loop based on the motor model. This method effectively suppresses coupling effects without depending on dynamic changes in feedback signals, thereby achieving faster response speeds. Nevertheless, it requires high accuracy in model parameters - inaccurate models can degrade decoupling performance. Implementation often requires precise mathematical modeling of motor dynamics and parameter identification algorithms.

Cross decoupling combines characteristics of both feedforward and feedback methods, optimizing decoupling performance through cross-compensation techniques. It demonstrates superior dynamic characteristics compared to pure feedback decoupling while maintaining lower sensitivity to model errors than pure feedforward decoupling. However, its control structure is relatively complex with higher computational requirements. The implementation typically involves matrix operations and coordinate transformations for d-q axis current control.

By simulating and comparing these three decoupling models, engineers can more intuitively analyze their steady-state performance and dynamic responses, enabling selection of the most appropriate control strategy for different application scenarios. Simulation frameworks often utilize MATLAB/Simulink with custom S-function blocks for decoupling algorithm implementation.