Spatial Identification of PMSM Direct Torque Control Systems

Spatial identification of PMSM direct torque control systems is a critical component for achieving high-performance control. This identification method primarily determines the system order and state-space model, providing foundations for optimizing controller parameters.

First, system identification requires actual operational data including key variables like voltage, current, and rotational speed. These data are typically collected through sensors and undergo preprocessing (e.g., using digital filters in MATLAB like filtfilt() for zero-phase distortion) to eliminate noise interference. Subsequently, identification algorithms such as Least Squares method (implemented via lsqcurvefit() function) or Subspace Identification method (using n4sid() in System Identification Toolbox) derive the mathematical model, including coefficient matrices for state-space equations.

The established state-space model describes PMSM dynamic characteristics, capturing relationships between state variables including stator flux, torque, and rotational speed. Through the identified model, direct torque control strategies can be optimized - for instance, adjusting PI controller gains using Ziegler-Nichols tuning methods or modifying hysteresis comparator thresholds through lookup table implementations, thereby improving system response speed and stability.

Furthermore, the identification process must account for variations in motor parameters (such as inductance or resistance drift under temperature effects). Thus, adaptive identification methods utilizing recursive algorithms like Recursive Least Squares (RLS) offer advantages for long-term operation. Ultimately, accurate system identification enhances the robustness of PMSM direct torque control, maintaining superior performance across wide speed ranges.