MATLAB Simulation of Direct Vector Rotor Field-Oriented Control

Direct Vector Rotor Field-Oriented Control (FOC) is a widely adopted control strategy for high-performance motor drives. Its core principle involves transforming three-phase AC motor variables into DC quantities in a rotating reference frame through coordinate transformations, achieving control performance similar to DC motors. In MATLAB simulations, this can be implemented using Simulink to build control models and validate system performance. The simulation typically involves mathematical modeling of motor equations and implementing coordinate transformation algorithms. First, it is essential to establish the mathematical model of the motor, including voltage equations, flux linkage equations, and torque equations. The key to rotor field-oriented control lies in aligning the d-axis (direct axis) with the rotor flux direction, ensuring that the q-axis (quadrature axis) exclusively influences torque, thereby achieving decoupled control. In simulation, Clarke and Park transformations are implemented to convert three-phase stator currents into dq-frame components. These transformations can be coded using MATLAB functions or Simulink blocks to process real-time current data. Closed-loop control typically consists of current and speed loops. The current loop tracks reference d-axis and q-axis currents, while the speed loop regulates torque current (iq) for precise speed control. During simulation, issues such as current tracking errors, parameter sensitivity, or poor dynamic response may arise, often related to PI regulator tuning, accuracy of flux observation, or inverter nonlinearities. Implementing adaptive PI controllers or sensorless observation algorithms can mitigate these challenges. Although simulations may not fully replicate real-system dynamics, they effectively demonstrate fundamental FOC principles, including flux-torque decoupling, coordinate transformation effects, and closed-loop regulation processes. By adjusting control parameters or optimizing observation algorithms (e.g., using sliding mode observers or model reference adaptive systems), system performance can be further enhanced. MATLAB/Simulink provides tools like PID Tuner and Control System Designer for systematic parameter optimization.