Simulation Model of Vector Control for Induction Motors Without Speed Sensors

The sensorless vector control simulation model for induction motors is a widely adopted technology in motor control applications, primarily designed to achieve precise speed and torque regulation without mechanical sensors. The core principle involves estimating rotor speed and position using electrical parameters (such as stator currents and voltages), thereby reducing hardware costs and enhancing system reliability.

The implementation typically involves these key components: Field-Oriented Control (FOC): This technique decomposes stator currents into flux-producing (d-axis) and torque-producing (q-axis) components, enabling independent control of magnetic flux and torque for optimized dynamic performance. Code implementation often requires Clarke/Park transformations and inverse transformations to convert between reference frames. Speed and Position Estimation: Algorithms like Model Reference Adaptive System (MRAS), Sliding Mode Observer (SMO), or Extended Kalman Filter (EKF) are employed to estimate rotor states in real-time based on motor mathematical models. In MATLAB/Simulink, these can be implemented using function blocks or S-functions with adaptive gain tuning mechanisms. Simulation Verification: Models are built in tools like MATLAB/Simulink or PLECS, where control parameters (such as PI regulator gains) are optimized through systematic tuning to improve dynamic response and disturbance rejection capabilities. Simulation scripts often include parameter sweep functions to test robustness across operating conditions.

The key advantage lies in reduced dependency on mechanical sensors, making it suitable for harsh environments or high-reliability applications like electric vehicles and industrial drives. Research focuses typically address estimation algorithm robustness, low-speed performance improvements, and parameter sensitivity analysis through Monte Carlo simulations or stability margin calculations.