MATLAB Simulation of Stepper Motor Control with Kalman Filter Implementation

In the field of motor control, stepper motors are widely adopted due to their precise positioning capabilities. MATLAB simulation provides an efficient and flexible tool for validating stepper motor control algorithms. This simulation focuses on implementing Kalman filter technology to achieve real-time stator current estimation, subsequently deriving rotor position and speed calculations.

### Simulation Objectives and Implementation Approach Current Estimation: Stator current signals in stepper motors typically contain noise and interference, which directly affect the accuracy of rotor position and speed estimation. The Kalman filter utilizes a state-space model, combining system dynamics and measurement noise characteristics, to effectively filter out noise and obtain current estimates closer to true values. In MATLAB implementation, this involves defining state transition matrices and measurement models using functions like 'kalman' or manual implementation through prediction-correction cycles. Position and Speed Estimation: Using the filtered current signals combined with motor models (such as stepper motor step angles and phase sequence relationships), real-time rotor position is estimated. Simultaneously, speed can be calculated through differentiation or state observer methods. Code implementation typically involves creating mathematical models that map current measurements to mechanical parameters using Euler integration or Runge-Kutta methods for continuous system simulation.

### Key Advantages of Kalman Filter Adaptability: Capable of online adjustment of filter gains to adapt to changing system noise conditions, implemented through covariance matrix updates in the algorithm. Prediction and Correction: Utilizes prediction steps and measurement updates to progressively optimize estimation results, enhancing position and speed tracking accuracy. The MATLAB code structure typically follows a two-step process: time update (prediction) and measurement update (correction) cycles.

### Simulation Extension Considerations Parameter Sensitivity: Stepper motor parameters like inductance and resistance may vary with temperature. The simulation can further analyze Kalman filter robustness to these parameter fluctuations by incorporating parameter variation studies in the code. Dynamic Response Optimization: Integration with PID or advanced control strategies (such as model predictive control) can improve system performance under variable speed or sudden load conditions. This can be implemented through control system toolbox functions or custom control algorithm development.

This simulation not only validates the effectiveness of Kalman filtering in stepper motor control but also provides theoretical reference for practical hardware implementation, with MATLAB/Simulink serving as an ideal platform for algorithm prototyping and verification.