How to Implement a Kalman Filter for MPU6050 Calibration on STM32F4 Quadcopter

Implementing a Kalman Filter for Real-Time Calibration of a MPU6050 IMU Sensor in an STM32F4-Based Quadcopter

The MPU6050 sensor is a popular choice for motion tracking in quadcopters, but it often requires precise calibration to function optimally. In this tutorial, we will implement a Kalman filter to improve the accuracy of the sensor readings in an STM32F4-based quadcopter. The Kalman filter will help fuse the readings from the accelerometer and gyroscope, providing a more reliable estimate of the quadcopter’s orientation.

Prerequisites

• Basic understanding of C programming and embedded systems.
• Familiarity with STM32 microcontrollers and the STM32CubeIDE.
• Basic knowledge of Kalman filters and sensor fusion principles.
• Access to an STM32F4 development board and a MPU6050 sensor.

Parts/Tools

• STM32F4 development board
• MPU6050 IMU sensor
• STM32CubeIDE
• Wiring tools (jumper wires, breadboard)
• Basic electronic components (resistors, capacitors, etc.) if needed

Steps

1. Setting Up the Hardware
   1. Connect the MPU6050 sensor to the STM32F4 board:

   
                   STM32F4 Pin    MPU6050 Pin
                   -------------------------
                   SDA            SDA
                   SCL            SCL
                   VCC            VCC
                   GND            GND
               

   2. Ensure all connections are secure and correct.
2. Configuring the STM32CubeIDE
   1. Create a new STM32 project in STM32CubeIDE.
   2. Select the appropriate STM32F4 microcontroller.
   3. Enable I2C peripheral in the configuration settings.
   4. Include necessary libraries for I2C communication and sensor data handling.
3. Writing the Code to Read Sensor Data
   1. Initialize the MPU6050:

   
                   void MPU6050_Init() {
                       // Write to the power management register
                       I2C_Write(MPU6050_ADDR, PWR_MGMT_1, 0x00); // Wake up the MPU6050
                   }
               

   2. Read accelerometer and gyroscope data:

   
                   void MPU6050_Read_Data(int16_t *ax, int16_t *ay, int16_t *az, 
                                          int16_t *gx, int16_t *gy, int16_t *gz) {
                       uint8_t data[14];
                       I2C_Read(MPU6050_ADDR, ACCEL_XOUT_H, data, 14);
                       *ax = (data[0] << 8) | data[1];
                       *ay = (data[2] << 8) | data[3];
                       *az = (data[4] << 8) | data[5];
                       *gx = (data[8] << 8) | data[9];
                       *gy = (data[10] << 8) | data[11];
                       *gz = (data[12] << 8) | data[13];
                   }
               

4. Implementing the Kalman Filter
   1. Define the Kalman filter structure:

   
                   typedef struct {
                       float q; // Process noise covariance
                       float r; // Measurement noise covariance
                       float x; // State estimate
                       float p; // Estimate error covariance
                       float k; // Kalman gain
                   } KalmanFilter;
               

   2. Initialize the Kalman filter:

   
                   void Kalman_Init(KalmanFilter *kf) {
                       kf->q = 0.001; // Process noise
                       kf->r = 0.01;  // Measurement noise
                       kf->x = 0;     // Initial state
                       kf->p = 1;     // Initial error covariance
                   }
               

   3. Update the Kalman filter with new sensor readings:

   
                   float Kalman_Update(KalmanFilter *kf, float measurement) {
                       // Prediction update
                       kf->p += kf->q;
                       
                       // Measurement update
                       kf->k = kf->p / (kf->p + kf->r);
                       kf->x += kf->k * (measurement - kf->x);
                       kf->p *= (1 - kf->k);
                       
                       return kf->x; // Return the filtered value
                   }
               

5. Testing and Calibration
   1. Gather sensor data and apply the Kalman filter:

   
                   void Process_Sensor_Data() {
                       int16_t ax, ay, az, gx, gy, gz;
                       MPU6050_Read_Data(&ax, &ay, &az, &gx, &gy, &gz);
                       float filtered_angle = Kalman_Update(&kalman_filter, gx);
                       // Use filtered_angle for further processing
                   }
               

   2. Test the quadcopter in different orientations to validate the calibration.

Troubleshooting

• Issue: No data from MPU6050.
  • Check the I2C connections and ensure proper power supply.
  • Verify the I2C address used in the code matches the sensor’s address.
• Issue: Inaccurate readings.
  • Adjust the process and measurement noise values in the Kalman filter.
  • Ensure the sensor is calibrated correctly before use.

Conclusion

By implementing a Kalman filter in conjunction with the MPU6050 sensor, you can significantly enhance the accuracy of orientation data in your STM32F4-based quadcopter. This tutorial provided a step-by-step approach to set up the sensor, implement the Kalman filter, and troubleshoot common issues. With these skills, you can improve the performance of your quadcopter and ensure stable flight.