Self-Balancing Car

Overview

This project is an Arduino-based self-balancing car that utilizes an MPU sensor for balance control and a PID controller for stability. The car can be controlled wirelessly using a Bluetooth module (HC-05). However, the remote control feature is not working perfectly in this version, so you may take it as a challenge to improve it.

Components Used

• Arduino (Uno/Nano/Equivalent)
• MPU9250 (Gyroscope, Accelerometer, and Magnetometer) - You can also use MPU6050 (Gyroscope and Accelerometer only)
• L298N Motor Driver
• HC-05 Bluetooth Module
• 2 DC Motors
• Potentiometers (for tuning P and D values)
• Power Supply (2 Li-ion batteries)

Pin Configuration

Motor Control Pins:

• ENA: Pin 6 (Motor A Speed Control)
• IN1: Pin 5 (Motor A Direction Control)
• IN2: Pin 4 (Motor A Direction Control)
• ENB: Pin 9 (Motor B Speed Control)
• IN3: Pin 7 (Motor B Direction Control)
• IN4: Pin 8 (Motor B Direction Control)

Potentiometer Pins:

• POT_P: A3 (For tuning Kp - Proportional Gain)
• POT_D: A2 (For tuning Kd - Derivative Gain)

Bluetooth Module:

• RX: Pin 10
• TX: Pin 11

Functionality

1. Orientation Measurement: The MPU sensor provides angle data, which is processed using the Madgwick filter.
   • Alternative Approaches:
     • You can use the Digital Motion Processor (DMP) of the MPU6050 to obtain quaternions, which can then be converted to yaw, pitch, and roll.
     • You can also use an Extended Kalman Filter (EKF) for orientation estimation. Find our EKF implementation here: https://github.com/sakar111/Sensor-Fusion-orientation-and-position/tree/AHRS_EKF_C%2B%2B/Documents/Arduino/libraries/major_project
     • References:
       • Madgwick filter mathematical equations and explanation: Madgwick Filter Documentation
       • Extended Kalman Filter (EKF) implementation and mathematical equations: EKF Documentation
2. PID Controller: The system uses a PID control loop to stabilize the car.

Setup Instructions

1. Upload the Code

   • Open the Arduino IDE and upload self_balancing_car.ino to the Arduino board.

2. Connect the Hardware

   • Ensure all components are properly connected according to the pin configuration.
   • Power the Arduino and Motor Driver using a suitable battery.

3. Calibrate the MPU6050

   • The setup phase includes calibrating the IMU for 40 seconds.
   • Important: Before uploading this code, make sure that you have calibrated the offsets of your IMU (Inertial Measurement Unit).
     • For accelerometer and gyroscope calibration, upload and run the calibration code from the calibration folder.
     • For magnetometer calibration, you will need MATLAB.

PID Tuning

• Kp (Proportional Gain): Adjusts how strongly the system reacts to angle errors.
• Ki (Integral Gain): Compensates for accumulated past errors (usually minimal in balancing systems).
• Kd (Derivative Gain): Helps in damping oscillations and providing smooth control.

Notes

• Ensure the IMU sensor is stable during initialization.
• The car must be placed on a level surface before starting.
• Fine-tune PID values using the potentiometers to achieve better balance and response.