Two-wheel self-balanced car based on Kalman filtering and PID algorithm

Self-balanced car is a typical Incomplete control system. Self-balanced car´s body is a natural instability, so it has multivariable, nonlinear, strong coupling and other characteristics. The choice of inertial sensors has become one of the most important issues while designing a self-balanced car. But the sensor module is too expensive that is the reason of the high cost of the self-balanced car. A lower costed acceleration ADXL335 and angular velocity sensor ISZ-650 are chosen to make a sensor module much cheaper and the attitude is measured with Kalman filtering and PID algorithm. Finally a self-balanced car model is made to prove the feasibility of reducing costs.