Extended Kalman Filter for Flywheel Systems

Smart motor controllers usually provide plentiful information that makes it possible to model the various mechanisms they control. One such mechanism, a flywheel, is one of the simplest models to implement, yet has various characteristics that need to be identified for the model to be accurate. To ensure accurate control of the flywheel, not only is real-time state information necessary such as position, velocity, and acceleration, but also system characteristics such as inertia, friction, and drag. This paper investigates the use of extended Kalman filter to simultaneously estimate the system state and parameters, providing rich information that the user could use to optimally control the end mechanism.