Stochastic Control for Stepper Motors using Gaussian Filter-based Disturbance and State Observer

This paper aims to mitigate the impact of modeling and measurement errors on the control of a stepper motor operating in microstepping mode. We present a control system incorporating a Gaussian filter-based disturbance and state observer, alongside stochastic control theory, treating modeling and observation errors as random variables. The Gaussian filter, developed in conjunction with a disturbance observer, facilitates highly accurate estimation of disturbances and states, while addressing measurement and modeling inaccuracies. Additionally, we propose a controller ensuring system stability through first-moment stability, a form of stochastic stability. By exploring stochastic stability, we aim to mitigate the effects of estimation errors during linearized feedback. Simulations are employed to compare the performance of the proposed method with that of the second-order extended Kalman filter.