State Estimation of Macromotion Positioning Tables Based on Switching Kalman Filter

In this brief, a switching Kalman filtering (SKF) method is proposed for the state estimation of macromotion positioning tables disturbed by random noises. In this method, the macromotion positioning tables working in noisy environment are described by the so-called stochastic sandwich models with a dead zone. In this scheme, a nonsmooth stochastic state space model is constructed first by introducing several embedded switch functions. These functions are used to describe the effect of a dead zone. Then, an SKF is developed based on the obtained nonsmooth stochastic state space model. The operating states of this filter can be switched automatically among different operating zones according to the change of system operating conditions. Moreover, the convergence of proposed switching filtering method is discussed. Then, the proposed SKF method is implemented to an X-Y macromotion positioning table for state estimation in a noisy case.