Robust-nonsmooth Kalman Filtering for Stochastic Sandwich Systems with Dead-zone

In this paper, a robust-nonsmooth Kalman filtering approach for stochastic sandwich systems with dead-zone is proposed, which can guarantee the variance of filtering error to be upper bounded. In this approach, the stochastic sandwich system with dead-zone is described by a stochastic nonsmooth state-space function. Then, in order to approximate the nonsmooth sandwich system within a bounded region around the equilibrium point, a linearization approach based on nonsmooth optimization is proposed. For handling the model uncertainty caused by linearization and modeling, the robust-nonsmooth Kalman filtering method is proposed for state estimation of the stochastic sandwich system with dead-zones with model uncertainty. Finally, both simulation and experimental examples are presented for evaluating the performance of the proposed filtering scheme.

Automated summary

A robust-nonsmooth Kalman filtering approach is proposed for stochastic sandwich systems with dead-zones, which guarantees an upper bound on the variance of filtering error. The approach describes the system using a stochastic nonsmooth state-space function and utilizes a linearization approach based on nonsmooth optimization to approximate the system within a bounded region around the equilibrium point. The method also handles model uncertainty and performs state estimation for the system through robust-nonsmooth Kalman filtering.