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

Baoan Li, Yonghong Tan*, Lei Zhou, and Ruili Dong*
International Journal of Control, Automation, and Systems, vol. 19, no. 1, pp.101-111, 2021

Abstract : In this paper, a robust-nonsmooth Kalman filtering approach for stochastic sandwich systems with deadzone 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.

Keyword : Dead-zone, Kalman filter, random noise, robustness, sandwich systems.

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