Simultaneous input and state estimation for stochastic nonlinear systems with additive unknown inputs. (January 2020) - Kim, Hunmin; Guo, Pinyao; Zhu, Minghui; Liu, Peng
Abstract: This paper investigates simultaneous input and state estimation for a class of nonlinear stochastic systems. We propose a recursive filter to concurrently estimate system states and unknown inputs. We show that the estimation errors of the proposed filter are Practically Exponentially Stable in probability, and the estimation error covariance matrices are uniformly bounded.