State Estimation for Stochastic Time Varying Systems with Disturbance Rejection

Qinghua Zhang (1) , Liangquan Zhang (1)

1 I4S - Statistical Inference for Structural Health Monitoring

State estimation in the presence of unknown disturbances is useful for the design of robust systems in different engineering fields. Most results available on this topic are restricted to linear time invariant (LTI) systems, whereas linear time varying (LTV) systems have been studied to a lesser extent. Existing results on LTV systems are mainly based on the minimization of the state estimation error covariance, ignoring the important issue of the stability of the state estimation error dynamics, which has been a main focus of the studies in the LTI case. The purpose of this paper is to propose a numerically efficient algorithm for state estimation with disturbance rejection, in the general framework of LTV stochastic systems, including linear parameter varying (LPV) systems, with easily checkable conditions guaranteeing the stability of the algorithm. The design method is conceptually simple: disturbance is first rejected from the state equation by appropriate output injection, then the Kalman filter is applied to the resulting state-space model after the output injection.

Mots clés: disturbance rejection; state estimation; LTV/LPV system; Kalman filter

Domaines: Automatique

Qinghua Zhang, Liangquan Zhang. State Estimation for Stochastic Time Varying Systems with Disturbance Rejection. SYSID 2018 - 18th IFAC Symposium on System Identification, Jul 2018, Stockholm, Sweden. pp.55-59, ⟨10.1016/j.ifacol.2018.09.090⟩. ⟨hal-01909572⟩