Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27576
Title: Estimation of systems with statistically-constrained inputs
Authors: Liang, Y
Zhang, L 
Zhou, D
Pan, Q
Keywords: Adaptive filter
Disturbance input
Kalman filtering
Minimum upper bound filter
Stochastic systems
Issue Date: 2010
Publisher: Elsevier
Source: Applied mathematics and computation, 2010, v. 217, no. 6, p. 2644-2656 How to cite?
Journal: Applied mathematics and computation 
Abstract: This paper discusses the estimation of a class of discrete-time linear stochastic systems with statistically-constrained unknown inputs (UI), which can represent an arbitrary combination of a class of un-modeled dynamics, random UI with unknown covariance matrix and deterministic UI. In filter design, an upper bound filter is explored to compute, recursively and adaptively, the upper bounds of covariance matrices of the state prediction error, innovation and state estimate error. Furthermore, the minimum upper bound filter (MUBF) is obtained via online scalar parameter convex optimization in pursuit of the minimum upper bounds. Two examples, a system with multiple piecewise UIs and a continuous stirred tank reactor (CSTR), are used to illustrate the proposed MUBF scheme and verify its performance.
URI: http://hdl.handle.net/10397/27576
ISSN: 0096-3003
EISSN: 1873-5649
DOI: 10.1016/j.amc.2010.07.077
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