TY - JOUR
T1 - Estimation of systems with statistically-constrained inputs
AU - Liang, Yan
AU - Zhang, Lei
AU - Zhou, Donghua
AU - Pan, Quan
PY - 2010/11/15
Y1 - 2010/11/15
N2 - 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.
AB - 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.
KW - Adaptive filter
KW - Disturbance input
KW - Kalman filtering
KW - Minimum upper bound filter
KW - Stochastic systems
UR - http://www.scopus.com/inward/record.url?scp=77958015314&partnerID=8YFLogxK
U2 - 10.1016/j.amc.2010.07.077
DO - 10.1016/j.amc.2010.07.077
M3 - 文章
AN - SCOPUS:77958015314
SN - 0096-3003
VL - 217
SP - 2644
EP - 2656
JO - Applied Mathematics and Computation
JF - Applied Mathematics and Computation
IS - 6
ER -