State Estimation for Linear Constrained Systems with Unknown Input

WEN Chuanbo; CAI Yunze

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WEN Chuanbo and CAI Yunze, “State Estimation for Linear Constrained Systems with Unknown Input,” Chinese Journal of Electronics, vol. 23, no. 3, pp. 539-544, 2014,

Citation:

WEN Chuanbo and CAI Yunze, “State Estimation for Linear Constrained Systems with Unknown Input,” Chinese Journal of Electronics, vol. 23, no. 3, pp. 539-544, 2014,

WEN Chuanbo and CAI Yunze, “State Estimation for Linear Constrained Systems with Unknown Input,” Chinese Journal of Electronics, vol. 23, no. 3, pp. 539-544, 2014,

Citation:

WEN Chuanbo and CAI Yunze, “State Estimation for Linear Constrained Systems with Unknown Input,” Chinese Journal of Electronics, vol. 23, no. 3, pp. 539-544, 2014,

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State Estimation for Linear Constrained Systems with Unknown Input

WEN Chuanbo^1,2,
CAI Yunze¹

1.
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;
2.
College of Electric Engineering, Shanghai Dianji University, Shanghai 200240, China

Funds: This work was supported by Natural Science Foundation of China (No.61374136, No.61374160, No.61104028), the Natural Science Foundation of Shanghai (No.12ZR1411800) and the Key Training Foundation from Shanghai Dianji University (No.12C102).

Received Date: 2013-01-01
Rev Recd Date: 2013-07-01
Publish Date: 2014-07-05

Abstract

Abstract

This paper deals with the filtering problem for linear discrete constrained dynamic systems with unknown input. The constraint matrix and constraint vector in this system are allowed to vary in the value and in the dimension. The original full state is separated into two parts, and the estimate of the state is reduced to find the optimal estimate of a singular system. The estimable condition is researched and a recursive estimator for the original full state is presented. A rigorous mathematical induction is given to compare the performance of our approach to that of the existing method without constraint. A numerical example is presented to demonstrate the effectiveness of the new method.
- Constrained filtering,
- Estimate error covariance,
- Unknown input,
- Singular system

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References(14)

References

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