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Robust Filter for Linear Stochastic Partial Differential Systems via a Set of Sensor Measurements

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2 Author(s)
Wen-Hao Chen ; Dept. of Electr. Eng., Nat. Tsing-Hua Univ., Hsinchu, Taiwan ; Chen, Bor-Sen

This study addresses a robust H filtering design problem for linear stochastic partial differential systems (LSPDSs) with external disturbance and measurement noise in the spatio-temporal domain. For LSPDSs, the robust H filter design via a set of sensor measurements needs to solve a complex Hamilton Jacobi integral inequality (HJII) for robust state estimation despite external disturbance and measurement noise. In order to simplify the design procedure, a stochastic spatial state space model is developed to represent the stochastic partial differential system via the semi-discretization finite difference scheme and the Kronecker product. Then based on this model a robust H filter design is proposed to achieve the robust state estimation via solving the linear matrix inequality (LMI). The proposed robust H filter has an efficient ability to attenuate the effect of spatio-temporal external disturbance and measurement noise on the state estimation of LSPDSs from the area energy point of view. Finally, a robust H state estimation example is given for the illustration of design procedure and the performance confirmation of the proposed robust filter design method.

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Circuits and Systems I: Regular Papers, IEEE Transactions on  (Volume:59 ,  Issue: 6 )