Open AccessSimultaneous Unknown Input and State Estimation for the Linear System with a Rank-Deficient Distribution Matrix
Author(s) -
Hongjian Yu,
Na Wang,
Kaihong Zhao
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/6693690
Subject(s) - recursive filter , rank (graph theory) , mathematics , matrix (chemical analysis) , filter (signal processing) , control theory (sociology) , feedthrough , state (computer science) , variance (accounting) , distribution (mathematics) , algorithm , mathematical optimization , filter design , computer science , artificial intelligence , root raised cosine filter , combinatorics , engineering , mathematical analysis , materials science , control (management) , accounting , electrical engineering , business , composite material , computer vision
The classical recursive three-step filter can be used to estimate the state and unknown input when the system is affected by unknown input, but the recursive three-step filter cannot be applied when the unknown input distribution matrix is not of full column rank. In order to solve the above problem, this paper proposes two novel filters according to the linear minimum-variance unbiased estimation criterion. Firstly, while the unknown input distribution matrix in the output equation is not of full column rank, a novel recursive three-step filter with direct feedthrough was proposed. Then, a novel recursive three-step filter was developed when the unknown input distribution matrix in the system equation is not of full column rank. Finally, the specific recursive steps of the corresponding filters are summarized. And the simulation results show that the proposed filters can effectively estimate the system state and unknown input.