scholarly journals Simultaneous Unknown Input and State Estimation for the Linear System with a Rank-Deficient Distribution Matrix

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yu Hua ◽  
Na Wang ◽  
Keyou Zhao
Keyword(s):  
Minimum Variance ◽  
Full Column Rank ◽  
Unbiased Estimation ◽  
Unknown Input ◽  
System State ◽  
Input Distribution ◽  
System Equation ◽  
Distribution Matrix ◽  
Simulation Results ◽  
Output Equation

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.

scholarly journals An Effective Adaptive Combination Strategy for Distributed Learning Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying
Keyword(s):  
Minimum Variance ◽  
Iteration Scheme ◽  
Estimation Problem ◽  
Lms Algorithm ◽  
Unbiased Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Combination Strategy ◽  
Adaptive Combination ◽  
Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

scholarly journals Time-Varying Wind Load Identification Based on Minimum-Variance Unbiased Estimation

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Huili Xue ◽  
Kun Lin ◽  
Yin Luo ◽  
Hongjun Liu
Keyword(s):  
Damping Ratio ◽  
Estimation Method ◽  
Minimum Variance ◽  
Wind Load ◽  
Ar Model ◽  
Unbiased Estimation ◽  
Recursive Identification ◽  
Time Varying ◽  
Building Structures ◽  
Shear Building

A minimum-variance unbiased estimation method is developed to identify the time-varying wind load from measured responses. The formula derivation of recursive identification equations is obtained in state space. The new approach can simultaneously estimate the entire wind load and the unknown structural responses only with limited measurement of structural acceleration response. The fluctuating wind speed process is investigated by the autoregressive (AR) model method in time series analysis. The accuracy and feasibility of the inverse approach are numerically investigated by identifying the wind load on a twenty-story shear building structure. The influences of the number and location of accelerometers are examined and discussed. In order to study the stability of the proposed method, the effects of the errors in crucial factors such as natural frequency and damping ratio are discussed through detailed parametric analysis. It can be found from the identification results that the proposed method can identify the wind load from limited measurement of acceleration responses with good accuracy and stability, indicating that it is an effective approach for estimating wind load on building structures.

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