scholarly journals Design of Estimation Algorithms from an Innovation Approach in Linear Discrete-Time Stochastic Systems with Uncertain Observations

10.5772/45777 ◽  
2012 ◽  
Author(s):  
J. Linares-Perez ◽  
R. Caballero-Aguila ◽  
I. Garcia-Garrido
Keyword(s):  
Discrete Time ◽  
Stochastic Systems ◽  
Estimation Algorithms ◽  
Innovation Approach

scholarly journals Optimal Fusion Filtering in Multisensor Stochastic Systems with Missing Measurements and Correlated Noises

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
R. Caballero-Águila ◽  
I. García-Garrido ◽  
J. Linares-Pérez
Keyword(s):  
Stochastic Systems ◽  
Linear Filter ◽  
Missing Measurements ◽  
Process Noise ◽  
Estimation Algorithms ◽  
Optimal Linear ◽  
One Step ◽  
Optimal Fusion ◽  
Innovation Approach ◽  
Correlated Noises

The optimal least-squares linear estimation problem is addressed for a class of discrete-time multisensor linear stochastic systems with missing measurements and autocorrelated and cross-correlated noises. The stochastic uncertainties in the measurements coming from each sensor (missing measurements) are described by scalar random variables with arbitrary discrete probability distribution over the interval[0,1]; hence, at each single sensor the information might be partially missed and the different sensors may have different missing probabilities. The noise correlation assumptions considered are (i) the process noise and all the sensor noises are one-step autocorrelated; (ii) different sensor noises are one-step cross-correlated; and (iii) the process noise and each sensor noise are two-step cross-correlated. Under these assumptions and by an innovation approach, recursive algorithms for the optimal linear filter are derived by using the two basic estimation fusion structures; more specifically, both centralized and distributed fusion estimation algorithms are proposed. The accuracy of these estimators is measured by their error covariance matrices, which allow us to compare their performance in a numerical simulation example that illustrates the feasibility of the proposed filtering algorithms and shows a comparison with other existing filters.

scholarly journals Finite-time estimation algorithms for LPV discrete-time systems with application to output feedback stabilization

Automatica ◽  
2021 ◽  
Vol 125 ◽  
pp. 109436
Author(s):  
Khadidja Chaib-Draa ◽  
Ali Zemouche ◽  
Fazia Bedouhene ◽  
Rajesh Rajamani ◽  
Yan Wang ◽  
...  
Keyword(s):  
Discrete Time ◽  
Output Feedback ◽  
Finite Time ◽  
Time Estimation ◽  
Feedback Stabilization ◽  
Estimation Algorithms ◽  
Output Feedback Stabilization ◽  
Discrete Time Systems ◽  
Time Systems

pth moment $${\cal D}$$-stability/stabilization of linear discrete-time stochastic systems

2021 ◽  
Vol 65 (3) ◽  
Author(s):  
Huasheng Zhang ◽  
Jianwei Xia ◽  
Yining Zhang ◽  
Hao Shen ◽  
Zhen Wang
Keyword(s):  
Discrete Time ◽  
Stochastic Systems

scholarly journals Fuzzy Stabilization for Nonlinear Discrete Ship Steering Stochastic Systems Subject to State Variance and Passivity Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Wen-Jer Chang ◽  
Bo-Jyun Huang ◽  
Po-Hsun Chen
Keyword(s):  
Linear Matrix Inequality ◽  
Discrete Time ◽  
Stochastic Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Sufficient Conditions ◽  
Control Technique ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Ship Steering

For nonlinear discrete-time stochastic systems, a fuzzy controller design methodology is developed in this paper subject to state variance constraint and passivity constraint. According to fuzzy model based control technique, the nonlinear discrete-time stochastic systems considered in this paper are represented by the discrete-time Takagi-Sugeno fuzzy models with multiplicative noise. Employing Lyapunov stability theory, upper bound covariance control theory, and passivity theory, some sufficient conditions are derived to find parallel distributed compensation based fuzzy controllers. In order to solve these sufficient conditions, an iterative linear matrix inequality algorithm is applied based on the linear matrix inequality technique. Finally, the fuzzy stabilization problem for nonlinear discrete ship steering stochastic systems is investigated in the numerical example to illustrate the feasibility and validity of proposed fuzzy controller design method.

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