Full-state and reduced-order state observer design for a class of linear time-varying systems

2017 ◽  
Author(s):  
L. T. L ◽  
G. R. Duan
Keyword(s):  
Linear Time ◽  
State Observer ◽  
Observer Design ◽  
Time Varying ◽  
Reduced Order ◽  
Full State ◽  
Time Varying Systems ◽  
Order State

scholarly journals Reduced-order state estimation for linear time-varying systems

2009 ◽  
Vol 11 (6) ◽  
pp. 595-609
Author(s):  
In Sung Kim ◽  
Bruno O. S. Teixeira ◽  
Jaganath Chandrasekar ◽  
Dennis S. Bernstein
Keyword(s):  
State Estimation ◽  
Linear Time ◽  
Time Varying ◽  
Reduced Order ◽  
Time Varying Systems ◽  
Order State

Output Adaptive Observers Design for Linear Non-Stationary Systems with Polynomial Parameters

2021 ◽  
Vol 22 (8) ◽  
pp. 404-410
Author(s):  
K. B. Dang ◽  
A. A. Pyrkin ◽  
A. A. Bobtsov ◽  
A. A. Vedyakov ◽  
S. I. Nizovtsev
Keyword(s):  
Linear Time ◽  
State Observer ◽  
Observer Design ◽  
Model Parameters ◽  
Time Varying ◽  
State Variables ◽  
Unknown Constant ◽  
Established Method ◽  
Time Varying Parameters ◽  
True Values

The article deals with the problem of state observer design for a linear time-varying plant. To solve this problem, a number of realistic assumptions are considered, assuming that the model parameters are polynomial functions of time with unknown coefficients. The problem of observer design is solved in the class of identification approaches, which provide transformation of the original mathematical model of the plant to a static linear regression equation, in which, instead of unknown constant parameters, there are state variables of generators that model non-stationary parameters. To recover the unknown functions of the regression model, we use the recently well-established method of dynamic regressor extension and mixing (DREM), which allows to obtain monotone estimates, as well as to accelerate the convergence of estimates to the true values. Despite the fact that the article deals with the problem of state observer design, it is worth noting the possibility of using the proposed approach to solve an independent and actual estimation problem of unknown time-varying parameters.

scholarly journals Parametric design to reduced-order functional observer for linear time-varying systems

2021 ◽  
pp. 002029402110211
Author(s):  
Da-Ke Gu ◽  
Li-Song Sun ◽  
Yin-Dong Liu
Keyword(s):  
Parametric Design ◽  
Linear Time ◽  
Design Flow ◽  
Observation Error ◽  
Time Varying ◽  
Nonsingular Transformation ◽  
Reduced Order ◽  
Functional Observer ◽  
Time Varying Systems ◽  
Generalized Sylvester Equation

This article studies the parametric design of reduced-order functional observer (ROFO) for linear time-varying (LTV) systems. Firstly, existence conditions of the ROFO are deduced based on the differentiable nonsingular transformation. Then, depending on the solution of the generalized Sylvester equation (GSE), a series of fully parameterized expressions of observer coefficient matrices are established, and a parametric design flow is given. Using this method, the observer can be constructed under the expected convergence speed of the observation error. Finally, two numerical examples are given to verify the correctness and effectiveness of this method and also the aircraft control problem.

scholarly journals Hybrid Dynamic Traffic Model for Freeway Flow Analysis Using a Switched Reduced-Order Unknown-Input State Observer

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1609 ◽  
Author(s):  
Yuqi Guo ◽  
Bin Li ◽  
Matthew Daniel Christie ◽  
Zongzhi Li ◽  
Miguel Angel Sotelo ◽  
...  
Keyword(s):  
Count Data ◽  
Management Strategies ◽  
Flow Analysis ◽  
State Observer ◽  
Observer Design ◽  
State Transitions ◽  
Input State ◽  
Reduced Order ◽  
Traffic Count ◽  
Order State

This paper introduces a new methodology for reconstructing vehicle densities of freeway segments by utilizing the limited data collected by traffic-counting sensors and developing a macroscopic traffic stream model formulated as a switched reduced-order state observer design problem with unknown or partially known inputs. Specifically, the traffic network is modeled as a hybrid dynamic system in a state space that incorporates unknown inputs. For freeway segments with traffic-counting sensors installed, vehicle densities are directly computed using field traffic count data. A reduced-order state observer is designed to analyze traffic state transitions for freeway segments without field traffic count data to indirectly estimate the vehicle densities for each freeway segment. A simulation-based experiment is performed applying the methodology and using data of a segment of Beijing Jingtong freeway in Beijing, China. The model execution results are compared with the field data associated with the same freeway segment, and highly consistent results are achieved. The proposed methodology is expected to be adopted by traffic engineers to evaluate freeway operations and develop effective management strategies.

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