On the State Estimation Problem for Stochastic Distributed Parameter Systems with a Semi-Permeable Wall

This work proposes a state estimation strategy over mobile sensor–actuator networks with missing measurements for a class of distributed parameter systems (DPSs) with time-varying delay. Initially, taking advantage of the abstract development equation theory and operator semigroup method, this kind of delayed DPSs described by partial differential equations (PDEs) is derived for evolution equations. Subsequently, the distributed state estimators including consistency component and gain component are designed; the purpose is to estimate the original state distribution of the delayed DPSs with missing measurements. Then, a delay-dependent guidance approach is presented in the form of mobile control forces by constructing an appropriate Lyapunov function candidate. Furthermore, by applying Lyapunov stability theorem, operator semigroup theory, and a stochastic analysis approach, the estimation error systems have been proved asymptotically stable in the mean square sense, which indicates the estimators can approximate the original system states effectively when this kind of DPS has time-delay and the mobile sensors occur missing measurements. Finally, the correctness of control strategy is illustrated by numerical simulation results.

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Set-membership filtering for automatic guided vehicles with unknown-but-bounded noises

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211043666 ◽

2021 ◽

pp. 014233122110436

Author(s):

Hao Yang ◽

Yilian Zhang ◽

Wei Gu ◽

Fuwen Yang ◽

Zhiquan Liu

Keyword(s):

State Estimation ◽

Estimation Algorithm ◽

The State ◽

Estimation Problem ◽

Filtering Method ◽

Automatic Guided Vehicles ◽

Tracking Process ◽

Set Membership ◽

Text Filtering ◽

System States

This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and [Formula: see text] filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.

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Computational development of a practical educational tool for state estimation of power systems using the MATLAB optimization toolbox

International Journal of Electrical Engineering Education ◽

10.1177/0020720918788722 ◽

2018 ◽

Vol 56 (2) ◽

pp. 105-123 ◽

Cited By ~ 1

Author(s):

EA Zamora-Cárdenas ◽

A Pizano-Martínez ◽

JM Lozano-García ◽

VJ Gutiérrez-Martínez ◽

R Cisneros-Magaña

Keyword(s):

Power Systems ◽

State Estimation ◽

Electric Power ◽

Smart Grids ◽

Weighted Least Squares ◽

Electric Power Systems ◽

The State ◽

Estimation Problem ◽

Educational Tool ◽

And Control

State estimation is one of the most important processes to perform a reliable monitoring and control of the steady-state operating condition of modern electric power systems; thus, it is currently a fundamental part in the development of research to enhance the monitoring and security of the smart grids operation. This important topic is taught in advanced courses of operation and control of power systems, for graduate and undergraduate power engineering students. However, the most used software packages for simulation and analysis of power systems by researchers, students, and educators have put little attention on the state estimation module. Due to this fact, this paper proposes an approach to develop the computational implementation of a practical educational tool for state estimation of electric power systems using the MATLAB optimization toolbox. In this proposal, the formulation of the state estimation problem consists of developing a general digital code to implement an objective function based on the weighted least squares method. While the lsqnonlin function of the MATLAB optimization toolbox solves the formulated state estimation problem. Simplifying both research and educational processes, this tool helps graduate and undergraduate students to improve learning, understanding, and the times of implementation and development of research in state estimation. Simulations of an equivalent model of the Mexican interconnected power system consisting of 190 buses and 46 machines are used to test and validate the proposal performance.

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