State estimation problem for the detection of valve closure in gas pipelines

2021 ◽  
pp. 1-21
Author(s):  
Italo M. Madeira ◽  
Mabel A. R. Lucumi ◽  
Helcio R. B. Orlande
Keyword(s):  
State Estimation ◽  
Estimation Problem ◽  
Valve Closure ◽  
Gas Pipelines

scholarly journals Data rate limits for the remote state estimation problem

2020 ◽  
Vol 53 (2) ◽  
pp. 4955-4960
Author(s):  
C. Kawan ◽  
A. Matveev ◽  
A. Pogromsky
Keyword(s):  
State Estimation ◽  
Data Rate ◽  
Estimation Problem ◽  
Remote State Estimation

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

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.

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

2018 ◽  
Vol 56 (2) ◽  
pp. 105-123 ◽  
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.

Sensor Fusion of Odometer, Compass and Beacon Distance for Mobile Robots

2020 ◽  
Vol 10 (1) ◽  
pp. 1-17
Author(s):  
Rufus Fraanje ◽  
René Beltman ◽  
Fidelis Theinert ◽  
Michiel van Osch ◽  
Teade Punter ◽  
...  
Keyword(s):  
State Estimation ◽  
Sensor Fusion ◽  
Simulation Experiment ◽  
Estimation Problem ◽  
Unknown Input ◽  
Fusion Algorithm ◽  
Process Noise ◽  
Distance Measurements ◽  
Differential Drive ◽  
Filter Solution

The estimation of the pose of a differential drive mobile robot from noisy odometer, compass, and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment.

Traffic State Estimation in Heterogeneous Networks with Stochastic Demand and Supply: Mixed Lagrangian–Eulerian Approach

2019 ◽  
Vol 2673 (11) ◽  
pp. 114-126 ◽  
Author(s):  
Mohammadreza Kavianipour ◽  
Ramin Saedi ◽  
Ali Zockaie ◽  
Meead Saberi
Keyword(s):  
State Estimation ◽  
Network Traffic ◽  
Ground Truth ◽  
Solution Algorithm ◽  
Estimation Problem ◽  
Demand And Supply ◽  
Traffic State ◽  
Probe Vehicles ◽  
Loop Detectors ◽  
Traffic State Estimation

A network fundamental diagram (NFD) represents the relationship between network-wide average flow and average density. Network traffic state estimation to observe NFD when congestion is heterogeneously distributed, as a result of a time-varying and asymmetric demand matrix, is a challenging problem. Recent studies have formulated the NFD estimation problem using both fixed measurements and probe trajectories. They are often based on a given ground-truth NFD for a single day demand. Stochastic variations in network demand and supply may significantly affect the approximation of an NFD. This study proposes a modified framework to estimate network traffic states to observe NFD while capturing the stochasticity in transportation networks. A mixed integer problem with non-linear constraints is formulated to address stochasticity in the NFD estimation problem. To solve this Nondeterministic Polynomial-hard (NP-hard) problem, a solution algorithm based on the simulated annealing method is applied. The problem is formulated and the solution algorithm is implemented to find an optimal configuration of loop detectors and probe vehicles to estimate the NFD of the Chicago downtown network and capture its day-to-day variations, considering a given available budget. Ground-truth NFDs and estimated NFDs based on a subset of loop detectors and probe vehicles are calculated using a simulation-based dynamic traffic assignment model, which is the best surrogate available to replicate real-world conditions. The main contribution of this study is to capture stochasticity in the demand and supply sides to find a more robust subset of links and trajectories to be acquired for the NFD estimation.

Solution of Three Dimensional State Estimation Problem in Rf Hyperthermia and Using Nanoparticles

2015 ◽  
Author(s):  
Leonardo Antonio Bermeo Varón ◽  
Helcio Rangel Barreto Orlande ◽  
Guillermo Enrique Eliçabe
Keyword(s):  
State Estimation ◽  
Three Dimensional ◽  
Estimation Problem
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