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.

scholarly journals A Two-Step State Estimation Algorithm for Hybrid AC-DC Distribution Grids

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1967
Author(s):  
Gaurav Kumar Roy ◽  
Marco Pau ◽  
Ferdinanda Ponci ◽  
Antonello Monti
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
Renewable Energy Sources ◽  
Estimation Algorithm ◽  
The State ◽  
Second Step ◽  
Distribution Grid ◽  
Dc Distribution ◽  
Attractive Option ◽  
Distribution Grids

Direct Current (DC) grids are considered an attractive option for integrating high shares of renewable energy sources in the electrical distribution grid. Hence, in the future, Alternating Current (AC) and DC systems could be interconnected to form hybrid AC-DC distribution grids. This paper presents a two-step state estimation formulation for the monitoring of hybrid AC-DC grids. In the first step, state estimation is executed independently for the AC and DC areas of the distribution system. The second step refines the estimation results by exchanging boundary quantities at the AC-DC converters. To this purpose, the modulation index and phase angle control of the AC-DC converters are integrated into the second step of the proposed state estimation formulation. This allows providing additional inputs to the state estimation algorithm, which eventually leads to improve the accuracy of the state estimation results. Simulations on a sample AC-DC distribution grid are performed to highlight the benefits resulting from the integration of these converter control parameters for the estimation of both the AC and DC grid quantities.

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.

scholarly journals Applied problems in frame theory

2019 ◽  
Author(s):  
◽  
Sara Botelho-Andrade
Keyword(s):  
State Estimation ◽  
Phase Retrieval ◽  
Approximate Solutions ◽  
The State ◽  
Estimation Problem ◽  
Frame Theory ◽  
Quantum Detection ◽  
Standard Linear ◽  
Measurement Vector ◽  
Equivalent Conditions

This thesis is a study of two applied problems in frame theory: phase retrieval and quantum detection. These problems are inspired by engineering applications in signal processing and information theory. ... The goal of the injectivity problem is to classify frames which are injective with respect to self-adjoint Hilbert-Schmidt operators. By associating vectors x [is an element of]H[superscript n] with vectors [median]x in a larger space, we are able to use standard linear algebra and functional analysis techniques to provide characterizations for the injectivity problem in complex and real Hilbert spaces, as well as construct solutions. Given an injective frame, the goal of the state estimation problem is to construct a self-adjoint trace one operator T such that the vector with coordinates less than Tx[subscript k], x[subscript k]> is equal to a predetermined measurement vector. We give equivalent conditions for solvability of the state estimation problem and provide best approximate solutions when no exact solution is possible. We also show results about density of both problems.

scholarly journals Estimation Of State Variables And Model Parameters For The Evolution Of COVID-19 In The City Of Rio de Janeiro

2020 ◽  
Author(s):  
Helcio R.B. Orlande ◽  
Marcelo Colaco ◽  
George S. Dulikravich ◽  
Luiz F.S. Ferreira
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Rio De Janeiro ◽  
Reproduction Number ◽  
The State ◽  
Estimation Problem ◽  
Evolution Model ◽  
Model Parameters ◽  
State Variables ◽  
The City

Evolution model is based on that used by Hernandez et al., which considers the following groups: Susceptible, Incubating, Asymptomatic, Symptomatic, Hospitalized, Recovered and Accumulated deaths. Evolution model considers the possibility of infections from asymptomatic, symptomatic and hospitalized individuals. Evolution model considers the possibility that individuals who have recovered from the disease become symptomatic again. Observation model accounts for underreport of cases and deaths. Observation model accounts for delays in reporting cases and deaths. Model parameters were initially estimated with the Markov Chain Monte Carlo (MCMC) method, by using the data of the city of Rio de Janeiro from February 28, 2020 to April 29, 2020. These estimations were used as initial input values for the solution of the state estimation problem for the city of Rio de Janeiro. Algorithm of Liu & West for the Particle Filter was used for the solution of the state estimation problem because it allows the simultaneous estimation of state variables and model parameters. State estimation problem was solved with the data of the city of Rio de Janeiro, from February 28, 2020 to May 05, 2020. Monte Carlo simulations were run for 20 future days, considering uncertainties in the model parameters and state variables. Initial conditions were given by the state variables and corresponding distributions estimated with the particle filter on May 05, 2020. Distributions of the model parameters were also given by the estimations obtained for this date. Data of the city of Rio de Janeiro, from May 06, 2020 to May 15, 2020, were used for the validation of the solution of the state estimation problem. The present model, with the parameters obtained with the Particle Filter, accurately fits the number of reported cases and the number of reported deaths, for 10 days ahead of the period used for the solution of the state estimation problem. The Ratio of Infected Individuals per Reported Cases was around 15 on May 05, 2020. The Indexes of Under-Reported Cases and Deaths were around 12 and 2, respectively, on May 05, 2020. The Effective Reproduction Number was around 1.6 on February 28, 2020 and dropped to around 0.9 on May 05, 2020. However, uncertainties related to this parameter are large and the effective reproduction number is between 0.3 and 1.5, at the 95% credibility level. The particle filter must be used to periodically update the estimation of state variables and model parameters, so that future predictions can be made. Day 0 is February 28, 2020.

scholarly journals Applying Set Membership Strategy in State of Charge Estimation for Lithium-ion Battery

2022 ◽  
Vol 21 ◽  
pp. 1-19
Author(s):  
Wang Jianhong ◽  
Ricardo A. Ramirez-Mendoza
Keyword(s):  
State Estimation ◽  
State Space ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
External Noise ◽  
The State ◽  
State Of Charge ◽  
Battery Management System ◽  
Set Membership ◽  
State Of Charge Estimation

As state of charge is one important variable to monitor the later battery management system, and as traditional Kalman filter can be used to estimate the state of charge for Lithium-ion battery on basis of probability distribution on external noise. To relax this strict assumption on external noise, set membership strategy is proposed to achieve our goal in case of unknown but bounded noise. External noise with unknown but bounded is more realistic than white noise. After equivalent circuit model is used to describe the Lithium-ion battery charging and discharging properties, one state space equation is constructed to regard state of charge as its state variable. Based on state space model about state of charge, two kinds of set membership strategies are put forth to achieve the state estimation, which corresponds to state of charge estimation. Due to external noise is bounded, i.e. external noise is in a set, we construct interval and ellipsoid estimation for state estimation respectively in case of external noise is assumed in an interval or ellipsoid. Then midpoint of interval or center of the ellipsoid are chosen as the final value for state of charge estimation. Finally, one simulation example confirms our theoretical results.

A Solution to the State Estimation Problem of Systems with Unknown Inputs

2012 ◽  
Vol 5 (2) ◽  
pp. 102-112 ◽  
Author(s):  
Hamidreza Bolandhemmat ◽  
Christopher Clark ◽  
Farid Golnaraghi
Keyword(s):  
State Estimation ◽  
The State ◽  
Estimation Problem ◽  
Unknown Inputs

State and Parametric Estimation of Nonlinear Systems Described by Wiener Sate-Space Mathematical Models

2015 ◽  
pp. 107-145 ◽  
Author(s):  
Houda Salhi ◽  
Samira Kamoun
Keyword(s):  
Nonlinear Systems ◽  
Mathematical Models ◽  
State Estimation ◽  
Estimation Algorithm ◽  
The State ◽  
Parametric Estimation ◽  
State Variables ◽  
Estimation Algorithms ◽  
And Performance ◽  
The Stability

This chapter deals with the description, the parametric estimation, the state estimation, and the parametric and state estimation conjointly of nonlinear systems. The focus is on the class of nonlinear systems, which are described by Wiener state-space discrete-time mathematical models. Thus, the authors develop a new recursive parametric estimation algorithm, which is based on least squares techniques. The stability conditions of the developed parametric estimation scheme are analyzed using the Lyapunov method. The state estimation problem of the considered nonlinear systems is formulated. Thus, the authors propose a recursive state estimation algorithm, which is based on Kalman Filter. A new recursive algorithm is proposed, which permits one to estimate conjointly the parameters and the state variables of nonlinear systems described by Wiener mathematical models, with unknown parameters and state variables. The efficiency and performance of the proposed recursive estimation algorithms are tested on numerical simulation examples.

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