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A fast moving horizon estimation algorithm based on nonlinear programming sensitivity

JOURNAL OF PROCESS CONTROL 2008年第9期18卷 876-884页

作者： Zavala, Victor M. Laird, Carl D. Biegler, Lorenz T. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

Moving horizon estimation (MHE) is an efficient optimization-based strategy for state estimation. Despite the attractiveness of this method, its application in industrial settings has been rather limited. This has been mainly due to the difficulty to solve, in real-time, the associated dynamic optimization problems. In this work, a fast MHE algorithm able to overcome this bottleneck is proposed. The strategy exploits recent advances in nonlinear programming algorithms and sensitivity concepts. A detailed analysis of the optimality conditions of MHE problems is presented. As a result, strategies for fast covariance information extraction from general nonlinear programming algorithms are derived. It is shown that highly accurate state estimates can be obtained in large-scale MHE applications with negligible on-line computational costs.. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： estimation algorithms Real-time Large-scale Nonlinear programming Sensitivity analysis

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Vehicle Trajectory estimation Using a High-Gain Multi-Output Nonlinear Observer

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第6期25卷 5733-5742页

作者： Alai, Hamidreza Zemouche, Ali Rajamani, Rajesh Univ Minnesota Twin Cities Dept Mech Engn Minneapolis MN 55455 USA Univ Lorraine CRAN UMR7039 Nancy France

This paper focuses on the design of a multi-output high gain observer for a vehicle trajectory tracking application. Tracking the trajectories of other vehicles on the road is needed for many applications ranging from collision avoidance to autonomous driving. Previously, such trajectory tracking has been done using linearized dynamic models, interacting-multiple-model (IMM) filters, or else by using LMI-based nonlinear observers. These estimation techniques suffer from some crucial shortcomings. Hence, this paper develops a high gain nonlinear observer for this application. The high gain observer approach offers the advantages of guaranteed feasibility and stability with just one constant observer gain for a wide range of motion. The challenges of transforming the vehicle dynamic model into the required companion form for applying the high gain observer technique are addressed. A coordinate transformation that allows for varying velocity and varying slip angle is shown to be appropriate. The high gain observer methodology for a dynamic system with multiple outputs is presented. Finally, simulation and experimental results on vehicle tracking are demonstrated. The experimental results show that, with a high gain observer, vehicle trajectories that span a large range of orientations can be accurately tracked using just one constant observer gain.

关键词： Observers Radar tracking Vehicle dynamics Trajectory Stability criteria Laser stability Kalman filters Vehicle trajectory estimation vehicle control filters estimation algorithms nonlinear observer e-scooter

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Parameter estimation of fuzzy controller and its application to inverted pendulum

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2004年第1期17卷 37-60页

作者： Oh, SK Pedrycz, W Rho, SB Ahn, TC Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G6 Canada Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland Wonkwang Univ Sch Elect & Elect Engn Iksan 570749 Chon Buk South Korea

In this paper, a new approach to estimate scaling factors of the fuzzy PID controller is presented. The performance of the fuzzy PID controller is sensitive to the variety of scaling factors. The design procedure dwells on the use of evolutionary computing (more specifically, a genetic algorithm) and estimation algorithm. The tuning of the scaling factors of the fuzzy PID controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy PID controller by means of three types of estimation algorithms such as HCM (Hard C-Means) clustering-based regression polynomial, neuro-fuzzy networks, and regression polynomials. Numerical studies are presented in detail along with a detailed comparative analysis. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： fuzzy PID/PD controller estimation algorithms HCM (Hard C-Means) HCM clustering based regression polynomial neuro-fuzzy networks (NFN) model

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Comparative study of lithium-ion battery open-circuit-voltage online estimation methods

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IET ELECTRICAL SYSTEMS IN TRANSPORTATION 2020年第2期10卷 162-169页

作者： Meng, Jianwen Boukhnifer, Moussa Diallo, Demba Ecole Ingenieurs ESTACA 12 Ave Paul Delouvrier F-78066 St Quentin En Yveline France Sorbonne Univ Univ Paris Saclay Univ Paris Sud GeePs Grp Elect Engn ParisCNRSCent Supelec 3 & 11 Rue Joliot Curie F-91192 Gif Sur Yvette France Univ Lorraine LCOMS EA 7306 F-57000 Metz France Shanghai Maritime Univ Dept Elect Automat Shanghai 201306 Peoples R China

As an important property and distinct characteristic of different lithium-ion batteries, open-circuit-voltage (OCV) online estimation can provide useful information for battery monitoring and fault diagnosis. However, studies dedicated to battery OCV estimation are not as much as the research efforts on state-of-charge determination and parameter identification such as capacity and resistance. Hence, a general discussion for selecting the battery OCV estimation algorithm is proposed in this study. To this end, modelling process of extended state-space model and autoregressive exogenous model is presented in detail. Four estimation algorithms, namely, Luenberger observer, Kalman filter, recursive least-square with forgetting factor and recursive least-square with variable forgetting factor are selected and compared in terms of estimation accuracy, computational cost, parameter tuning and robustness to parameter variations. Based on real battery cell parameters and environmental conditions, simulation results have shown that even if they are less robust to model uncertainty, observer-based methods exhibit better estimation performances than regression-based ones.

关键词： recursive estimation autoregressive processes battery management systems least squares approximations secondary cells fault diagnosis Kalman filters state-space methods observers lithium compounds environmental conditions computational cost variable forgetting factor recursive least-square Kalman filter Luenberger observer modelling process battery OCV estimation algorithm lithium-ion battery open-circuit-voltage online estimation methods parameter identification state-of-charge determination fault diagnosis battery monitoring battery cell parameters estimation accuracy estimation algorithms autoregressive exogenous model extended state-space model

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Non-quadratic quality criteria in parameter estimation of continuous-time models

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IET CONTROL THEORY AND APPLICATIONS 2011年第13期5卷 1494-1508页

作者： Kowalczuk, Z. Kozlowski, J. Gdansk Univ Technol Dept Decis Syst Fac Elect Telecommun & Informat PL-80933 Gdansk Poland

A consequent and consistent continuous-time approach to system parameter estimation is introduced. estimation algorithms, the underlying quality criteria and models of identified systems are described in the continuous-time domain, while suitable discretising operations are performed solely for the purpose of ultimate numerical realisation of estimation procedures. The considered indices of estimation quality take the form of integrals of absolute prediction errors rather than a common form of integrals or sums of square errors. In order to overcome the problem of analytical minimisation of such non-differentiable criteria, an approximate method is derived and applied in practical implementation of the resultant estimation schemes. Specific weighting mechanisms utilised in the algorithms allow for tracking the time-variant parameters of non-stationary systems, while with the employed instrumental variable the accuracy of estimates gets improved by means of suppression of the asymptotic bias. Following the so-called direct approach, an auxiliary discrete-time model that retains 'physical' parameterisation is obtained based on 'finite-horizon' spline-based integration of both sides of the presumed differential equation. In this aspect, application of splines makes the respective discrete-time processing resistant to cumulation of numerical errors. The attached numerical examples demonstrate the performance of the discussed estimation routines.

关键词： identified systems continuous-time models Optimisation techniques differential equations Other topics in statistics estimation quality splines numerical errors estimation procedures square errors absolute prediction errors nonquadratic quality criteria analytical minimisation system parameter estimation infinite horizon resultant estimation schemes continuous-time approach physical parameterisation weighting mechanisms discussed estimation routines time-variant parameters auxiliary discrete-time model discrete-time processing resistant estimation algorithms splines (mathematics) ultimate numerical realisation Mathematical analysis asymptotic bias nonstationary systems Interpolation and function approximation (numerical analysis) continuous time systems Optimal control minimisation Discrete control systems continuous-time domain nondifferentiable criteria differential equation finite-horizon spline-based integration parameter estimation discrete time systems asymptotic stability

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estimation of Particulate Backscattering Coefficient in Turbid Inland Water Using Sentinel 3A-OLCI Image

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 8577-8593页

作者： Xu, Jiafeng Bian, Yingchun Lyu, Heng Miao, Song Li, Yunmei Liu, Huaiqing Xu, Jie Nanjing Normal Univ Key Lab Virtual Geog Environm Educ Minist Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Invocat Geog Informat Reso Nanjing 210023 Peoples R China

The particulate backscattering coefficient (b(bp)) plays an important role in the underwater light field. However, it is difficult to accurately estimate b(bp)(lambda) in turbid inland water with complex optical properties. To accurately estimate the backscattering coefficients in inland water, a simple classification method based on the shape of remote sensing reflectance was first proposed to distinguish two water types (i.e., water type 1 and water type 2) with different backscattering characteristics. Then, trigonometric functions were developed to simulate the backscattering coefficients at all bands in water type 1 and the backscattering coefficients in the visible band of water type 2, whereas a linear function was built to estimate the backscattering coefficients in the near-infrared band of water type 2. The proposed algorithm was compared with four state-of-the-art methods and validated by an independently measured dataset of three lakes in the middle and lower reaches of the Yangtze River in 2020. The results showed that the proposed algorithm performed well in inland waters, with all mean absolute percentage errors < 40% and root-mean-square errors < 0.25 m(-1). Finally, the algorithm was applied to Ocean and Land Color Instrument images from 2016 to 2020 in Lake Taihu and Lake Hongze. It was found that the backscattering coefficients in Lake Taihu and Lake Hongze showed opposite seasonal variation trends, and the b(bp)(676) in Lake Hongze began to decrease since 2017, whereas no obvious interannual variation was observed in Taihu Lake in recent five years.

关键词： Backscatter Lakes Water Remote sensing Reflectivity Atmospheric measurements estimation estimation algorithms inland waters ocean and land color instrument (OLCI) images particulate backscattering coefficient

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Development of an Autonomous Measurement System for estimation of Snow Profile on Road Surfaces

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第9期72卷 11169-11183页

作者： Espindola, Andre Rajamani, Rajesh Univ Minnesota Dept Mech Engn 111 Church St SE Minneapolis MN 55455 USA

This paper presents the development of a novel prototype system to measure the profile of snow and detect ice on road surfaces. While researchers have previously developed methods to estimate the tire-road friction coefficient, methods to create a high resolution map of snow cover and ice on roads have not been developed. The prototype and data processing methods presented in this study provide a potential solution that would greatly benefit winter road maintenance teams. Having a device capable of creating a detailed map of snow and ice cover on roads would allow for more efficient plowing and salt deployment, better informing of the public about current road conditions, as well as better analysis of current plowing techniques to improve winter road maintenance operations. The measurement of the snow profile is based on the use of two types of inexpensive ranging sensors mounted on a rotationally controlled stepper motor platform. The technical challenges addressed in the development include analysis of the scan path over which the system scans the road, differentiation between asphalt, concrete, and snow surfaces using reflection amplitudes, accurate transformation of raw distance measurements to inertial coordinates, and identification of the Euler angles of the sensors using parameters associated with the scanned surface profile. Extensive experimental results and test data are presented, which show that the system is capable of measuring the snow profile on a road with an accuracy of +/- 1 cm when a reasonable portion of bare road is visible, and +/- 2 cm when only small portions of bare road are visible.

关键词： estimation algorithms ice detection laser sensors snow profile winter road maintenance

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A stochastic approach to linear estimation in H_∞

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AUTOMATICA 1998年第4期34卷 405-414页

作者： Ninness, B Univ Newcastle Dept Elect & Comp Engn Callaghan NSW 2308 Australia

This paper examines the problem of system identification from frequency response data. Recent approaches to this problem, known collectively as 'estimation in H-infinity', involve deterministic descriptions of noise corruptions to the data. In order to provide 'worst-case' convergence with respect to these deterministic noise descriptions, non-linear data algorithms are required. In contrast, this paper examines 'worst-case' estimation in H-infinity when the disturbances are subject to mild stochastic assumptions and linearity in the data algorithms is employed. Issues of convergence, error bounds, and model order selection are considered. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： estimation theory estimation algorithms system identification robust estimation error analysis

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Model-based design of optimal experiments for nonlinear systems in the context of guaranteed parameter estimation

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COMPUTERS & CHEMICAL ENGINEERING 2017年 99卷 198-213页

作者： Mukkula, Anwesh Reddy Gottu Paulen, Radoslav Tech Univ Dortmund Proc Dynam & Operat Grp Emil Figge Str 70 D-44227 Dortmund Germany

An approach to the design of experiments is presented in the framework of bounded-error (guaranteed) parameter estimation for nonlinear static and dynamic systems. The guaranteed parameter estimation determines non-asymptotic confidence limits on the unknown parameters of a mathematical model. An essential part of the solution procedure is the approximation of the joint-confidence region. In this contribution, we develop and analyze the procedure and different ways of achieving a tight over-approximation of the solution set of guaranteed parameter estimation based on the expected values of parameters. Finally we propose to solve the problem of the design of experiments as a bilevel program. We demonstrate our approach and analyze the nature of the problem in the static and dynamic case studies. The proposed approach is also compared to the experiment design in the context of least-squares estimation and to the linearization-based techniques for optimal experiment design proposed in the literature earlier. (C) 2017 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Optimal experiment design estimation algorithms Parameter estimation Bounded noise Bounded-error estimation

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Least squares estimation techniques for position tracking of radioactive sources

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AUTOMATICA 2001年第11期37卷 1727-1737页

作者： Howse, JW Ticknor, LO Muske, KR Los Alamos Natl Lab Modeling Algorithms & Informat Grp Los Alamos NM 87545 USA Los Alamos Natl Lab Stat Grp Los Alamos NM 87545 USA Villanova Univ Dept Chem Engn Villanova PA 19085 USA

This paper describes least squares estimation algorithms used for tracking the physical location of radioactive sources in real time as they are moved around in a facility. We present both recursive and moving horizon nonlinear least squares estimation algorithms that consider both the change in the source location and the deviation between measurements and model predictions. The measurements used to estimate position consist of four count rates reported by four different gamma ray detectors. There is an uncertainty in the source location due to the large variance of the detected count rate, and the uncertainty in the background count rate. This work represents part of a suite of tools which will partially automate security and safety assessments, allow some assessments to be done remotely, and provide additional sensor modalities with which to make assessments. Published by Elsevier Science Ltd.

关键词： state estimation estimation algorithms maximum likelihood estimators

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