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Aircraft parameter estimation by using the optimal input design and linear matrix inequalities

IFAC Proceedings Volumes 2003年第16期36卷 657-662页

作者： Carine Jauberthie Lilianne Denis-Vidal Ghislaine Joly-Blanchard University of Technology Compiègne Dpt GI BP 20 529 60205 Compiègne France fax (33)244 23 44 77 Univ. Sciences and Tech. Lille. UFR Math(M2) 59655 Villeneuve d'Ascq France fax (33)320 43 69 93 University of Technology Compiègne Dpt GI DP 20 529 60205 Compiègne France fax (33)344 23 44 77

System identification based on physical laws often involves parameter estimation. Even if parameters are theoretically identifiable, they may be poorly estimable for a given experiment. Thus a significant increase in accuracy of the parameter estimation may be obtained by a suitable choice of experimental conditions. The original idea of this paper is the combination of a dynamical programming method with a gradient algorithm in the solution of the optimal design. After getting an optimal input, the parameter estimation is performed by minimizing a weighted least square criterion. Weights are either based on the known measurement noise or given by the solution of a linear matrix inequality problem.

关键词： Non-linear systems Control systems Input design Parameter estimation identification algorithm

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Parameter Estimation of Fish Behavior Model by Using Water Tank Experiment Data

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IFAC Proceedings Volumes 1987年第5期20卷 327-332页

作者： N. Sannomiya K. Matuda M. Nouri-Shirazi K. Ishizaki Department of Electronics Kyoto Institute of Technology Matsugasaki Sakyo-ku Kyoto 606 Japan Tokyo University of Fisheries Konan Minato-ku Tokyo 108 Japan Electrical and Instrumentation Department Nippon Steel Corporation Edamitsu Yawatahigashi-ku Kitakyushu 805 Japan

A method is presented for modeling the behavior of fish in a water tank. The motion of fish is described mathematically by nonlinear state equations, in which the main causes for the motion are expressed as the components of the external force. A water tank experiment is carried out for obtaining the observation records of fish behavior. The time series data of the position for each fish is calculated from the image data recorded by a video tape. With regard to the situation of fish behavior records, a robust parameter estimation algorithm is proposed by applying the probabilistic data association approach to the least squares algorithm and the usual robust algorithm. As a case study, the parameters included in the model are identified by using the proposed algorithm as well as the least squares algorithm.

关键词： Parameter estimation identification algorithm Physical modeling Applications of system identification Fishery

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identification of Nonlinear Static Processes with Local Polynomial Regression and Subset Selection

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IFAC Proceedings Volumes 2009年第10期42卷 138-143页

作者： Heiko Sequenz Alexander Schreiber Rolf Isermann Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University Darmstadt Germany (Tel: +49-6151-16 3914

The presented method for nonlinear system identification is based on the LOLIMOT algorithm introduced by Nelles and Isermann [1996]. The LOLIMOT algorithm divides the input space by a tree-construction algorithm and interpolates the local linear models by local membership functions. Instead of assuming local linear models, the presented algorithm utilizes general local nonlinear functions, which make the algorithm more flexible. These are approximated by a multidimensional Taylor series. Since the amount of regressors grows fast with the number of inputs and the expansion order, a subset selection procedure is introduced. It reveals significant regressors and gives information about the local functional behavior. The local subset selection is implemented as a stepwise regression with replacement of regressors. Mallows’ C p -statistic is used for the subset selection algorithm and is also implemented for final model selection. The benefit of the extended algorithm lies in the higher flexibility in the local models, which results in less partitions of the input space by a similar approximation quality.

关键词： Local Regression Subset Selection LOLIMOT identification algorithm Multivariate Polynomials Least-Squares Estimation Automotive Emissions

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Efficient identification and Accurate Evaluation for Shutdown Operation Flow of Cryogenic Air Separation Units

Efficient Identification and Accurate Evaluation for Shutdow...

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第35届中国过程控制会议

作者： Duowen Chen Guanghui Yang Hongfeng Lou Xiongfeng Feng Zuhua Xu Zhijiang Shao Department of Polytechnic Zhejiang University State Key Lab of Industrial Control Technology Department of Control Science and Engineering Zhejiang University

During the operation of air separation units(ASU), planned short-term shutdowns are often required to ensure a balance between supply and demand of downstream air separation products, and maintain equipment performance and ensure production safety. Making the device shut down smoothly and safely is a key measure in air separation production. However, this process highly relies on manual participation and carries risks and uncertainties. In response to this practical demand, we propose an ASU Shutdown operation flow assistance system(SOFAS). We firstly build a standard logical model based on the actual shutdown process and develop algorithms to assist in executing standardized and safe shutdown operations. Then, we propose an operation flow identification algorithm to identify the shutdown operation flow, and an evaluation algorithm to quantitatively analyze the efficiency and safety of the operation flow using two performance indicators. Finally, the effectiveness of the method was verified through experimental analysis using the actual operational data of ASU.

关键词： Air Separation Unit Shut down identification algorithm Evaluation algorithm Operation flow

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Parameter estimation using a novel nonlinear constrained sequential state estimator

Parameter estimation using a novel nonlinear constrained seq...

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UKACC International Conference on Control 2010

作者： Stephen C. Stubberud Kathleen A. Kramer Allen R. Stubberud Oakridge Technology Del Mar CA USA University of San Diego San Diego CA USA University of California Irvine CA USA

ISBN: (纸本)9781627481199

A sequential state estimation routine was developed to allow for the incorporation of constraints into their estimates. In this effort, the application of this powerful state estimator to the problem where parameters of the system model are incorporated into the state vector is examined. The research has looked at the issues that arise in both the open-loop implementation, such as occurs in the target tracking application, and the closed-loop implementation that occurs in the feedback control problem. This effort is aimed toward system identification of parameters that have hard limits on their values. This type of parameter estimation can provide the foundation for a training paradigm for elliptical basis functions in neural networks.

关键词： Constrained estimator Constrained parameters Estimation algorithm identification algorithm Parameter estimation State estimation Parameter estimation Systems modelling Sequential state vectors System identification feedback control Neural network

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Recursive and Iterative Stochastic Gradient algorithms Based on Two-Step Update Estimation for Wiener Systems

Recursive and Iterative Stochastic Gradient Algorithms Based...

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Asian Control Conference

作者： Rui-Qi Dong Yi-Yang Zhang Ying Zhang Harbin Institute of Technology Shenzhen Graduate School Shenzhen P. R. China

ISBN: (纸本)9781509015740;9781509015733

In this paper, a recursive stochastic gradient algorithm based on two-step update estimation and an iterative stochastic gradient algorithm based on two-step update estimation are established for the Wiener system by introducing a relaxation factor, which controls the relative importance of the two estimation parts. In addition, the convergence performance of the proposed SG-TSU and ISG-TSU algorithms are then analyzed. It is shown by a numerical example that if the relaxation factor is appropriately chosen, the proposed SG-TSU and ISG-TSU algorithms converge more quickly and have higher convergence precision than the standard SG and ISG algorithms, respectively.

关键词： Stochastic gradient identification algorithm Wiener system Two-step update estimation

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Investigation on Early-stage Internal Short Circuit identification for Power Battery Pack

Investigation on Early-stage Internal Short Circuit Identifi...

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2019 2nd International Conference on Sustainable Energy, Environment and Information Engineering(SEEIE 2019)

作者： Xu Zhang Yue Pan Enhua Wang Minggao Ouyang Languang Lu Xuebing Han Guoqing Jin Anjian Zhou Huiqian Yang School of Mechanical Engineering Beijing Institute of Technology State Key Laboratory of Automotive Safety and Energy Tsinghua University Chongqing Changan New Energy Vehicle Technology Co. Ltd

The spontaneous internal short circuit is a very important way for the thermal runaway of lithium-ion battery. identification of the internal short circuit accurately and in time during the working process is critical for the safety of lithium-ion battery. In this study, a modified algorithm for the early-stage internal short circuit identification is investigated. This method is based on the mean-difference model and the recursive least square algorithm. An equivalent experiment is setup to validate the feasibility of this method for the identification of early-stage internal short circuit. The results indicate that this method can identify the internal short circuit only when the SOC is very low. For ordinary operation conditions of the power battery pack, this method is not suitable for early-stage internal short circuit identification.

关键词： lithium-ion battery internal short circuit identification algorithm mean-difference model early stage

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Guaranteed Non-Asymptotic Confidence Ellipsoids for FIR Systems

Guaranteed Non-Asymptotic Confidence Ellipsoids for FIR Syst...

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IEEE Annual Conference on Decision and Control

作者： Erik Weyer Balazs Csanad Csaji Marco C. Campi Department of Electrical and Electronic Engineering The University of Melbourne Parkville VIC 3010 Australia MTA SZTAKI: The Institute for Computer Science and Control of the Hungarian Academy of Sciences Kende utca 13-17 H-1111 Budapest Hungary Department of Information Engineering University of Brescia Via Branze 38 25123 Brescia Italy

ISBN: (纸本)9781467357159

Recently, a new finite-sample system identification algorithm, called Sign-Perturbed Sums (SPS), was introduced in [2]. SPS constructs finite-sample confidence regions that are centered around the least squares estimate, and are guaranteed to contain the true system parameters with a user-chosen exact probability for any finite number of data points. The main assumption of SPS is that the noise terms are independent and symmetrically distributed about zero, but they do not have to be stationary, nor do their variances and distributions have to be known. Although it is easy to determine if a particular parameter belongs to the confidence region, it is not easy to describe the boundary of the region, and hence to compactly represent the exact confidence region. In this paper we show that an ellipsoidal outer-approximation of the SPS confidence region can be found by solving a convex optimization problem, and we illustrate the properties of the SPS region and the ellipsoidal outer-approximation in simulation examples.

关键词： Ellipsoids Noise measurement Finite-sample systems identification algorithm

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AN INNOVATION STATE SPACE APPROACH FOR TIME SERIES FORECASTING

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Journal of Time Series Analysis 1993年第6期14卷 589-601页

作者： Libert, Gaëtan Wang, Liang Liu, Bao Faculté Polytechnique de Mons Belgium Tianjin University China

Abstract. An innovation state space modelling approach is presented in which the structures and parameters of a model are determined by an identification algorithm proposed by Tse and Weinert (IEEE Trans. Automat. Contr. 120 (1975), 603–13) and the singular value decomposition technique. This approach is applied to two typical data series to illustrate its use, and its forecasting accuracy is compared with other time series approaches. Copyright © 1993, Wiley Blackwell. All rights reserved

关键词： identification algorithm Innovation state space model singular value decomposition time series forecasting

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