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Robust Kalman smoother for EIV state space model based on multivariate least trimmed squares estimator

IMA JOURNAL OF MATHEMATICAL CONTROL AND INFORMATION 2012年第1期29卷 23-32页

作者： AlMutawa, Jaafar King Fahd Univ Petr & Minerals Dept Math Sci Dhahran 31261 Saudi Arabia

This paper derives a robust Kalman smoother estimate for the errors-in-variables state space model that is less sensitive to outliers in the sense of the multivariate least trimmed squares (MLTS) method. Since the MLTS estimate is a combinatorial optimization problem, the randomized algorithm has been proposed. However, the uniform sampling method has a high computational cost and may lead to a biased estimate. Therefore, we apply the subsampling method. The algorithm presented here is both efficient and easy to implement. A Monte Carlo simulation result shows the efficiency of the proposed algorithm.

关键词： errors-in-variables state space model multivariate least trimmed squares Kalman filter and smoother outliers random search algorithm subsampling method

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ROBUST KALMAN FILTER AND SMOOTHER FOR ERRORS-IN-VARIABLES STATE SPACE MODELS WITH OBSERVATION OUTLIERS BASED ON THE MINIMUM-COVARIANCE DETERMINANT ESTIMATOR

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ASIAN JOURNAL OF CONTROL 2011年第4期13卷 513-521页

作者： Almutawa, Jaafar King Fahd Univ Petr & Minerals Dept Math & Stat Dhahran 31261 Saudi Arabia

In this paper, we propose a robust Kalman filter and smoother for the errors-in-variables (EIV) state space models subject to observation noise with outliers. We introduce the EIV problem with outliers and then present the minimum covariance determinant (MCD) estimator which is a highly robust estimator in terms of protecting the estimate from the outliers. Then, we propose the randomized algorithm to find the MCD estimate. However, the uniform sampling method has a high computational cost and may lead to biased estimates, therefore we apply the sub-sampling method. A Monte Carlo simulation result shows the efficiency of the proposed algorithm.

关键词： Errors-in-variables model state space models minimum covariance determinant Kalman filter and smoother outliers random search algorithm sub-sampling method

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A Timed-Based Approach for Genetic algorithm: Theory and Applications

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2011年第6期E94D卷 1306-1320页

作者： Mehrafsa, Amir Sokhandan, Alireza Karimian, Ghader Univ Tabriz Elect & Comp Engn Dept Tabriz Iran Univ Tabriz Dept Elect Tabriz Iran

In this paper, a new algorithm called TGA is introduced which defines the concept of time more naturally for the first time. A parameter called TimeToLive is considered for each chromosome, which is a time duration in which it could participate in the process of the algorithm. This will lead to keeping the dynamism of algorithm in addition to maintaining its convergence sufficiently and stably. Thus, the TGA guarantees not to result in premature convergence or stagnation providing necessary convergence to achieve optimal answer. Moreover, the mutation operator is used more meaningfully in the TGA. Mutation probability has direct relation with parent similarity. This kind of mutation will decrease ineffective mating percent which does not make any improvement in offspring individuals and also it is more natural. Simulation results show that one run of the TGA is enough to reach the optimum answer and the TGA outperforms the standard genetic algorithm.

关键词： genetic algorithms time unit time to live population generator crossover probability GAVaPS premature convergence random search algorithm

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Monte Carlo simulation via a numerical algorithm for solving a nonlinear inverse problem

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2010年第9期15卷 2436-2444页

作者： Farnoosh, R. Ebrahimi, M. Iran Univ Sci & Technol Narmak Sch Math Tehran 16846 Iran

This paper is intended to provide a numerical algorithm involving the combined use of the finite differences scheme and Monte Carlo method for estimating the diffusion coefficient in a one-dimensional nonlinear parabolic inverse problem. In the present study, the functional form of the diffusion coefficient is unknown a priori. The unknown diffusion coefficient is approximated by the polynomial form and the present numerical algorithm is employed to find the solution. To modify the values of estimated coefficients of this polynomial form, we introduce a random search algorithm in Monte Carlo method for global optimization. A numerical test is performed in order to show the efficiency and accuracy of the present work. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Nonlinear inverse problem Finite differences scheme Monte Carlo optimization Diffusion coefficient random search algorithm

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Identification of errors-in-variables state space models with observation outliers based on minimum covariance determinant

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JOURNAL OF PROCESS CONTROL 2009年第5期19卷 879-887页

作者： AlMutawa, Jaafar King Fahd Univ Petr & Minerals Dept Math & Stat Dhahran 31261 Saudi Arabia

In this paper, a subspace system identification algorithm for the errors-in-variables (EIV) state space models subject to observation noise with outliers has been developed. By using the minimum covariance determinant (MCD) estimator, the outliers have been identified and deleted. Then the classical EIV subspace system identification algorithms have been applied to estimate the parameters of the state space models. In order to solve the MCD problem for the EIV state space models, a random search algorithm has been proposed. A Monte-Carlo simulation results demonstrate the effectiveness of the proposed algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Subspace system identification Errors-in-variables State space models Outliers Minimum covariance determinant random search algorithm

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External linearisation of a MEMS electrothermal scanner with application in a confocal microscope

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MICRO & NANO LETTERS 2009年第2期4卷 106-111页

作者： Poland, S. P. Girkin, J. M. Li, L. Uttamchandani, D. Univ Strathclyde Inst Photon Glasgow Lanark Scotland Univ Durham Dept Phys Durham DH1 3HP England Univ Strathclyde Ctr Microsyst & Photon Glasgow Lanark Scotland

A compensatory mapping (CM) technique for external linearisation of a MEMS electrothermally actuated optical scanner is reported. Each individual axis of a two-axis MEMS scanner was linearised by the CM technique, and the scanner was then incorporated into a confocal optical microscope configuration. Crosstalk in the scanner when both axes are driven simultaneously, and arising when a drive signal to one axis of the scanner produces a mechanical response in the orthogonal scan axis, was subsequently compensated for by using a look-up-table optimised using a random search algorithm.

关键词： MEMS electrothermal actuated optical scanner random processes Optical shutters, windows, diaphragms, deflectors, choppers, and scanners lookup-table Micromechanical and nanomechanical devices and systems Optical microscopy random search algorithm two-axis MEMS scanner external linearisation confocal optical microscope configuration optical scanners compensatory mapping technique table lookup optical microscopes micromechanical devices MEMS and NEMS device technology linearisation techniques

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Robust Kalman filter and smoother for errors-in-variables model with observation outliers

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IFAC Proceedings Volumes 2008年第2期41卷 456-461页

作者： Jaafar ALMutawa Department of Mathematical Sciences King Fahd University of Petroleum and Minerals PO Box 158 Dhahran 31261 Saudi Arabia

In this paper, we propose a robust Kalman filter and smoother for the errors-invariables (EIV) state space model subject to observation noise with outliers. We introduce the EIV problem with outliers and then we present the minimum covariance determinant (MCD) estimator which is highly robust estimator to detect outliers. As a result, a new statistical test to check the existence of outliers which is based on the Kalman filter and smoother has been formulated. Since the MCD is a combinatorial optimization problem the randomized algorithm has been proposed in order to achieve the optimal estimate. However, the uniform sampling method has a high computational cost and may lead to biased estimate, therefore we apply the sub-sampling method. A Monte Carlo simulation result shows the efficiency of the proposed algorithm.

关键词： Errors-in-variables model minimum covariance determinant Kalman filter and smoother outliers random search algorithm sub-sampling method

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Robust synthesis of a PID controller by uncertain multimodel approach

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INFORMATION SCIENCES 2007年第6期177卷 1441-1451页

作者： Toscano, R. ENISE ECL CNRS UMR5513Lab Tribol & Dynam Syst F-42023 St Etienne 2 France

This paper presents an effective method to design a PID (or PI) controller for nonlinear systems where desirable robustness and performance properties must be maintained across a large range of operating conditions. For this purpose, an uncertain multimodel of the original nonlinear system is used. The uncertainties affecting the system are treated as stochastic matrices. Based on this multimodel representation a robust PID controller can be designed in order to obtain acceptable performance for all operating conditions. Numerical examples show the practical applicability of the proposed method. (c) 2006 Elsevier Inc. All rights reserved.

关键词： PID controller multimodel multi-PID parametric uncertainties robust stability random search algorithm

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Identification of errors-in-variables model with observation outliers based on minimum-covariance-determinant

Identification of errors-in-variables model with observation...

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26th American Control Conference

作者： Almutawa, J. King Fahd Univ Petr & Minerals Fac Mathemat Sci Dept Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781424409884

In this paper, we develop a subspace system identification algorithm for the errors-in-variables (EIV) model subject to observation noise with outliers. By using the minimum covariance determinant (MCD), we identify and delete the outliers, and then apply the classical EIV subspace system identification algorithms to get state space models. In order to solve the MCD problem for the EIV model we propose a random search algorithm. The proposed algorithm has been applied to a heat exchanger data.

关键词： subspace system identification errors-in-variables model outliers minimum covariance determinant random search algorithm

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A simple method to find a robust output feedback controller by random search approach

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ISA TRANSACTIONS 2006年第1期45卷 35-44页

作者： Toscano, R CNRS Lab Tribol & Dynam Syst UMR 5513 ECLENISE F-42023 St Etienne 2 France

This paper presents a simple but effective method for finding a robust output feedback controller via a random search algorithm. The convergence of this algorithm can be guaranteed. Moreover, the probability to find a solution as well as the number of random trials can be estimated. The robustness of the closed-loop system is improved by the minimization of a given cost function reflecting the performance of the controller for a set of plants. Simulation studies are used to demonstrate the effectiveness of the proposed method. (c) 2006 ISA-The Instrumentation, Systems, and Automation Society.

关键词： output feedback pole placement random search algorithm robustness condition number cost function NP-hard problem

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