A novel on-line observer/Kalman filter identification method and its application to input-constrained active fault-tolerant tracker design for unknown stochastic systems

Wu, Chia‐Yi; Tsai, Jason Sheng‐Hong; Guo, Shu‐Mei; Shieh, Leang-San; Canelon, Jose I.; Ebrahimzadeh, Faezeh; Wang, Li

doi:10.1016/j.jfranklin.2014.12.004

Cited by 26 publications

(16 citation statements)

References 37 publications

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“…A partir dos parâmetros de Markov estimados " Y i yu (k), a estimação de cada submodelo local inverso, referente a regra i, é obtida pelo algoritmo de realização de autossistemas, ou simplesmente ERA (do inglês Eigensystems Realization Algorithm) (Juang, 1994;Chen, 1999), a m de estimar submodelos de realização mínima de ordem reduzida na forma canônica do observador Wu et al (2015), os quais são necessários para o mapeamento inverso do sistema dinâmico em cada ponto de operação. Assim, a matriz de Hankel, em função dos parâmetros de Markov, é dada por: pr×g(m+r) , e p ≥ 0 e g ≥ 0 são as contantes de observabilidade e controlabilidade, respectivamente, ambas inteiras e arbitrárias.…”

Section: Algoritmo De Realização De Autossistemas (Era)unclassified

“…Assim, a matriz de Hankel, em função dos parâmetros de Markov, é dada por: pr×g(m+r) , e p ≥ 0 e g ≥ 0 são as contantes de observabilidade e controlabilidade, respectivamente, ambas inteiras e arbitrárias. Observa-se que pr < g(m + r) e p ≥ q são condições necessárias para executar o ERA via matriz de Hankel (Juang, 1994;Wu et al, 2015). Logo, calcula-se H i yu (0) e H i yu (1), e em seguida realiza-se a operação de decomposição de valores singulares sobre H i yu (0), para obter a denição da ordem de realização mínima reduzida n min .…”

Section: Algoritmo De Realização De Autossistemas (Era)unclassified

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Identificação Inversa Recursiva Multivariável Baseada em Modelo Nebuloso de Realização Mínima no Espaço de Estados com Observador de Kalman

Serra

Magalhães

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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In this paper, a methodology for inverse fuzzy modeling of multivariable nonlinear dynamic systems, by direct approach, with state space minimal realization, from experimental data, is proposed. The adopted methodology consists of a formulation for online parametric estimation of the inverse fuzzy model, based on the fuzzy and recursive version of the Observer/Kalman Identication algorithm, using the approach of the Gustafson-Kessel fuzzy clustering algorithm. The analysis of the experimental results related to the recursive identication of Helicopter 2DoF illustrate the eciency and applicability of the adopted methodology. Resumo: Neste artigo, uma metodologia para modelagem nebulosa inversa de sistemas não lineares multivariáveis, pela abordagem direta, de realização mínima no espaço de estados, a partir de dados experimentais é proposta. A metodologia adotada consiste numa formulação para estimação paramétrica online do modelo nebuloso inverso, baseada na versão nebulosa e recursiva do algoritmo OKID (Observer/Kalman Identication), usando-se a abordagem do algoritmo de agrupamento nebuloso Gustafson-Kessel. A análise dos resultados experimentais relacionados à identicação recursiva de um Helicópitero 2DoF ilustram a eciência e aplicabilidade da metodologia adotada.

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Section: Algoritmo De Realização De Autossistemas (Era)unclassified

Identificação Inversa Recursiva Multivariável Baseada em Modelo Nebuloso de Realização Mínima no Espaço de Estados com Observador de Kalman

Serra

Magalhães

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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“…In general, for any natural number q; I q denotes the identity matrix of size q. Because of the structure of the extended state vector (8), it is natural to introduce the following block structure of the gain and covariance matrices:…”

Section: Remarkmentioning

confidence: 99%

“…Because of that, there have been increasing research efforts to improve the Kalman filter in order to overcome the nonlinearities and uncertainties. The extended Kalman filter approach has been traditionally used to recursively estimate the states of a nonlinear system corrupted by noise, which has a Gaussian (normal) distribution [6][7][8][9][10][11][12]. The extended Kalman filter uses local linearization to extend the scope of the Kalman filter to the problem of state estimation of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Joint state and parameter robust estimation of stochastic nonlinear systems

Stojanović

Nedić

2015

Intl J Robust & Nonlinear

104

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SUMMARYSuccessful implementation of many control strategies is mainly based on accurate knowledge of the system and its parameters. Besides the stochastic nature of the systems, nonlinearity is one more feature that may be found in almost all physical systems. The application of extended Kalman filter for the joint state and parameter estimation of stochastic nonlinear systems is well known and widely spread. It is a known fact that in measurements, there are inconsistent observations with the largest part of population of observations (outliers). The presence of outliers can significantly reduce the efficiency of linear estimation algorithms derived on the assumptions that observations have Gaussian distributions. Hence, synthesis of robust algorithms is very important. Because of increased practical value in robust filtering as well as the rate of convergence, the modified extended Masreliez-Martin filter presents the natural frame for realization of the joint state and parameter estimator of nonlinear stochastic systems. The strong consistency is proved using the methodology of an associated ODE system. The behaviour of the new approach to joint estimation of states and unknown parameters of nonlinear systems in the case when measurements have non-Gaussian distributions is illustrated by intensive simulations.

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“…The SG algorithm requires lower computational cost, but the RLS algorithm has a faster convergence rate than the SG algorithm [16]. The RLS algorithm has been applied to the identification of various systems [17,18]. For example, on the basis of the work in [19], Jin et al proposed an auxiliary model based recursive least squares algorithm for autoregressive output-error autoregressive systems [20]; and Wang and Tang presented an auxiliary model based recursive least squares algorithm for a class of linear-in-parameter output-error moving average systems [21].…”

Section: Introductionmentioning

confidence: 99%

Coupled Least Squares Identification Algorithms for Multivariate Output-Error Systems

Huang

Ding

2017

Algorithms

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This paper focuses on the recursive identification problems for a multivariate output-error system. By decomposing the system into several subsystems and by forming a coupled relationship between the parameter estimation vectors of the subsystems, two coupled auxiliary model based recursive least squares (RLS) algorithms are presented. Moreover, in contrast to the auxiliary model based recursive least squares algorithm, the proposed algorithms provide a reference to improve the identification accuracy of the multivariate output-error system. The simulation results confirm the effectiveness of the proposed algorithms.

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A novel on-line observer/Kalman filter identification method and its application to input-constrained active fault-tolerant tracker design for unknown stochastic systems

Cited by 26 publications

References 37 publications

Identificação Inversa Recursiva Multivariável Baseada em Modelo Nebuloso de Realização Mínima no Espaço de Estados com Observador de Kalman

Identificação Inversa Recursiva Multivariável Baseada em Modelo Nebuloso de Realização Mínima no Espaço de Estados com Observador de Kalman

Joint state and parameter robust estimation of stochastic nonlinear systems

Coupled Least Squares Identification Algorithms for Multivariate Output-Error Systems

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