Towards a systematic analysis of fuzzy observers

Palm, Rainer; Driankov, Dimiter

doi:10.1109/nafips.1999.781678

Cited by 17 publications

(8 citation statements)

References 8 publications

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“…For the model (4.1), several types of observers have been considered, including linear observers, fuzzy Luenberger observers (Palm and Driankov, 1999;Bergsten and Palm, 2000), sliding-mode observers Oudghiri et al, 2007), etc. The observer design problem arises as soon as the measurement vector does not coincide with the state vector, i.e., y = x.…”

Section: Observer Design For Ts Systemsmentioning

confidence: 99%

“…The observer (4.2) can be seen as a generalization of the classical Luenberger observer (Luenberger, 1966) to fuzzy systems, and is referred to as a "fuzzy-Luenberger observer" in several publications (Palm and Driankov, 1999;Bergsten and Palm, 2000). In what follows, we refer to it simply as a fuzzy observer.…”

Section: Observer Design For Ts Systemsmentioning

confidence: 99%

“…For details on sliding mode observers, the interested reader is referred to (Palm and Driankov, 1999;Palm and Bergsten, 2000;Oudghiri et al, 2007). For details on sliding mode observers, the interested reader is referred to (Palm and Driankov, 1999;Palm and Bergsten, 2000;Oudghiri et al, 2007).…”

Section: Observer Design For Ts Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Stability Analysis and Nonlinear Observer Design Using Takagi-Sugeno Fuzzy Models

Babuška

Schutter

Lendek

et al. 2011

Studies in Fuzziness and Soft Computing

202

144

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VI PrefaceThe intended audience are graduate students and researchers both from academia and industry. For newcomers to the field, the book provides a concise introduction dynamic TS fuzzy models along with two methods to construct TS models for a given nonlinear system.While this monograph focusses mainly on the theory and methodology of state and parameter estimation in nonlinear distributed dynamic systems, the methods presented can readily be used in applications such as industrial processes, traffic systems, environmental systems, energy and water distribution networks, and so on.Supplementary information relevant to this book is available at the website:http://www.dcsc.tudelft.nl/fuzzybook/ Comments, suggestions, or questions concerning the book or the website are welcome. Interested readers are encouraged to get in touch with the authors using the contact information on the website.

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Section: Observer Design For Ts Systemsmentioning

confidence: 99%

Section: Observer Design For Ts Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Stability Analysis and Nonlinear Observer Design Using Takagi-Sugeno Fuzzy Models

Babuška

Schutter

Lendek

et al. 2011

Studies in Fuzziness and Soft Computing

202

144

View full text Add to dashboard Cite

show abstract

“…The so-called "ThauLuenberger fuzzy observer" [19] has been widely used to estimate the state variables of TS fuzzy nonlinear systems [22], [24]. In general, the sector function vector z may be constituted of the system's states, inputs, or measurements.…”

Section: B Lmi Based Ts Fuzzy Observermentioning

confidence: 99%

Gradient-based Takagi-Sugeno fuzzy local observer

Beyhan

Dilmen

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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In this paper, a recently introduced nonlinear gradient-based observer [1] has been adopted for Takagi-Sugeno (TS) fuzzy systems. The designed observer is especially aimed to estimate the unmeasurable states of the TS fuzzy systems where the LMI solution is not feasible to find the observer gains. The estimation of gradient observer is evaluated based on the Levenberg-Marquardt direction where the local convergence property is guaranteed using Lyapunov function approach. The numerical simulations present accurate estimation results for TS fuzzy nonlinear systems including a comparison with the conventional Extended Kalman Filter (EKF) yielding acceptable results.

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“…For this reason, it is interesting to consider the case where the weighting functions depend on unmeasurable variables such as the state of the system, and thus, the weighting functions in the observer will depend on the estimated state. In this context, there are few works, nevertheless, we can cite [16], [17] and [18], where the authors proposed an observer, using the assumption of Lipschitz property of the considered perturbed term. The conditions of convergence of the observer are expressed in terms of Linear Matrix Inequalities (LMI) [19].…”

Section: B Problem Statementmentioning

confidence: 99%

Robust observer design for uncertain Takagi-Sugeno model with unmeasurable decision variables: an L₂ approach

Ichala

Marx

Ragot

et al. 2008

2008 16th Mediterranean Conference on Control and Automation

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This paper deals with the problem of state estimation of nonlinear uncertain systems described by uncertain multiple model form with unmeasurable decision variables. We propose two methods to attenuate the effect of modeling uncertainties and measurement noise on the state estimation. The first method is based, under some assumptions, on the second method of Lyapunov and L 2 approach. The second method allows to reduce the conservatism of the convergence conditions issued from the assumptions of first method. The convergence conditions of the observer are presented in terms of linear matrix inequality (LMI) formulation. The validity and applicability of the proposed methods are illustrated by an academic example.

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Towards a systematic analysis of fuzzy observers

Cited by 17 publications

References 8 publications

Stability Analysis and Nonlinear Observer Design Using Takagi-Sugeno Fuzzy Models

Stability Analysis and Nonlinear Observer Design Using Takagi-Sugeno Fuzzy Models

Gradient-based Takagi-Sugeno fuzzy local observer

Robust observer design for uncertain Takagi-Sugeno model with unmeasurable decision variables: an L₂ approach

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Towards a systematic analysis of fuzzy observers

Cited by 17 publications

References 8 publications

Stability Analysis and Nonlinear Observer Design Using Takagi-Sugeno Fuzzy Models

Stability Analysis and Nonlinear Observer Design Using Takagi-Sugeno Fuzzy Models

Gradient-based Takagi-Sugeno fuzzy local observer

Robust observer design for uncertain Takagi-Sugeno model with unmeasurable decision variables: an L2 approach

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Robust observer design for uncertain Takagi-Sugeno model with unmeasurable decision variables: an L₂ approach