107,389 research outputs found
Integrated fault estimation and accommodation design for discrete-time Takagi-Sugeno fuzzy systems with actuator faults
No full textThis paper addresses the problem of integrated robust
fault estimation (FE) and accommodation for discrete-time
Takagi–Sugeno (T–S) fuzzy systems. First, a multiconstrained
reduced-order FE observer (RFEO) is proposed to achieve FE for
discrete-time T–S fuzzy models with actuator faults. Based on the
RFEO, a new fault estimator is constructed. Then, using the information
of online FE, a new approach for fault accommodation
based on fuzzy-dynamic output feedback is designed to compensate
for the effect of faults by stabilizing the closed-loop systems. Moreover,
the RFEO and the dynamic output feedback fault-tolerant
controller are designed separately, such that their design parameters
can be calculated readily. Simulation results are presented to
illustrate our contributions
On the interpretation and identification of dynamic Takagi-Sugenofuzzy models
No full textDynamic Takagi-Sugeno fuzzy models are not always easy to interpret, in particular when they are identified from experimental data. It is shown that there exists a close relationship between dynamic Takagi-Sugeno fuzzy models and dynamic linearization when using affine local model structures, which suggests that a solution to the multiobjective identification problem exists. However, it is also shown that the affine local model structure is a highly sensitive parametrization when applied in transient operating regimes. Due to the multiobjective nature of the identification problem studied here, special considerations must be made during model structure selection, experiment design, and identification in order to meet both objectives. Some guidelines for experiment design are suggested and some robust nonlinear identification algorithms are studied. These include constrained and regularized identification and locally weighted identification. Their usefulness in the present context is illustrated by examples
On-line evolution of Takagi-Sugeno fuzzy models
No full textEvolving Takagi-Sugeno (eTS) fuzzy models and the method for their on-line identification has been recently introduced for both MISO and MIMO case. In this paper, the mechanism for rule-base evolution, one of the central points of the algorithm together with the recursive clustering and modified recursive least squares (RLS) estimation, is studied in detail. Different scenarios are considered for the rule base upgrade and modification. The radius of influence of each fuzzy rule is considered to be a vector instead of a scalar as in the original eTS approach, allowing different areas of the data space to be covered by each input variable. Simulation results using a well-known benchmark (Mackey-Glass chaotic time-series prediction) are presented. Copyright © 2004 IFA
New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems
No full textThis paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use
On-line identification of MIMO evolving Takagi-Sugeno fuzzy models
No full textEvolving Takagi-Sugeno (eTS) fuzzy models and the method for their on-line identification has been recently introduced as an effective tool for design of flexible system models with minimum a priori information. Their structure develops on-line during the process of model identification itself. In this paper, this approach has been extended for the case of multi-input multi-output (MIMO) system model. Both parts of the identification algorithm, namely the unsupervised fuzzy rule-base antecedents learning by a recursive, noniterative clustering, and the supervised linear sub-model parameters learning by Kalman-filtering-based procedure, are extended for the MIMO case. The radius of influence of each fuzzy rule is considered a vector instead of a scalar as in the original eTS approach, allowing different areas of the data space to be covered by each input variable. As in the eTS, in MIMO eTS, the rule-base and parameters of the fuzzy model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. Simulation results using a well-known benchmark are considered in this paper. Further investigation concern the application of MIMO eTS to predictive modeling of the speech spectrum magnitude, classification of multi-channel source modulation etc. (c) IEEE Pres
H ? filtering for stochastic singular fuzzy systems with time-varying delay
No full textThis paper considers the H? filtering problem
for stochastic singular fuzzy systems with timevarying
delay. We assume that the state and measurement
are corrupted by stochastic uncertain exogenous
disturbance and that the system dynamic is modeled
by Ito-type stochastic differential equations. Based on
an auxiliary vector and an integral inequality, a set of
delay-dependent sufficient conditions is established,
which ensures that the filtering error system is e?t -
weighted integral input-to-state stable in mean (iISSiM).
A fuzzy filter is designed such that the filtering
error system is impulse-free, e?t -weighted iISSiM and
the H? attenuation level from disturbance to estimation
error is belowa prescribed scalar.Aset of sufficient
conditions for the solvability of the H? filtering problem
is obtained in terms of a new type of Lyapunov
function and a set of linear matrix inequalities. Simulation
examples are provided to illustrate the effectiveness
of the proposed filtering approach developed in
this paper
Funciones de Takagi Generalizadas
No full textTesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Matemáticas, leída el 23-02-2024La función de Takagi es, probablemente, el ejemplo más sencillo de una función continua no derivable en ningún punto. En notación moderna, la función de Takagi T : [0,1]→R se define comoT (x) =∞Xn=012n φ¡2nx¢(T)siendo φ(x) la distancia del punto x al entero más cercano. Es importante destacar que la definición original dada por T. Takagi en [102] es completamente diferente a la presentada anteriormente. Probablemente, este hecho junto con el aislamiento de Japón a principios del siglo XX, justifican el paso inadvertido de la función de Takagi en el mundo occidental. Como consecuencia, la función de Takagi fue redescubierta a lo largo del siglo XX por numerosos autores como B. van der Waerden [107], R. Tambs-Lyche [104], G. de Rham [94]y T. H. Hildebrandt [68] entre otros...The Takagi function is probably the simplest example of a continuous nowhere differentiable function. In modern notation, it is defined asT (x) =∞Xn=012n φ¡2nx¢, x ∈ [0,1], (T) where φ(x) is the distance from the point x to the nearest integer. It is important to note that the original definition given by T. Takagi in [102] is entirely different from the one presented above. Probably this, along with Japan’s isolation in the early 20th century, led to the overlooked status of the Takagi function for several decades in the Western World. As a result, the Takagi function was rediscovered throughout the 20th century by numerous authors such as B. L. Van der Waerden [107], R. Tambs-Lyche [104], G. de Rham [94], and T.H. Hildebrandt [68], among others...Fac. de Ciencias MatemáticasTRUEunpu
Lantern slide 'Daibutsu at Kamadura'
No full textSlide frame marked, 'Daibutsu at Kamakura. By T. Takagi, Kobe'Japa
- …
