Adaptive observers for TS fuzzy systems with unknown polynomial inputs

Lendek, Zsófia; Lauber, Jimmy; Guerra, Thierry Marie; Babuška, Robert; Schutter, Bart De

doi:10.1016/j.fss.2010.03.010

Cited by 83 publications

(19 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fuzzy clustering algorithms were developed and tested in serval previous works [21][22][23][24][25], is a benchmark among different methods of fuzzy classification based on minimizing the objective function of the form:…”

Section: Algorithm Of Fuzzy Clusteringmentioning

confidence: 99%

Gas turbine modeling using adaptive fuzzy neural network approach based on measured data classification

Benyounes

Hafaifa

Kouzou

et al. 2016

Mathematics-in-Industry Case Studies

View full text Add to dashboard Cite

The use of gas turbines is widespread in several industries such as; hydrocarbons, aerospace, power generation. However, despite to their many advantages, they are subject to multiple exploitation problem that need to be solved. Indeed, the purpose of the present paper is to develop mathematical models of this industrial system using an adaptive fuzzy neural network inference system. Where the knowledge variables in this complex system are determined from the real time input/output data measurements collected from the plant of the examined gas turbine. It is obvious that the advantage of the neuro-fuzzy modeling is to obtain robust model, which enable a decomposition of a complex system into a set of linear subsystems. On the other side, by focusing on the membership functions for residual generator to get consistent settings based on the used data structure classification and selection, where the main goal is to obtain a robust system information to ensure the supervision of the examined gas turbine.

show abstract

Section: Algorithm Of Fuzzy Clusteringmentioning

confidence: 99%

Gas turbine modeling using adaptive fuzzy neural network approach based on measured data classification

Benyounes

Hafaifa

Kouzou

et al. 2016

Mathematics-in-Industry Case Studies

View full text Add to dashboard Cite

show abstract

“…In addition, to maintain the desired performance, a robust fault estimation observer was designed in [14] based on piecewise Lyapunov functions. Fault-diagnosis schemes for T-S fuzzy model with unmeasurable premise variables were proposed based on a fuzzy PI observer and adaptive observer in [15] and [16], respectively, where faults are considered as unknown inputs in polynomials form. A multi-constrained reduced-order fault estimation observer is designed in [17] for T-S systems with actuator faults.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, adaptive fuzzy observers have been used to estimate disturbances, faults or unmodeled dynamics of practical systems, such that practical nonlinear systems can be better approximated by T-S fuzzy systems. For example, in [14], a fault estimation observer was designed for discrete-time T-S fuzzy systems via piecewise Lyapunov functions, and in [16], states and unknown inputs were estimated simultaneously by the adaptive observer designed for T-S fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fault estimation for T-S fuzzy systems with unmeasurable premise variables

Liu

Wang

et al. 2018

Adv Differ Equ

View full text Add to dashboard Cite

This paper is concerned with the fault estimation problem for a class of Takagi-Sugeno (T-S) fuzzy systems with actuator faults and sensor disturbances. Premise variables of the T-S fuzzy systems are assumed to be unmeasurable such that conventional parallel distributed compensation (PDC) methods are not applicable. A modified adaptive observer is designed to estimate states and fault parameters simultaneously. Finally, a simulation example is presented which shows the effectiveness of the proposed method.

show abstract

“…Interesting work addressing the design aspects for TS controllers exists in the literature: see for example, Tanaka and Wang (2001), Guerra et al (2006), and Lendek et al (2010).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy distributed cooperative tracking for a swarm of unmanned aerial vehicles with heterogeneous goals

Kladis

Menon

Edwards

2015

International Journal of Systems Science

View full text Add to dashboard Cite

This article proposes a systematic analysis for a tracking problem which ensures cooperation amongst a swarm of UAVs, modelled as nonlinear systems with linear and angular velocity constraints, in order to achieve different goals. A distributed Takagi-Sugeno (TS) framework design is adopted for the representation of the nonlinear model of the dynamics of the UAVs. The distributed control law which is introduced is composed of both node and network level information. Firstly feedback gains are synthesised using a Parallel Distributed Compensation (PDC) control law structure, for a collection of isolated UAVs; ignoring communications among the swarm. Then secondly, based on an alternation-like procedure, the resulting feedback gains are used to determine Lyapunov matrices which are utilised at network level to incorporate into the control law the relative differences in the states of the vehicles, and to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is performed using tools from linear control theory: in particular the design criteria are posed as Linear Matrix Inequalities (LMIs). An example based on a UAV tracking scenario is included to outline the efficacy of the approach.

show abstract

Adaptive observers for TS fuzzy systems with unknown polynomial inputs

Cited by 83 publications

References 44 publications

Gas turbine modeling using adaptive fuzzy neural network approach based on measured data classification

Gas turbine modeling using adaptive fuzzy neural network approach based on measured data classification

Adaptive fault estimation for T-S fuzzy systems with unmeasurable premise variables

Fuzzy distributed cooperative tracking for a swarm of unmanned aerial vehicles with heterogeneous goals

Contact Info

Product

Resources

About