Adaptive fuzzy control algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00193029" target="_blank" >RIV/68407700:21230/12:00193029 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive fuzzy control algorithms

Popis výsledku v původním jazyce

In past few years a great progress has been achieved in the area of adaptive control of nonlinear systems. Since a general solution of the corresponding problems is usually hard to find an approximation by fuzzy systems offers sometimes a feasible way how to deal with them. Whereas Mamdani fuzzy systems adopt in most cases the sliding mode control approach or are combined with neural networks, adaptive control of Takagi-Sugeno (TS) fuzzy systems relies mostly on Lyapunov approach. The usual choice for Lyapunov function is a quadratic one, however piecewise quadratic or fuzzy Lyapunov functions that are used outside the adaptive framework offer a great potential for improving the current results. Although control of TS fuzzy systems typically utilizes Parallel Distributed Compensation (PDC) a non-PDC concept can be useful in some cases [4]. All the mentioned approaches lead to Linear Matrix Inequalities solved by convex programming techniques. The idea of using polynomial models in the

Název v anglickém jazyce

Adaptive fuzzy control algorithms

Popis výsledku anglicky

In past few years a great progress has been achieved in the area of adaptive control of nonlinear systems. Since a general solution of the corresponding problems is usually hard to find an approximation by fuzzy systems offers sometimes a feasible way how to deal with them. Whereas Mamdani fuzzy systems adopt in most cases the sliding mode control approach or are combined with neural networks, adaptive control of Takagi-Sugeno (TS) fuzzy systems relies mostly on Lyapunov approach. The usual choice for Lyapunov function is a quadratic one, however piecewise quadratic or fuzzy Lyapunov functions that are used outside the adaptive framework offer a great potential for improving the current results. Although control of TS fuzzy systems typically utilizes Parallel Distributed Compensation (PDC) a non-PDC concept can be useful in some cases [4]. All the mentioned approaches lead to Linear Matrix Inequalities solved by convex programming techniques. The idea of using polynomial models in the

Druh

O - Ostatní výsledky

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

<a href="/cs/project/GAP103%2F12%2F1187" target="_blank" >GAP103/12/1187: Identifikace stochastických, nelineárních systémů pro pokročilé řízení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů