Open AccessSampled‐data based quantisation control for T–S fuzzy switched systems with actuator failures dependent on an improved Lyapunov functional method
Author(s) -
Zhao Jianrong,
Liu Wei,
Zhuang Guangming,
Chu Yuming,
Li Yongmin
Publication year - 2018
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.5057
Subject(s) - control theory (sociology) , actuator , mathematics , piecewise , controller (irrigation) , fuzzy logic , dwell time , quantization (signal processing) , sampling (signal processing) , exponential stability , fuzzy control system , asynchronous communication , lyapunov function , computer science , control (management) , nonlinear system , algorithm , artificial intelligence , mathematical analysis , quantum mechanics , physics , medicine , clinical psychology , computer network , filter (signal processing) , agronomy , computer vision , biology
Sampled‐data based quantisation control for Takagi–Sugeno (T–S) fuzzy switched systems with actuator failures under asynchronous switching is studied. By the T–S fuzzy model, sampled‐data based asynchronous control is extended into switched non‐linear systems. An improved functional method is introduced. Applying this method, improved Lyapunov functionals are constructed. These functionals are mode dependent and piecewise continuous; not every term of these functionals is required to be positive definite; sampling states at both sides of sampling intervals are introduced into these functionals. More useful information with respect to system states is reflected in constructing functionals. Combining these functionals with the averaged dwell time technique, stabilisation conditions are relaxed, and improved exponential stabilisation criteria are obtained with less conservatism. In addition, variation ranges of membership functions are taken into account to reduce conservatism. Then, a sampled‐data based fuzzy quantisation controller is designed. Simulation examples are given to illustrate the effectiveness of the proposed results.