Please use this identifier to cite or link to this item: https://doi.org/10.1007/s40815-016-0162-4
Title: Membership-Function-Dependent Stability Analysis and Control Synthesis of Guaranteed Cost Fuzzy-Model-Based Control Systems
Authors: Lam, H.K
Xiao, B 
Yu, Y
Yin, X
Han, H
Tsai, S.-H
Chen, C.-S
Keywords: Control system analysis
Control system synthesis
Control systems
Control theory
Controllers
Cost benefit analysis
Cost functions
Costs
Linear matrix inequalities
Matrix algebra
Membership functions
Nonlinear feedback
Piecewise linear techniques
System stability
Dependent stability
Fuzzy controllers
Fuzzy model based control
Guaranteed cost
Linear quadratic cost function
Lyapunov stability theory
Minimization of costs
Stability analysis
Control system stability
Issue Date: 2016
Citation: Lam, H.K, Xiao, B, Yu, Y, Yin, X, Han, H, Tsai, S.-H, Chen, C.-S (2016). Membership-Function-Dependent Stability Analysis and Control Synthesis of Guaranteed Cost Fuzzy-Model-Based Control Systems. International Journal of Fuzzy Systems 18 (4) : 537-549. ScholarBank@NUS Repository. https://doi.org/10.1007/s40815-016-0162-4
Rights: Attribution 4.0 International
Abstract: This paper focuses on the guaranteed cost stability analysis of fuzzy-model-based (FMB) control systems. Representing the nonlinear plant using a Takagi–Sugeno (T–S) fuzzy model, a fuzzy controller is employed to close the feedback loop. A weighted linear quadratic cost function is considered as the cost index to measure the performance of the closed-loop fuzzy system in terms of the system states, system outputs, and control signals. The stability of the FMB control system is investigated by the Lyapunov stability theory subject to the minimization of cost index for performance realization. A membership-function-dependent approach using the piecewise-linear membership functions is employed to include the information of membership functions into the stability analysis. Membership-function-dependent stability conditions in terms of linear matrix inequalities are obtained to determine the system stability and feedback gains with the consideration of the system performance measured by the cost function. A simulation example is provided to illustrate the effectiveness and merits of the proposed approach. © 2016, The Author(s).
Source Title: International Journal of Fuzzy Systems
URI: https://scholarbank.nus.edu.sg/handle/10635/181351
ISSN: 15622479
DOI: 10.1007/s40815-016-0162-4
Rights: Attribution 4.0 International
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