Journal ArticleDOI
Limit-cycle analysis of dynamic fuzzy control systems
Jau-Woei Perng
- Vol. 17, Iss: 9, pp 1553-1561
TLDR
The main purpose of this study is to predict limit cycles of a dynamic fuzzy control system by combining a stability equation, describing function and parameter plane, and the suppression of the limit cycle by adjusting control parameters.Abstract:
The main purpose of this study is to predict limit cycles of a dynamic fuzzy control system by combining a stability equation, describing function and parameter plane. The stability of a linearized dynamic fuzzy control system is then analyzed using stability equations and the parameter plane method, with the assistance of a describing function method. This procedure identifies the amplitude and frequency of limit cycles that are clearly formed by the dynamic fuzzy controller in the parameter plane. Moreover, the suppression of the limit cycle by adjusting control parameters is proposed. Continuous and sampled-data systems are addressed, and the proposed approach can easily be extended to a fuzzy control system with multiple nonlinearities. Simulations are performed to demonstrate the effectiveness of the proposed scheme.read more
Citations
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Adaptive least square control in discrete time of robotic arms
TL;DR: The uniform stability of the tracking error and parameters error for the aforementioned controller is guaranteed by means of a Lyapunov-like analysis.
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On the stability of fuzzy dynamical systems
Moiseis dos Santos Cecconello,Rodney Carlos Bassanezi,Adilson J. V. Brandão,Jefferson Cruz dos Santos Leite +3 more
TL;DR: This work studies the asymptotic behavior of fuzzy solutions obtained by using Zadeh's extension at the deterministic solutions of initial value problems and obtains some result regarding the existence of fuzzy equilibrium points that generalize the already known results.
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Stability analysis of dynamic nonlinear interval type-2 TSK fuzzy control systems based on describing function
Zahra Namadchian,Assef Zare +1 more
TL;DR: This paper focuses on the limit cycles prediction problem to discuss the stability analysis of dynamic nonlinear interval type-2 Takagi–Sugeno–Kang fuzzy control systems with adjustable parameters.
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On fuzzy uncertainties on the logistic equation
TL;DR: This paper considers the population density at a specific time as a fuzzy variable in which the possibility distribution function depends on theossibility distribution functions of the environmental carrying capacity, the initial population density and the intrinsic growth rate.
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Fuzzy sliding mode control design based on stability margins
TL;DR: A method to design FSMC algorithms with desired PM and GM is presented and a step-by-step process to tune the FSMC parameters was provided to validate the proposed method.
References
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Journal ArticleDOI
Parameter space methods for robust control design: a guided tour
TL;DR: The results obtained in studies of robust stability and stabilizability of control systems with parametric (structured) uncertainties are reviewed in this paper, where both the algebraic methods based upon characteristic equations and the methods using Lyapunov functions and Riccati equations are discussed and compared.
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Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity
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Journal ArticleDOI
Dynamic parallel distributed compensation for Takagi-Sugeno fuzzy systems: An LMI approach
TL;DR: A unified systematic framework for designing dynamic feedback controllers for nonlinear systems described by Takagi–Sugeno (T–S) models is presented, which can be applied to hybrid or switching systems.