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Control laws for Takagi–Sugeno fuzzy models
Fuzzy Sets and Systems, 2001Abstract This work presents control laws for fuzzy models of Takagi–Sugeno (TS) (Sugeno and Kang, Fuzzy Sets and Systems 28 (1988) 15–33, Takagi and Sugeno, IEEE Trans. Systems Man Cybernet. 15(1) (1985) 116–132). These laws are not directly put in the classical form called PDC for parallel distributed compensation (Wang et al., IEEE Trans.
Guerra, Thierry Marie +1 more
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2010
In this chapter we first introduce the continuous-time Takagi-Sugeno (TS) fuzzy systems that are employed throughout the book. In the second part of the chapter, we present methods to construct TS models that represent or approximate a nonlinear dynamic system starting from a given model of this system.
Zsófia Lendek +3 more
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In this chapter we first introduce the continuous-time Takagi-Sugeno (TS) fuzzy systems that are employed throughout the book. In the second part of the chapter, we present methods to construct TS models that represent or approximate a nonlinear dynamic system starting from a given model of this system.
Zsófia Lendek +3 more
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Output stabilization of Takagi–Sugeno fuzzy systems
Fuzzy Sets and Systems, 2000The authors discuss a class of Takagi-Sugeno models governed by rules of the form \[ \text{-if } z_1\text{ is }M_{1i} \text{ and\dots and }z_p \text{ is }M_{pi} \text{ then }dx/dt= A_ix(t)+B_i u(t),\;y(t)=C_ix(t) \] (in the above \(x(t)\in \mathbb{R}^n\), \(u(t)\in \mathbb{R}^m\) and \(y(t)\in \mathbb{R}^q\), \(i=1, 2, \dots,r\); \(M_{1i},\dots,M_{pi}\)
Yoneyama, Jun +3 more
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Takagi–Sugeno Fuzzy Modeling Using Mixed Fuzzy Clustering
IEEE Transactions on Fuzzy Systems, 2017This paper proposes the use of mixed fuzzy clustering (MFC) algorithm to derive Takagi–Sugeno (T–S) fuzzy models (FMs). Mixed fuzzy clustering handles both time invariant and multivariate time variant features, allowing the user to control the weight of each component in the clustering process. Two model designs based on MFC are investigated.
Catia M. Salgado +5 more
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Constrained fuzzy controller design of discrete Takagi–Sugeno fuzzy models
Fuzzy Sets and Systems, 2003The contribution of this paper is to provide a method in the design of a discrete fuzzy controller with linear output feedback gains for the discrete-time Takagi-Sugeno fuzzy systems. This method is based on the concept of the PDC (parallel distributed compensation) and it is developed according to a specified common observability Gramian, which can ...
Chang, Wen-Jer, Sun, Chein-Chung
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ON FUZZINESS MEASURES VIA SUGENO'S INTEGRAL
1995We introduce a notion of partial order for fuzzy sets in connection with their greater or smaller fuzziness. This allows us to give a simple basis to theory of fuzziness measures. Sugeno's integral permits to built very large classes of fuzziness measures.
P. BENVENUTI, VIVONA, Doretta, M. DIVARI
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IEEE Conference on Cybernetics and Intelligent Systems, 2004., 2004
This paper deals with approximations of large rule Takagi-Sugeno (TKS) fuzzy controller by four rule TKS fuzzy controller (simpler) via comparison of rule base of two controllers. The inequalities between two controllers are compensated by proposed compensating factors.
R.K. Arya, R. Mitra, V. Kumar
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This paper deals with approximations of large rule Takagi-Sugeno (TKS) fuzzy controller by four rule TKS fuzzy controller (simpler) via comparison of rule base of two controllers. The inequalities between two controllers are compensated by proposed compensating factors.
R.K. Arya, R. Mitra, V. Kumar
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Sensitivity analysis of Takagi–Sugeno fuzzy neural network
Information Sciences, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Jian +4 more
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Takagi-Sugeno Fuzzy Logic Systems
2017The achievements obtained by Fuzzy Logic undoubtedly changed the way expert information is represented, manipulated, and interpreted in computational systems. Nevertheless, the initialization of Mamdani FLSs’ main parameters, namely its membership functions and their interdependency relations, is a process that depends on the knowledge of an expert ...
Rómulo Antão +3 more
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A Takagi-Sugeno fuzzy gain-scheduler
Proceedings of IEEE 5th International Fuzzy Systems, 2002In the present paper we describe the design of a fuzzy gain scheduler for tracking a reference trajectory of a nonlinear autonomous system. The proposed fuzzy gain scheduling method has two major advantages over the existing crisp gain scheduling methods.
D. Driankov, R. Palm, U. Rehfuess
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