In this paper, the Takagi–Sugeno (T–S) fuzzy model is used to express dynamic systems, and an on-line identification algorithm for its parameters and structures is presented. A new multivariable fuzzy generalized predictive control approach is put forward based on the identified fuzzy model by means of Clark’s principle of single-variable generalized predictive control, some of whose performanc...

Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties and to perform under noisy conditions in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for interval type-2 Takagi–Sugeno–Kang (TSK) fuzzy neural networks is proposed in this paper. Differently from the other rece...

“Fuzzy Functions” are proposed to be determined separately by two regression estimation models: the least squares estimation (LSE), and Support Vector Machines for Regression (SVR), techniques for the development of fuzzy system models. LSE model tries to estimate the fuzzy function parameters linearly in the original space, whereas SVR algorithm maps the data samples into higher dimensional fe...

In the design of modern and classical control systems, the first step is establish a suitable mathematical model to describe the behavior of the controlled plant (Takagi & Sugeno, 1985; Ying et al., 1990). However, in practical situations, such a requirement is not feasible because in practical control systems the plants are always nonlinear systems, which makes this task analytically unfeasibl...

This paper addresses a Compensatory Wavelet Neuro-Fuzzy System (CWNFS) for temperature control. The proposed CWNFS model is five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). We adopt the non-orthogonal and compactly supported functions as wavelet neural network bases. Besides, the compensatory fuzzy reasoning method ...

This paper presents an active fault tolerant control (FTC) strategy for induction motor drive that ensures trajectory tracking and offset the effect of the sensor faults despite the presence of load torque disturbance. The proposed approaches use a fuzzy descriptor observer to estimate simultaneously the system state and the sensor fault. The physical model of induction motor is approximated by...

This article presents a Takagi–Sugeno–Kang Fuzzy Neural Network (TSKFNN) approach to predict freeway corridor travel time with an online computing algorithm. TSKFNN, a combination of a Takagi–Sugeno– Kang (TSK) type fuzzy logic system and a neural network, produces strong prediction performance because of its high accuracy and quick convergence. Real world data collected from US-290 in Houston,...

Having attracted much attention in the past few years, predator-prey system provides a good mathematical model to present the correlation between predators and preys. This paper focuses on the robust stability of Lotka-Volterra predator-prey system with the fuzzy impulsive control model, and Takagi-Sugeno T-S fuzzy impulsive control model as well. Via the T-S model and the Lyapunov method, the ...

A large class of nonlinear systems can be well approximated by Takagi-Sugeno fuzzy models, for which methods and algorithms have been developed to analyze their stability and to design observers and controllers. However, results obtained for Takagi-Sugeno fuzzy models are in general not directly applicable to the original nonlinear system. In this paper, we investigate what conclusions can be d...

In this paper, a systematic methodology to design fuzzy Takagi-Sugeno observers and controllers will be used to estimate the angular positions and speeds, as well as to stabilise an experimental mechanical system with 3 degrees of freedom (fixed quadrotor). Takagi-Sugeno observers and controllers are compared to observers and controllers based on the linearized model, both designed with the sam...