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,...

Fuzzy set theory and fuzzy logic are the convenient tools for handling uncertain, imprecise, or unmodeled data in intelligent decision-making systems. The utility of fuzzy logic in system controls domain is presented in the context of a mobile robot navigation control application. The Takagi-Sugeno controller is a fuzzy model capable of approximating a wide class of nonlinear systems by decompo...

Takagi-Sugeno (TS) fuzzy modeling and control techniques are applied to the classical nonlinear Ball and Beam problem. The nonlinear model is segmented to different local linear models. Local controllers are hence designed using LMI theory for achieving a robust control behavior for ball position. TS fuzzy modeling is applied to generate suitable fuzzy models, and the associated controllers. Us...

In this paper we consider stability analysis and controller design for periodic Takagi-Sugeno fuzzy models. To develop the conditions, we use a switching nonquadratic Lyapunov function defined at the time instants when the subsystems switch. Using the proposed conditions we are able to handle periodic Takagi-Sugeno systems where the local models or even the subsystems are unstable or cannot be ...

This paper concerns the use of fuzzy structures to model linear dynamic systems. A systematic method is proposed to generate the rules and also select the antecedent and consequent membership functions directly from the mathematical expression. The procedure is applied to the Takagi-Sugeno-Kang fuzzy structures and later adapted to the Mamdani fuzzy structures. It is shown that the Mamdani stru...

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...

This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local ...