This paper describes the design of an adaptive direct control scheme for a class of nonlinear systems. The architecture is based on a fuzzy inference system (FIS) of Takagi Sugeno (TS) type to approximate a feedback linearization control law. The parameters of the consequent part of the fuzzy system are adapted and changed according to a law derived using Lyapunov stability theory. The asymptot...

This paper proposes a Takagi-Sugeno neuro-fuzzy inference system for direct torque and stator reactive power control applied to a doubly fed induction motor. The control variables (d-axis and q-axis rotor voltages) are determined through a control system composed by a neuro-fuzzy inference system and a first order Takagi-Sugeno fuzzy logic controller. Experimental results are presented to valid...

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 ...

This document deals with the problem of passive fuzzy controller design state-derivative feedback approach for nonlinear stochastic singular systems. Recently, systems have a greater focus on literature because they can keep more physical system characteristics than conventional At first, Takagi-Sugeno models are used to represent Then, and parallel distributed compensation method employed cont...

In this chapter, a class of nonlinear time-delay systems based on the TakagiŽ . w x Sugeno T-S fuzzy model is defined 1 . We investigate the delay-independent stability of this model. A model-based fuzzy stabilization design utilizing Ž . the concept of parallel distributed compensation PDC is employed. The main idea of the controller design is to derive each control rule to compensate each rul...

This paper presents a robust indirect model reference fuzzy control scheme for control and synchronization of chaotic nonlinear systems subject to uncertainties and external disturbances. The chaotic system with disturbance is modeled as a Takagi–Sugeno fuzzy system. Using a Lyapunov function, stable adaptation laws for the estimation of the parameters of the Takagi–Sugeno fuzzy model are deriv...

In this paper, the stability analysis and control design of Takagi–Sugeno (TS) fuzzy systems subject to uncertain time-delay are addressed. The proposed approach is based on linear matrix inequalities and the Lyapunov–Krasovskii theory, where a new fuzzy weighting-dependent Lyapunov–Krasovskii functional is introduced. By employing the Gu discretization technique and strategies to add slack mat...

This paper presents a decentralized control problem for stabilizing the nonlinear large-scale descriptor (LSD) systems that use proportional-plus-derivative state (PD) feedback scheme. The have great focus on research because they can contain more physical characteristics than standard state-space systems. At first, each subsystem in LSD system be represented by Takagi-Sugeno (T-S) with interco...

Fermentation process is vital and important in many biotechnological applications. However modeling the fermentation process is considered a challenging and complex problem. The complexity of the problem is driven by the need of efficient, accurate, not expensive, and reliable predictive models. In this paper, we apply a Takagi-Sugeno Fuzzy Logic technique for modeling the lipase activity produ...

The prediction of uncertain and predictive nonlinear systems is an important and challenging problem. Fuzzy logic models are often a good choice to describe such systems, however in many cases these become complex soon. commonlly, too less effort is put into descriptor selection and in the creation of suitable local rules. Moreover, in common no model reduction is applied, while this may analyz...