In this paper, we introduce a robust state feedback controller design using Linear Matrix Inequalities (LMIs) and guaranteed cost approach for Takagi-Sugeno fuzzy systems. The purpose on this work is to establish a systematic method to design controllers for a class of uncertain linear and non linear systems. Our approach utilizes a certain type of fuzzy systems that are based on Takagi-Sugeno ...

The paper presents development and tuning tehniques and solutions for PI and PID controllers, and Takagi-Sugeno fuzzy controllers with PI and PID type dynamics meant for applications which can be characterised with low order benchmark type modeles (for example electrical and hydraulic driving and positioning systems).Two type of plants and two control structures with homogenous and with non-hom...

This paper introduces a new approach for designing an adaptive fuzzy model predictive control (AFMPC) using the Particle Swarm Optimization (PSO) algorithm. The system to be controlled is modeled by a Takagi-Sugeno fuzzy inference system whose parameters are identified using recursive least square algorithm. These parameters are used to calculate the objective function based on predictive appro...

When controlling Takagi-Sugeno fuzzy systems, verification of some sector conditions is usually assumed. However, setpoint changes may alter the sector bounds. Alternatively, setpoint changes may be considered as an offset addition in many cases, and hence affine Takagi-Sugeno models may be better suited to this problem. This work discusses a nonconstant change of variable in order to carry out...

In this note, we consider a similar type of Gauss inequality for fuzzy integrals. More precisely, we show that the inequality x(S) ∫ ∞

Adaptive binarization methodologies threshold the intensity of pixels with respect to adjacent exploiting integral images. In turn, images are generally computed optimally using summed-area-table algorithm (SAT). This document presents a new adaptive technique based on fuzzy through an efficient design modified SAT for integrals. We define this methodology as FLAT (Fuzzy Local Thresholding). Th...

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...

AbstructThis paper presents a gain scheduling based approach to the design of fuzzy controllers. We demonstrate that the Sugeno fuzzy controller is in fact a special case of gain scheduling where the feedback gains are adjusted (“scheduled”) via membership functions in fuzzy if-then rules. When the desired gains at various operating points are available, we can apply the ANFIS learning algorith...

This paper investigates delay-dependent robust asymptotic state estimation of fuzzy neural networks with mixed interval time-varying delay. In this paper, the Takagi–Sugeno (T–S) fuzzy model representation is extended to the robust state estimation of Hopfield neural networks with mixed interval time-varying delays. The main purpose is to estimate the neuron states, through available output mea...