Fuzzy robust tracking control for uncertain nonlinear systems

TL;DR: The key point of the proposed approaches is to achieve conditions under a LMI (linear matrix inequalities) formulation in the case of an uncertain and disturbed T-S fuzzy model that facilitates obtaining solutions through interior point optimization methods for some nonlinear output tracking control problems.

TL;DR: A fuzzy guaranteed cost output tracking control design problem for a flexible air-breathing hypersonic vehicle (FAHV), which can be modelled in a Takagi-Sugeno (T-S) model form, is concerned.

TL;DR: A robust nonlinear fuzzy control algorithm is developed and applied to two different engine torque control structures and tested on a three cylinder SI engine test bench to prove the effectiveness of the method.

TL;DR: A detailed comparative analysis carried out from the viewpoint of the performance and the design methodology, is provided for the fuzzy cascade controller and the conventional PD cascade controller whose design relied on the use of the serial genetic algorithms.

TL;DR: The authors represent a nonlinear plant with a Takagi-Sugeno fuzzy model with a model-based fuzzy controller design utilizing the concept of the so-called "parallel distributed compensation" and presents a design methodology for stabilization of a class of nonlinear systems.

TL;DR: The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models, which possess certain advantages over neural networks.

TL;DR: In this paper, the problem of robustly stabilizing a linear uncertain system is considered with emphasis on the interplay between the time-domain results on the quadratic stabilization of uncertain systems and the frequency domain results on H/sup infinity / optimization.

TL;DR: This study introduces a fuzzy control design method for nonlinear systems with a guaranteed H/sub /spl infin// model reference tracking performance using the Takagi and Sugeno (TS) fuzzy model to represent a nonlinear system.