In this paper, we are interested in the adaptive fuzzy control a technique has been studied and applied, namely adaptive fuzzy control based on theory of Lyapunov. The system based on the stability theory is used to approximate the gains Ke and kdce to ensure the stability of the control in real time .the simulations results obtained by using Matlab environment gives that the fuzzy adaptive control more robust, also it has superior dynamics performances. The results and test of robustness will be presented.

This paper presents the contribution of a fuzzy controller to compensate the influence of stator resistance variation which can degrade the performance and stability of a direct torque control (DTC). Nevertheless, the original term DTC refers to a strategy that provides good performance, but it also has some negative aspects to the level of switching and inaccuracy in the engine model which recommends the use of a new technique the SVM which proposes an algorithm based on the modulation of the space vector in order to carry out a predictive regulation of the torque and flux of the induction motor and provides a fixed switching frequency, thus improving the dynamic response and the static behavior of the DTC.

Since the work of Lotfi Zadeh in 1965, the fuzzy logic continues to interest researchers and industrialists who gather around the "theories of uncertainty". The ramifications of fuzzy logic extend to fields as varied as control, the diagnosis of complex systems, bioinformatics, decision support. Research work is done in bio-informatic field where a system for decision support of anesthetic depth fuzzy basic. This study was carried out under general anesthesia with propofol. We use in our work some parameters influencing the patient’s condition during the course of surgery to control their effects on the depth of general anesthesia by fuzzy logic. In this paper, we propose using the environment MatLab R2017a to realize this application. A comparison between the predictions of the anesthesiologist and anesthetic predictions according to fuzzy logic of our work is done. This study will serve as a guide in developing new anesthesia control systems for patients.