This paper proposes a new method to stabilize the continuous-time TS fuzzy models by using results from their discretized models. The proposed approach is structured as follows: first, a discrete model is obtained from the discretization of the continuous TS fuzzy model. Second the gains of a non-PDC controller ensuring the stabilization of the discrete model are determined. Third, we check if the discrete control law used continuously without any zero-order hold can stabilize the continuous TS model. It is obvious that two sets of LMIS conditions are obtained: one for the discrete model, and one for the continuous on. But these two sets of LMIS conditions are much easier to solve than a single set of BMIS conditions usually found when a non PDC control law is directly applied to a continuous model. Simulation examples illustrate the effectiveness of this approach.

 Interval type-2 fuzzy logic controllers have attracted much research interests in recent years due to their ability to cope with uncertainty. In this paper, we propose a systematical methodology to construct an interval type-2 fuzzy logic controller. The main contribution of this methodology is to generate the rule base of an interval type-2 fuzzy logic controller based on an existing conventional PI controller. As a consequence a linear control law is transformed to a nonlinear structure. Thus, the designer can benefit from the nonlinear structure of the proposed controller and the extra degree of freedom of type-2 fuzzy sets. The simulation results have shown that the proposed controller can manage the uncertainties much better than linear conventional PI and type-1 fuzzy logic controllers.

 In this paper, we propose the use of a Particle Swarm Optimization (PSO) for tuning decentralized typical fuzzy PI (FPI) controllers applied for the stabilization of a helicopter model. The method is used such as to minimize a square error (SE) fitness function which quantifies the performance of the whole control system. The proposed PSO method exploits all the available knowledge about the system under control since, the considered typical FPI controllers are characterized by simple and interpretable structure, which can reduces the tuning time.

 This paper proposes an approach of digital controller design on microcontrollers based on control surface discretization. As experimental setup a simple system composed of a DC motor, the PWM drive and a encoder was built. Identification of this simple system was done using the first order linear model and the Hammerstein-Wiener model. The quality of these models was compared based on the fitness function and their ability to cope with system nonlinearities. A discrete PI speed controller was designed based on the Hammerstein-Wiener model and tuned by the Gauss-Newton optimization algorithm. The Fuzzy logic controller was designed on the basis of the PI controller behaviour with genetic algorithm parameter tuning. The control surface of the Fuzzy logic controller was discretized for microcontroller implementation. Both controllers were implemented on the Arduino Mega platform and their control performances were tested and compared.

 This paper presents a methodology for leakage localization using FIR (Fuzzy Inductive Reasoning). A real water network situated in Barcelona has been subdivided in zones which could contain a leakage. Two sensors measure pressures on two separated points of the network. A faulty fuzzy model for each zone and sensor is generated. Test data have been used for classification of leakages in order to evaluate how this methodology helps in leakage localization. Results are compared with another isolation methodology. All the work has been done using simulations carried out by EPANET connected with Matlab. FIR applications used are programmed in Matlab too.