Control and Modelling Using Takagi-Sugeno Fuzzy Logic of Irrigation Station by Sprinkling
Wael Chakchouk, Abderrahmen Zaafouri, Anis Sallami, Hussein Taha, Bab Menara · 2014
The spay under pressure is an effective save of water, this task should be automated and controlled in order to limit the waste of water and the facilities of damages. For this reason, it's necessary to find a mathematic model describing the irrigation process. In order to facilitate this step the fuzzy model of takagi- segeno is the best approaches to the representation of nonlinear systems. Various techniques are used in the literature of such systems; the clustering technique is one of best solution. In this paper we'll modelled the irrigation station with the T-S algorithm and use the fuzzy c-means (FCM) algorithm and present the results of simulation with the use of some regulation methods such the PI regulator of station and the fuzzy logic controller. The development of a mathematical model making it number of clusters is fixed by an expert according to type to represent as well possible the dynamic of application considered and of performances required behavior of a complex real process represents a very by this last. By consequent to each cluster one important problem in the real world. In recent years and correspond homogeneous zone of operation of the with the evolution of technology, a significant effort has system that is defined in the form of a linear local model. been given to modelling, identification and control of We are interested to model and identify a nonlinear such systems. The Takagi-Sugeno fuzzy model (1, 2) is system by fuzzy logic approach such Takagi-Sugeno one of the best approaches to the representation of such (T-S) approach. The latter, uses modelling containing a process. Indeed, the T-S fuzzy model can approximate linguistic rules to obtain the model of system outputs. highly nonlinear system into several locally linear Initially we present the fuzzy logic approach follows, one subsystems. The identification problem in T-S fuzzy gives an outline on the first two models. Then, we detail model can be summarized in two steps: structure (T-S) model, one uses the method of fuzzy coalescence for identification and parameter estimation, several the identification of the nonlinear systems by the fuzzy techniques were developed to conclude the modelling of C-means (FCM) algorithm. We will in addition present this type of the systems. One quotes primarily the tests of validation of (T-S) model. Then, we will give the technique neuron-blur (3) and technique of clustering results of identification and modelling of the station of (4-9). Indeed Several researchers have noticed that a irrigation by sprinkling. We are interested in the analysis nonlinear system can be approximated by the sum of and the synthesis of controls which we apply to the several linear sub-systems. Method of clustering proves model obtained from the station of irrigation. We will to be a technique interesting for identification and the present the behavior of the linked system overlooked the modelling of the nonlinear systems. Indeed this technique disturbances in the case of the regulation by PI controller consists in approximating the total nonlinear system by a then for the case of the fuzzy controller. A comparative cluster represents one fuzzy rule of Takagi-Sugeno. The