Modeling, Identification and Control of Irrigation Station with Sprinkling: Takagi-Sugeno Approach

The spray under pressure is an effective save on water. This task should be automated and controlled in order to limit the water waste and the facilities of damages. For this reason, it’s necessary to find a mathematical model describing the irrigation process. In order to facilitate this step the Takagi-Sugeno fuzzy model is the best approaches of nonlinear systems representation. Various techniques are used in the literature of such systems; the clustering technique is one of the best solutions. In this paper, we’ll model the irrigation station with the T-S algorithm and use the fuzzy c-means (FCM) algorithm and present the results of simulation and some validation tests and we present the stability of T-S irrigation station model.

[1]  Marc Moonen,et al.  Low-Complexity Distributed Total Least Squares Estimation in Ad Hoc Sensor Networks , 2012, IEEE Transactions on Signal Processing.

[2]  Ahmad Taher Azar,et al.  Overview of Type-2 Fuzzy Logic Systems , 2012, Int. J. Fuzzy Syst. Appl..

[3]  Jie Jia,et al.  Two-stage recursive least squares parameter estimation algorithm for output error models , 2012, Math. Comput. Model..

[4]  Troudi Ahmed Nonlinear system identification using clustering algorithm and particle swarm optimization , 2012 .

[5]  Ahmad Taher,et al.  Adaptive Neuro-Fuzzy Systems , 2010 .

[6]  Norlaili Mohd Noh,et al.  Adaptive neuro-fuzzy inference system identification model for smart control valves with static friction , 2013, 2013 IEEE International Conference on Control System, Computing and Engineering.

[7]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Zhang Li,et al.  T-S Fuzzy Modeling Method Based on C-Means Clustering , 2013, 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[9]  Abderrahmen Zaafouri,et al.  Hybrid control of a station of irrigation by sprinkling: Fuzzy supervisor approach , 2015, 2015 4th International Conference on Systems and Control (ICSC).

[10]  Abdelkader Chaari,et al.  A novel weighted recursive least squares based on Euclidean particle swarm optimization , 2013, Kybernetes.

[11]  Yu-Geng Xi,et al.  A clustering algorithm for fuzzy model identification , 1998, Fuzzy Sets Syst..

[12]  Zhang Shu-Ling,et al.  Fuzzy Particle Swarm Clustering of Infrared Images , 2009, 2009 Second International Conference on Information and Computing Science.

[13]  Woo-seok Jang,et al.  Optimized fuzzy clustering by predator prey particle swarm optimization , 2007, IEEE Congress on Evolutionary Computation.

[14]  Abderrahmen Zaafouri,et al.  Control and Modelling Using Takagi-Sugeno Fuzzy Logic of Irrigation Station by Sprinkling , 2014 .

[15]  P. Lu,et al.  Random sampling fuzzy c-means clustering and recursive least square based fuzzy identification , 2006, 2006 American Control Conference.

[16]  Troudi Ahmed,et al.  Nonlinear system identification using new Extended Possibilistic C-Means Algorithm and Particle Swarm Optimization , 2013, 2013 International Conference on Control, Decision and Information Technologies (CoDIT).

[17]  Xueli An,et al.  T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm , 2009, Eng. Appl. Artif. Intell..

[18]  Victor Hugo Grisales Palacio Modélisation et commande floues de type Takagi-Sugeno appliquées à un bioprocédé de traitement des eaux usées , 2007 .

[19]  Constantin V. Negoita,et al.  On Fuzzy Systems , 1978 .

[20]  Noureddine Zahid,et al.  Fuzzy clustering based on K-nearest-neighbours rule , 2001, Fuzzy Sets Syst..

[21]  Ilya V. Kolmanovsky,et al.  Predictive energy management of a power-split hybrid electric vehicle , 2009, 2009 American Control Conference.

[22]  Donald Gustafson,et al.  Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[23]  Bo Fu,et al.  T–S Fuzzy Model Identification With a Gravitational Search-Based Hyperplane Clustering Algorithm , 2012, IEEE Transactions on Fuzzy Systems.

[24]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.