Neuro-Fuzzy Scheduler for the Control of Real Time Spherical Tank Process
Citation
Boo. Poonguzhali, R. Vinodha "Neuro-Fuzzy Scheduler for the Control of Real Time Spherical Tank Process", International Journal of Engineering Trends and Technology (IJETT), V51(1),51-56 September 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
PID controller is the simplest and best suited controller in process industries to maintain the process at the desired set value. But the linear controller needs adaptation mechanism to cope up with non-linear dynamics of the process. The aim of the proposed work is the real time implementation of the neuro-fuzzy scheduler to contribute the conventional PI control in the entire span of spherical tank process. Neuro-fuzzy integrates the parallel computation and learning abilities of neural network with the human like knowledge representation and explanation abilities of a fuzzy system. The best performance enhancement has been seen in neuro-fuzzy scheduled PI controller than conventional fuzzy scheduled PI control. The real time implementation is done in the laboratory spherical tank setup in MATLAB-SIMULINK environment using V-MAT interface card.
Reference
[1] C.T. Lin and C.S.G. Lee, “Neural-Network-based Fuzzy Logic Control and Decision System”, IEEE Transactions on Computers, Vol. 40(12), pp. 1320-1336, 1993.
[2] Zahedi, “Prediction of Hydrate Formation Temperature by Both Statistical Models and Artificial Neural Network Approaches”, Vol 50(8), pp. 2052-2059, 2009.
[3] Ron Ya-Jun, Dou Chun-Xia, Yuan Shi-wen, “A Design of Fuzzy Neural Network Forecast Controller on Superheat Temperature [J]”, (Yanshan University, Qinhuangdao 066004, China).
[4] James c. Bezdek, Chris Coray, Robert Gunderson and James Watson, “Detection and Characterization of Cluster Substructure I. Linear Structure: Fuzzy c-Lines”, Journal on Applied Mathematics, Vol 40(2), pp. 339-357, 1981.
[5] Z.Y. Wangi , C. Sahay, “Agile monitoring system for turning of difficult – to - cut materials”, Computer and Industrial Engineering, 18th International Conference on Computer and Industrial Engineering., Vol. 31(3-4), pp. 537-948, 1996.
[6] Ihsan Erozan, Ozden Ustun, Orhan Torkul, “An Improved Fuzzy C-Means Algorithm for Cell Formation Problems with Alternative Routes”, International Journal of Fuzzy System Applications, Vol. 4(4), pp. 15-30, 2015.
[7] J.R. Yang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE Transactions on Systems, Man, and Cybernetic, Vol. 23(3), pp. 665-685, 1993. [8] S.R. Khuntia, K.B. Mohanty, S. Panda, C. Ordeal, “A Comparative Study of P-I, I-P, Fuzzy and Neuro-Fuzzy Controllers for Speed Control of DC Motor Drive”, World Academy of Science, Engineering & Technology, Vol. 24(44), pp. 525, 2010.
[9] J.S.R. Jang, C.T. Sun, and E. Mizutani, “Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence”, IEEE Transactions on Automatic Control, Vol. 42(10), 1997.
[10] Boo. Poonguzhali and R. Vinodha, “Implementation of Anti-Reset Windup Scheme in PI Controller for Spherical Tank Process”, International Journal of Modelling and Simulation, Vol. 34(4), pp. 1-15, 2014.
[11] K.R. Sundaresan and R.R. Krishnaswamy, “Estimation of Time Delay, Time Constant Parameters in Time, Frequency and Laplace Domains”, Canadian Journal of Chemical Engineering, Vol. 56(2), pp. 257-262, 1978.
[12] G. Ziegler and N.B. Nichols, “Optimum Settings for Automatic Controllers”, Transcation ASME, Vol. 64(11), pp. 759-768, 1942.
Keywords
Neuro fuzzy, scheduler, spherical tank.