International Journal of Innovative Research in Computer Science and Technology
Year: 2014, Volume: 2, Issue: 6
First page : ( 47) Last page : ( 49)
Online ISSN : 2350-0557.
Miss.Mali Priyadarshani S , Mrs. Tirmare Aarti H, Miss. Mohite Sangita R.
In industry process control, the model identification and predictive control of nonlinear systems are always difficult problems. This necessitates the development of empirical nonlinear model from dynamic plant data. This process is known as ‘Nonlinear System Identification’. Artificial neural networks are the most popular frame-work for empirical model development. The model is implemented by training a Multi-Layer Perceptron Artificial Neural network (MLP-ANN) with input output experimental data. Satisfactory agreement between identified and experimental data is found and results shown that the neural model successfully predicts the evolution of the product composition. Trained data available from nonlinear system used for process control using Model Predictive Control (MPC) algorithm. The Simulation result illustrates the validity and feasibility of the MPC algorithm.
[1] Su.H.T. & McAvoy.T.J., (1997) .Artificial Neural Networks for nonlinear Process Identification and Control”. In Henson, M.A., &Seaborg, D.E. (Eds).Nonlinear process control, Chapter 7(pp.371-428).Englewood Cliffs, NJ: Prentice-Hall.
[2] Michael A.Henson (1998).Nonlinear model predictive control: current status and future directions.
[3] Martin T.Hagan, Howar B.Demuth.Neural Networks for Control.
[4] Rihab Khalid Al Seyab (2006).Nonlinear Model Predictive Control Using Automatic Differentiation.
[5] Stephen Piche &, Bijan Sayyar-Rodsari, Doug Johnson, arid Mark Gerules. Nonlinear model predictive control using neural networks.
[6] J.C.Hokins and D.M.Himmelblau.Artificial neural network models of knowledge representation in chemical engineering. Computers & Chemical Engineering.12:881-890.1988.
[7] K.Hornik, M.Stinchcombe, and H.White.Multilayer feedforward networks are universal approximatros.Neural Networks, 2(5):359-366, 1989.
[8] M.Pottmann and D.Seborg.A radial basis function control strategy and its application to a ph neutralization process. Proceedings of Second European Control Conf.ECC’93, 1993.Groningen, Netherlands.
[9] Piyush Shrivastava. Control of Nonliear Process using Neural Network Based model predictive Control , IJEST 2011
Assistant Professor, E & TC Department, Bharati Vidyapeeth’s College of Engineering, Kolhapur, Maharashtra, India
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