Abstract
On-line Radial Basis Function (RBF) neural network based multiple steady states controller for nonlinear system is presented. The unsafe upper steady states can be prevented with the optimizer for Constrained General Model Controller (CGMC).Process simulator package is used to generate a wide range of operation data and the dynamic simulator is built as the real plant. The effectiveness is illustrated with a Continuous Stirred Tank Reactor (CSTR) and OPC tools are developed for on-line data acquisition and computation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Molnar, J.M.: Safety Analysis of CSTR Towards Changes in Operating Conditions. J. Loss Prev. Proc. Ind 16, 373–380 (2003)
Sivakumar, S.C.: Steady-state Operability Characteristics of Reactors. Computers Chem. Engng 24, 1563–1568 (2000)
Knapp, T.D., Buddman, H.M.: Adaptive Control of a CSTR with a Neural Network Model. J. Proc. Cont. 1, 53–68 (2001)
Galvan, I.M., Zaldivar, J.M.: Application of Recurrent Neural Networks in Batch Reactors Part II:Nonlinear Inverse and Predictive Control of the Heat Transfer Fluid Temperature. Chem. Engineering Processing 37, 149–161 (1998)
Fogler, H.S.: Elements of Chemical Reaction Engineering. Prentice Hall, London (1999)
KaapoB, B.B.: Application of Cybernetics in Chemic and Chemical Engineering. Chemical Industry Publishing Company, Beijing (1983)
Pottmann, M., Seborg, D.E.: A Nonlinear Predictive Control Strategy Based on Radial Basis Function Models. Computers Chem. Engng 21, 965–980 (1997)
Aspen Technology, Inc.: HYSYS 3.2 Documentation, User Guide. Ten Canal Park, Cambridge, MA02141, U.S.A (2003)
Lee, P.L., Sullivan, G.R.: Generic Model Control (GMC). Computers Chem. Engng 12, 573–580 (1988)
OPC Foundation: OPC Data Access Specification 1.0 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, X., Huang, D., Jin, Y. (2005). RBFNN-Based Multiple Steady States Controller for Nonlinear System and Its Application. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_3
Download citation
DOI: https://doi.org/10.1007/11427469_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)