Complexity
Volume 2017 (2017), Article ID 9241254, 8 pages
https://doi.org/10.1155/2017/9241254
Research Article
A Gain-Scheduling PI Control Based on Neural Networks
Dipartimento di Ingegneria Meccanica, Chimica e dei Materiali, Università degli Studi di Cagliari, Via Marengo 2, 09123 Cagliari, Italy
Correspondence should be addressed to Stefania Tronci; ti.acinu.mcmid@icnort.ainafets
Received 13 July 2017; Revised 19 September 2017; Accepted 24 September 2017; Published 19 October 2017
Academic Editor: Jing Na
Copyright © 2017 Stefania Tronci and Roberto Baratti. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Linked References
- M. Mulas, S. Tronci, F. Corona et al., “Predictive control of an activated sludge process: an application to the Viikinmäki wastewater treatment plant,” Journal of Process Control, vol. 35, pp. 89–100, 2015. View at Publisher · View at Google Scholar · View at Scopus
- I. Machón-González and H. López-García, “Feedforward nonlinear control using neural gas network,” Complexity, vol. 2017, Article ID 3125073, 11 pages, 2017. View at Publisher · View at Google Scholar
- G. Cogoni, S. Tronci, R. Baratti, and J. A. Romagnoli, “Controllability of semibatch nonisothermal antisolvent crystallization processes,” Industrial & Engineering Chemistry Research, vol. 53, no. 17, pp. 7056–7065, 2014. View at Publisher · View at Google Scholar · View at Scopus
- J. Na, X. Ren, C. Shang, and Y. Guo, “Adaptive neural network predictive control for nonlinear pure feedback systems with input delay,” Journal of Process Control, vol. 22, no. 1, pp. 194–206, 2012. View at Publisher · View at Google Scholar · View at Scopus
- P. L. Lee and G. R. Sullivan, “Generic model control (GMC),” Computers & Chemical Engineering, vol. 12, no. 6, pp. 573–580, 1988. View at Publisher · View at Google Scholar · View at Scopus
- B. J. Cott and S. Macchietto, “Temperature control of exothermic batch reactors using generic model control,” Industrial & Engineering Chemistry Research, vol. 28, no. 8, pp. 1177–1184, 1989. View at Publisher · View at Google Scholar · View at Scopus
- T. D. Knapp, H. M. Budman, and G. Broderick, “Adaptive control of a CSTR with a neural network model,” Journal of Process Control, vol. 11, no. 1, pp. 53–68, 2001. View at Publisher · View at Google Scholar · View at Scopus
- A. Vega, F. Diez, and J. M. Alvarez, “Programmed cooling control of a batch crystallizer,” Computers & Chemical Engineering, vol. 19, pp. 471–476, 1995. View at Publisher · View at Google Scholar
- E. E. Ekpo and I. M. Mujtaba, “Evaluation of neural networks-based controllers in batch polymerisation of methyl methacrylate,” Neurocomputing, vol. 71, no. 7–9, pp. 1401–1412, 2008. View at Publisher · View at Google Scholar · View at Scopus
- R. Kamesh, P. S. Reddy, and K. Y. Rani, “Comparative study of different cascade control configurations for a multiproduct semibatch polymerization reactor,” Industrial & Engineering Chemistry Research, vol. 53, no. 38, pp. 14735–14754, 2014. View at Publisher · View at Google Scholar · View at Scopus
- S. M. Alsadaie and I. M. Mujtaba, “Generic model control (GMC) in multistage flash (MSF) desalination,” Journal of Process Control, vol. 44, pp. 92–105, 2016. View at Publisher · View at Google Scholar · View at Scopus
- C. Foscoliano, S. Del Vigo, M. Mulas, and S. Tronci, “Predictive control of an activated sludge process for long term operation,” Chemical Engineering Journal, vol. 304, pp. 1031–1044, 2016. View at Publisher · View at Google Scholar · View at Scopus
- R. Baratti, A. Servida, and S. Tronci, “Neural DMC control strategy for a CSTR in presence of noise,” in IFAC Symposium Dycops-6, G. Stephanopoulos, Ed., vol. 34, pp. 680–694, 6th edition, 2001. View at Publisher · View at Google Scholar
- S. S. Ge, C. C. Hang, and T. Zhang, “Nonlinear adaptive control using neural networks and its application to CSTR systems,” Journal of Process Control, vol. 9, no. 4, pp. 313–323, 1999. View at Publisher · View at Google Scholar · View at Scopus
- G. M. Scott and W. H. Ray, “Creating efficient nonlinear neural network process models that allow model interpretation,” Journal of Process Control, vol. 3, no. 3, pp. 163–178, 1993. View at Publisher · View at Google Scholar · View at Scopus
- J. C. Principe, N. R. Euliano, and W. C. Lefebvre, Neural and Adaptive Systems: Fundamentals through Simulations, vol. 672, Wiley, New York, NY, USA, 2000. View at Publisher · View at Google Scholar
- B. A. Ogunnaike and W. H. Ray, Process Dynamics, Modeling and Control, Oxford University Press, 1994.
- G. Lightbody and G. W. Irwin, “Direct neural model reference adaptive control,” IEE Proceedings Control Theory and Applications, vol. 142, no. 1, pp. 31–43, 1995. View at Publisher · View at Google Scholar · View at Scopus