Networked Predictive Process Control

  • Panagiotis D. Christofides
  • Jinfeng Liu
  • David Muñoz de la Peña
Part of the Advances in Industrial Control book series (AIC)


In Chap. 3, a two-tier networked control architecture, which naturally augments preexisting, point-to-point control systems with networked control systems taking advantage of real-time wired or wireless sensor and actuator networks, is presented. The two-tier networked control architecture for systems with continuous and asynchronous measurements is first presented and then the design is extended to include systems with continuous and asynchronous measurements which involve time-varying measurement delays. Using a nonlinear continuous stirred tank reactor (CSTR) example and a nonlinear reactor–separator example, the two-tier control architecture is demonstrated to be more optimal compared with conventional control systems and to be more robust compared with centralized predictive control systems. The two-tier control architecture is also applied to the problem of optimal management and operation of a standalone wind–solar energy generation system.


Wind Turbine Network Control System Continuous Stir Tank Reactor Prediction Horizon Output Feedback Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 8.
    Camponogara, E., Jia, D., Krogh, B. H., & Talukdar, S. (2002). Distributed model predictive control. IEEE Control Systems Magazine, 22, 44–52. CrossRefGoogle Scholar
  2. 11.
    Christofides, P. D., & El-Farra, N. H. (2005). Control of nonlinear and hybrid process systems: Designs for uncertainty, constraints and time-delays. Berlin: Springer. Google Scholar
  3. 21.
    Fogler, H. S. (1999). Elements of chemical reaction engineering. Englewood Cliffs: Prentice Hall. Google Scholar
  4. 33.
    Hofierka, J., & Suri, M. (2002). The solar radiation model for open source GIS: implementation and applications. In Proceedings of the open source GIS-GRASS users conference (pp. 1–19). Trento, Italy. Google Scholar
  5. 40.
    Khalil, H. K. (1996). Nonlinear systems (2nd ed.). New York: Prentice Hall. Google Scholar
  6. 48.
    Lin, Y., Sontag, E. D., & Wang, Y. (1996). A smooth converse Lyapunov theorem for robust stability. SIAM Journal on Control and Optimization, 34, 124–160. CrossRefMATHMathSciNetGoogle Scholar
  7. 51.
    Liu, J., Muñoz de la Peña, D., Ohran, B. J., Christofides, P. D., & Davis, J. F. (2008b). A two-tier architecture for networked process control. Chemical Engineering Science, 63, 5394–5409. CrossRefGoogle Scholar
  8. 57.
    Liu, J., Muñoz de la Peña, D., Ohran, B. J., Christofides, P. D., & Davis, J. F. (2010c). A two-tier control architecture for nonlinear process systems with continuous/asynchronous feedback. International Journal of Control, 83, 257–272. CrossRefMATHMathSciNetGoogle Scholar
  9. 64.
    Massera, J. L. (1956). Contributions to stability theory. Annals of Mathematics, 64, 182–206. CrossRefMathSciNetGoogle Scholar
  10. 73.
    Muñoz de la Peña, D., & Christofides, P. D. (2008). Stability of nonlinear asynchronous systems. Systems & Control Letters, 57, 465–473. CrossRefMATHMathSciNetGoogle Scholar
  11. 82.
    Peinke, J., Anahua, E., Barth, S., Goniter, H., Schaffarczyk, A. P., Kleinhans, D., & Friedrich, R. (2008). Turbulence a challenging issue for the wind energy conversion. In Proceedings of 2008 European wind energy conference & exhibition. Brussels Expo, Belgium. Google Scholar
  12. 88.
    Qi, W., Liu, J., Chen, X., & Christofides, P. D. (2011). Supervisory predictive control of stand-alone wind–solar energy generation systems. IEEE Transactions on Control Systems Technology, 19, 199–207. CrossRefGoogle Scholar
  13. 92.
    Rawlings, J. B., & Stewart, B. T. (2008). Coordinating multiple optimization-based controllers: New opportunities and challenges. Journal of Process Control, 18, 839–845. CrossRefGoogle Scholar
  14. 104.
    Valenciaga, F., & Puleston, P. F. (2005). Supervisor control for a stand-alone hybrid generation system using wind and photovoltaic energy. IEEE Transactions on Energy Conversion, 20, 398–405. CrossRefGoogle Scholar
  15. 105.
    Valenciaga, F., Puleston, P. F., Battaiotto, P. E., & Mantz, R. J. (2000). Passivity/sliding mode control of a stand-alone hybrid generation system. IEE Proceedings. Control Theory and Applications, 147, 680–686. CrossRefGoogle Scholar
  16. 106.
    Valenciaga, F., Puleston, P. F., & Battaiotto, P. E. (2001). Power control of a photovoltaic array in a hybrid electric generation system using sliding mode techniques. IEE Proceedings. Control Theory and Applications, 148, 448–455. CrossRefGoogle Scholar
  17. 107.
    Valenciaga, F., Puleston, P. F., & Battaiotto, P. E. (2004). Variable structure system control design method based on a differential geometric approach: application to a wind energy conversion subsystem. IEE Proceedings. Control Theory and Applications, 151, 6–12. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Panagiotis D. Christofides
    • 1
  • Jinfeng Liu
    • 1
  • David Muñoz de la Peña
    • 2
  1. 1.Department of Chemical and Biomolecular EngineeringUniversity of California, Los AngelesLos AngelesUSA
  2. 2.Departamento de Ingeniería de Sistemas y AutomáticaUniversidad de SevillaSevillaSpain

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