Optimal Control and Dynamic Optimization

  • Urmila M. Diwekar
Part of the Applied Optimization book series (APOP, volume 80)

Abstract

Optimal control problems involve vector decision variables. These problems are one of the most mathematically challenging problems in optimization theory.

Keywords

Optimal Control Problem Dynamic Optimization Geometric Brownian Motion Isoperimetric Problem Reflux Ratio 
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.

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Bibliography

  1. 1.
    Aris R. (1961), The Optimal Design of Chemical Reactors, Academic Press, London.MATHGoogle Scholar
  2. 2.
    Bellman R. (1957), Dynamic Programming, Princeton University Press, Princeton, New Jersey.Google Scholar
  3. 3.
    Betts J. T. (2001), Practical Method for optimal control Using Nonlinear Programming, SIAM, Philadelphia, PA.Google Scholar
  4. 4.
    Boltyanskii V. G., R. V. Gamkrelidze, and L. S. Pontryagin (1956), On the theory of optimum processes (in Russian), Doklady Akad. Nauk SSSR, 110, no. 1.Google Scholar
  5. 5.
    Converse A. O. and G. D. Gross (1963), Optimal distillate policy in batch distillation, Industrial Engineering Chemistry Fundamentals, 2 ,217.CrossRefGoogle Scholar
  6. 6.
    Diwekar U. M. (1992), Unified approach to solving optimal designcontrol problems in batch distillation, Aiche Journal, 38,1551.CrossRefGoogle Scholar
  7. 7.
    Diwekar U. M. (1995), Batch Distillation: Simulation, Optimal Design and Control, Taylor and Francis Publishers Inc. Washington DC.Google Scholar
  8. 8.
    Diwekar, U. M., Malik, R. K., and K. P. Madhavan (1987), Optimal Reflux Rate Policy Determination for Multicomponent Batch Distillation Columns, Computers and chemical Engineering, 11,629.CrossRefGoogle Scholar
  9. 9.
    Dixit A. K. and Pindyck, R.S.(1994), Investment Under Uncertainty, Princeton University Press, Princeton, NJ.Google Scholar
  10. 10.
    Fan L. T. (1966), The Continuous Maximum Principle, John Wiley & Sons, New York.MATHGoogle Scholar
  11. 11.
    Gilliland E. R. (1940), Multicomponent rectification. Estimation of the number of theoretical plates as a function of reflux, Industrial Engineering Chemistry, 32,1220.CrossRefGoogle Scholar
  12. 12.
    Kirk D. E. (1970), Optimal Control Theory An Introduction, Prentice Hall, Englewood Cliffs, N.J.Google Scholar
  13. 13.
    Naf U. G. (1994) Stochastic Simulation Using gPROMS, Computers and chemical Engineering, 18, S743.CrossRefGoogle Scholar
  14. 14.
    Merton R. C, and P. A. Samuelson (1990), Continuous-Time Finance, B. Blackwell Publishing, Cambridge Massachusetts, USA.Google Scholar
  15. 15.
    Pontryagin L. S. (1956), Some mathematical problems arising in connection with the theory of automatic control system (in Russian), Session of the Academic Sciences of the USSR on Scientific Problems of Automatic Industry, October 15–20.Google Scholar
  16. 16.
    Pontryagin L. S. (1957), Basic problems of automatic regulation and control (in Russian), Izdvo Akad Nauk SSSR. Google Scholar
  17. 17.
    Rico-Ramirez V., U. Diwekar, and B. Morel (2002), Real option theory from finance to batch distillation, submitted to Computers and Chemical Engineering. Google Scholar
  18. 18.
    Thompson G. L. and Sethi, S. P. (1994), optimal control Theory, Martinus Nijhoff Publishing, Boston, MA.Google Scholar
  19. 19.
    Troutman J. L. (1995), Variational Calculus and Optimal Control, Second Edition, Springer, New York, NY.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Urmila M. Diwekar
    • 1
  1. 1.Center for Uncertain Systems: Tools for Optimization & Management, Department of Chemical Engineering, and Institute for Environmental Science & PolicyUniversity of Illinois at ChicagoChicagoUSA

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