Minimax LQG control

Ian Petersen

International Journal of Applied Mathematics and Computer Science (2006)

  • Volume: 16, Issue: 3, page 309-323
  • ISSN: 1641-876X

Abstract

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This paper presents an overview of some recent results concerning the emerging theory of minimax LQG control for uncertain systems with a relative entropy constraint uncertainty description. This is an important new robust control system design methodology providing minimax optimal performance in terms of a quadratic cost functional. The paper first considers some standard uncertainty descriptions to motivate the relative entropy constraint uncertainty description. The minimax LQG problem under consideration is further motivated by analysing the basic properties of relative entropy. The paper then presents a solution to a worst case control system performance problem which can be generalized to the minimax LQG problem. The solution to this minimax LQG control problem is found to be closely connected to the problem of risk-sensitive optimal control.

How to cite

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Petersen, Ian. "Minimax LQG control." International Journal of Applied Mathematics and Computer Science 16.3 (2006): 309-323. <http://eudml.org/doc/207795>.

@article{Petersen2006,
abstract = {This paper presents an overview of some recent results concerning the emerging theory of minimax LQG control for uncertain systems with a relative entropy constraint uncertainty description. This is an important new robust control system design methodology providing minimax optimal performance in terms of a quadratic cost functional. The paper first considers some standard uncertainty descriptions to motivate the relative entropy constraint uncertainty description. The minimax LQG problem under consideration is further motivated by analysing the basic properties of relative entropy. The paper then presents a solution to a worst case control system performance problem which can be generalized to the minimax LQG problem. The solution to this minimax LQG control problem is found to be closely connected to the problem of risk-sensitive optimal control.},
author = {Petersen, Ian},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {robust control; risk-sensitive control; minimax control; stochastic uncertain system; LQGcontrol; output-feedback control; LQG control},
language = {eng},
number = {3},
pages = {309-323},
title = {Minimax LQG control},
url = {http://eudml.org/doc/207795},
volume = {16},
year = {2006},
}

TY - JOUR
AU - Petersen, Ian
TI - Minimax LQG control
JO - International Journal of Applied Mathematics and Computer Science
PY - 2006
VL - 16
IS - 3
SP - 309
EP - 323
AB - This paper presents an overview of some recent results concerning the emerging theory of minimax LQG control for uncertain systems with a relative entropy constraint uncertainty description. This is an important new robust control system design methodology providing minimax optimal performance in terms of a quadratic cost functional. The paper first considers some standard uncertainty descriptions to motivate the relative entropy constraint uncertainty description. The minimax LQG problem under consideration is further motivated by analysing the basic properties of relative entropy. The paper then presents a solution to a worst case control system performance problem which can be generalized to the minimax LQG problem. The solution to this minimax LQG control problem is found to be closely connected to the problem of risk-sensitive optimal control.
LA - eng
KW - robust control; risk-sensitive control; minimax control; stochastic uncertain system; LQGcontrol; output-feedback control; LQG control
UR - http://eudml.org/doc/207795
ER -

References

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  1. Boel R.K., James M.R. and Petersen I.R. (2002): Robustness and risk sensitive filtering. - IEEE Trans. Automat. Contr., Vol. 47, No. 3, pp. 451-461. 
  2. Collings I.B., James M.R. and Moore J.B. (1996): An information-state approach to risk-sensitive tracking problems. - J. Math. Syst. Estim. Contr., Vol. 6, No. 3, pp. 1-24. Zbl1076.93544
  3. Dai Pra P., Meneghini L. and Runggaldier W.J. (1996): Connections between stochastic control and dynamic games. - Math. Contr. Syst.Signals, Vol. 9, No. 2, pp. 303-326. Zbl0874.93096
  4. Doyle J., Packard A. and Zhou K. (1991): Review of LFT's, LMI's and μ. - Proc. 30-th IEEE Conf. s Decision on Control, Brighton, England, pp. 1227-1232. 
  5. Dupuis P. and Ellis R. (1997): A Weak Convergence Approach to the Theory of Large Deviations. - New York: Wiley. Zbl0904.60001
  6. Dupuis P., James M.R. and Petersen I.R. (2000): Robust properties of risk-sensitive control. - Math. Contr. Signals, Syst., Vol. 13, No. 4, pp. 318-332. Zbl0971.93081
  7. Horowitz I. (1963): Synthesis of Feedback Systems. - New York: Academic Press. Zbl0121.07704
  8. Jacobson D.H. (1973): Optimal stochastic linear systems with exponential performance criteria and their relation to deterministic differential games. - IEEE Trans. Automat. Contr., Vol. 18, No. 2, pp. 124-131. Zbl0274.93067
  9. Luenberger D.G. (1969): Optimization by Vector Space Methods. - NewYork: Wiley. Zbl0176.12701
  10. Moheimani S.O.R., Savkin A.V. and Petersen I.R. (1997): Minimax optimal control of discrete-time uncertain systems with structured uncertainty. - Dynam. Contr., Vol. 7, No. 1, pp. 5-24. Zbl0870.93021
  11. Petersen I.R. and James M.R. (1996): Performance analysis and controller synthesis for nonlinear systems with stochastic uncertainty constraints. - Automatica, Vol. 32, No. 7, pp. 959-972. Zbl0861.93033
  12. Petersen I.R., James M.R. and Dupuis P. (2000a): Minimax optimal control of stochastic uncertain systems with relative entropy constraints. - IEEE Trans. Automat. Contr., Vol. 45, No. 3, pp. 398-412. Zbl0978.93083
  13. Petersen I.R., Ugrinovski V. and Savkin A.V. (2000b): e Robust Control Design using H^∞ Methods. - London: Springer. 
  14. Savkin A.V. and Petersen I.R. (1995): Minimax optimal control of uncertain systems with structured uncertainty. - Int. J. RobustNonlin. Contr., Vol. 5, No. 2, pp. 119-137. Zbl0829.49003
  15. Savkin A.V. and Petersen I.R. (1996): Uncertainty averaging approach to output feedback optimal guaranteed cost control of uncertain systems. - J. Optim. Th. Applic., Vol. 88, No. 2, pp. 321-337. Zbl0853.93065
  16. Savkin A.V. and Petersen I.R. (1997): Output feedback guaranteed cost control of uncertain systems on an infinite time interval. - Int. J. Robust Nonlin. Contr., Vol. 7, No. 1, pp. 43-58. Zbl0866.93042
  17. Ugrinovskii V.A. and Petersen I.R. (1997): Infinite-horizon minimax optimal control of stochastic partially observed uncertain systems. - Proc. Control 97 Conference, Sydney, Australia, pp. 616-621. 
  18. Ugrinovskii V.A. and Petersen I.R. (1999a): Finite horizon minimax optimal control of stochastic partially observed time varying uncertain systems. - Math. Contr., Signals Syst., Vol. 12, No. 1, pp. 1-23. Zbl0929.93038
  19. Ugrinovskii V.A. and Petersen I.R. (1999b): Guaranteed cost LQG filtering for stochastic discrete time uncertain systems via risk-sensitive control. - Proc. IEEE Conf. Decision and Control, Phoenix, AZ, pp. 564-569. 
  20. Ugrinovskii V.A. and Petersen I.R. (2001a): Minimax LQG control of stochastic partially observed uncertain systems. - SIAM J. Contr. Optim., Vol. 40, No. 4, pp. 1189-1226. Zbl1016.93072
  21. Ugrinovskii V.A. and Petersen I.R. (2001b): Robust stability and performance of stochastic uncertain systems on an infinite time interval. - Syst. Contr. Lett., Vol. 44, No. 4, pp. 291-308. Zbl0986.93052
  22. Ugrinovskii V.A. and Petersen I.R. (2002a): Robust output feedback stabilization via risk-sensitive control. - Automatica, Vol. 38,No. 6, pp. 945-955. Zbl1030.93049
  23. Ugrinovskii V. and Petersen I.R. (2002b): Robust filtering of stochastic uncertain systems on an infinite time horizon. - Int. J. Contr., Vol. 75, No. 8, pp. 614-626. Zbl1018.93027
  24. Whittle P. (1981): Risk-sensitive linearquadraticGaussian control. - Adv. Appl. Prob., Vol. 13, No. 4, pp. 764-777. Zbl0489.93067
  25. Xie L., Ugrinovski V.A. and Petersen I.R. (2004a): Finite horizon robust state estimation for uncertain finite-alphabet hidden markov models with conditional relative entropy constraints. - Proc. 43-rd IEEE Conf. Decision and Control, Atlantis, Paradise Island,Bahamas, (on CD-ROM). 
  26. Xie L., Ugrinovski V.A. and Petersen I.R. (2004b): Probability distances between finite-alphabet hidden markov models. - Proc. 2-nd IFAC Symp. System, Structure and Control, Oaxaca, Mexico, (on CD-ROM). 
  27. Xie L., Ugrinovskii V.A. and Petersen I.R. (2005a): Probabilistic distances between finite-state finite-alphabet hidden Markov models. - IEEE Trans. Automat. Contr., Vol. 50, No. 4, pp. 505-511. 
  28. Xie L., Ugrinovskii V.A. and Petersen I.R. (2005b): A duality relationship for regular conditional relative entropy. - Proc. 16-th IFAC World Congress, Prague, Czech Republic, (on CD-ROM). 
  29. Yoon M. and Ugrinovskii V. (2003): Robust tracking problem for continuous time stochastic uncertain systems. - Proc. 42-nd IEEE Conf. Decision and Control, Hawaii, pp. 282-287. 
  30. Yoon M.G., Ugrinovskii V.A. and Petersen I.R. (2004): Robust finite horizon minimax filtering for stochastic discrete time uncertain systems. - Syst. Contr. Lett., Vol. 52, No. 2, pp. 99-112. Zbl1157.93531
  31. Yoon M.G., Ugrinovskii V.A. and Petersen I.R. (2005): On the worst disturbance of minimax optimal control. - Automatica, Vol. 41, No. 5, pp. 847-855. Zbl1093.93030

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