Maximal solution to algebraic Riccati equations linked to infinite Markov jump linear systems

Original Article
  • 73 Downloads

Abstract

In this paper, we deal with a perturbed algebraic Riccati equation in an infinite dimensional Banach space. Besides the interest in its own right, this class of equations appears, for instance, in the optimal control problem for infinite Markov jump linear systems (from now on iMJLS). Here, infinite or finite has to do with the state space of the Markov chain being infinite countable or finite (see Fragoso and Baczynski in SIAM J Control Optim 40(1):270–297, 2001). By using a certain concept of stochastic stability (a sort of L 2-stability), we prove the existence (and uniqueness) of maximal solution for this class of equation and provide a tool to compute this solution recursively, based on an initial stabilizing controller. When we recast the problem in the finite setting (finite state space of the Markov chain), we recover the result of de Souza and Fragoso (Syst Control Lett 14:233–239, 1999) set to the Markovian jump scenario, now free from an inconvenient technical hypothesis used there, originally introduced in Wonham in (SIAM J Control 6(4):681–697).

Keywords

Perturbed Riccati equation Maximal solution Infinite Markov chain Continuous-time linear system Control problem 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ait Rami M, Chen X, Moore JB, Zhou XY (2001) Solvability and asymptotic behavior of generalized Riccati equations arising in indefinite stochastic LQ controls. IEEE Trans Automat Control 46: 428–440MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Baczynski J, Fragoso MD (2008) Maximal versus strong solution to algebraic Riccati equations arising in infinite Markov jump linear systems. Syst Control Lett 57: 246–254MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    de Souza CE, Fragoso MD (1990) On the existence of maximal solution for generalized algebraic Riccati equations arising in stochastic control. Syst Control Lett 14: 233–239MATHCrossRefGoogle Scholar
  4. 4.
    do Val JB, Basar T (1999) Receding horizon control of jump linear systems and a macroeconomic policy problem. J Econ Dynam Control 23(8): 1099–1131MATHCrossRefGoogle Scholar
  5. 5.
    Dufour F, Bertrand P (1994) The filtering problem for continuous-time linear systems with Markovian switching coefficients. Syst Control Lett 23: 453–CrossRefMathSciNetGoogle Scholar
  6. 6.
    Feng X, Loparo KA, Ji Y, Chizeck HJ (1992) Stochastic stability properties of jump linear systems. IEEE Trans Automat Control 37: 1884–1892CrossRefMathSciNetGoogle Scholar
  7. 7.
    Fragoso MD, Baczynski J (2001) Optimal control for continuous time LQ—problems with infinite Markov jump parameters. SIAM J Control Optim 40(1): 270–297MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Fragoso MD, Baczynski J (2002) Lyapunov coupled equations for continuous time infinite Markov jump linear systems. J Math Anal Appl 274: 319–335MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Fragoso MD, Baczynski J (2005) On an infinite dimensional perturbed Riccati equation arising in stochastic control. Linear Algebra Appl 406: 165–176MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Fragoso MD, Costa OLV, de Souza CE (1998) A new approach to linearly perturbed Riccati equations arising in stochastic control. Appl Math Optim 37: 99–126MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Fragoso MD, Costa OLV (2005) A unified approach for stochastic and mean square stability of continuous-time linear systems with Markovian jumping parameters and additive disturbances. SIAM J Control Optim 44(x): 1165–1191CrossRefMathSciNetGoogle Scholar
  12. 12.
    Gray WS., Gonzalez O (1998) Modelling electromagnetic disturbances in closed-loop computer controlled flight systems. In: IEEE 37th Conf. on Decision and Control, Philadelphia, Pennsylvania, pp. 359-364Google Scholar
  13. 13.
    Ito K, Morris KA (1998) An approximation theory of solutions to operator Riccati equations for H control. SIAM J Control Optim 36: 82–99MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Ji Y, Chizeck Y (1990) Controllability, stabilizability, and continuous-time Markovian jumping linear quadratic control. IEEE Trans Automat Control 35: 777–788MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Malhame R, Chong CY (1985) Electric load model synthesis by diffusion approximation in a high order hybrid state stochastic systems. IEEE Trans Automat Control 30: 854–860MATHCrossRefGoogle Scholar
  16. 16.
    Mariton M (1990) Jump linear systems in automatic control. Marcel Dekker, New YorkGoogle Scholar
  17. 17.
    Morris KA, Navasca C (2005) Solution of algebraic Riccati equations arising in control of partial differential equations. In: Control and Boundary Analysis, Lecture Notes in Pure Appl Math, Chapman Hall/CRC, Boca Raton, FL, pp. 257-280Google Scholar
  18. 18.
    Pazy A (1983) Semigroups of linear operators and applications to partial differential equations. Springer, New YorkMATHGoogle Scholar
  19. 19.
    Wonham WM (1968) On a matrix Riccati equation of stochastic control. SIAM J Control 6(4): 681–697CrossRefMathSciNetGoogle Scholar
  20. 20.
    Wonham WM (1970) Random differential equation of control theory. In: Bharucha-Reid AT (eds) Probab Meth in Appl Math, vol 2. Academic-Press, New YorkGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.National Laboratory for Scientific Computing -LNCC/CNPqRio de JaneiroBrazil

Personalised recommendations