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Moment Characteristic Method in the Optimal Control Theory of Diffusion-Type Stochastic Systems

  • CONTROL IN STOCHASTIC SYSTEMS AND UNDER UNCERTAINTY CONDITIONS
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Abstract

In this paper, we provide a substantive description of one of the methods for solving problems of the optimal programmed control of diffusion-type stochastic systems with a quadratic quality functional on a finite time interval that allows reducing the stochastic formulation of the question to a deterministic one. We try to present the main ideas, advantages, and disadvantages of the method, as well as to outline as widely as possible the range of problems, to which this method can be applied in an accessible (but mathematically rigorous) form. Here, we do not provide technical details of using the method for solving specific problems. However, there are references in the paper, where these details can be found. New results, which are illustrated by examples that are directly related to the well-known applied problems of optimal control, are obtained. A rather comprehensive review of the current practical applications of the moment characteristic method is provided.

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REFERENCES

  1. P. J. McLane, “Linear optimal stochastic control using instantaneous output feedback,” Int. J. Control 13, 383–396 (1971).

    Article  MathSciNet  Google Scholar 

  2. Yu. I. Paraev, Introduction to the Statistical Dynamics of Control and Filtering Processes (Sov. Radio, Moscow, 1976) [in Russian].

    Google Scholar 

  3. M. M. Khrustalev and K. A. Tsar’kov, “Sufficient relative minimum conditions in the optimal control problem for quasilinear stochastic systems,” Autom. Remote Control 79, 2169 (2018).

    Article  MathSciNet  Google Scholar 

  4. L. S. Pontryagin, V. G. Boltyanskii, R. S. Gamkrelidze, and E. F. Mischenko, The Mathematical Theory of Optimal Processes (Fizmatgiz, Moscow, 1961; Pergamon, Oxford, New York, 1964).

  5. V. F. Krotov and V. I. Gurman, Methods and Problems of Optimal Control (Nauka, Moscow, 1973) [in Russian].

    Google Scholar 

  6. W. H. Fleming and R. Rishel, Deterministic and Stochastic Optimal Control (Springer, New York, 1975).

    Book  Google Scholar 

  7. A. V. Panteleev and A. S. Bortakovskii, Control Theory in Examples and Tasks, The School-Book (Vyssh. Shkola, Moscow, 2003) [in Russian].

    Google Scholar 

  8. B. Øksendal, Stochastic Differential Equations: An Introduction with Applications, 6th ed. (Springer, Berlin, Heidelberg, 2014).

    MATH  Google Scholar 

  9. M. M. Khrustalev, “Nash equilibrium conditions in stochastic differential games with incomplete state awareness. I. Sufficient equilibrium conditions,” Izv. Akad. Nauk, Teor. Syst. Upravl., No. 6, 194–208 (1995).

  10. M. M. Khrustalev, “Nash equilibrium conditions in stochastic differential games where players information about a state is incomplete. II. Lagrange method,” J. Comput. Syst. Sci. Int. 35, 67 (1996).

    MATH  Google Scholar 

  11. V. S. Korolyuk, N. I. Portenko, A. V. Skorokhod, and A. F. Turbin, Handbook of Probability Theory and Mathematical Statistics (Nauka, Moscow, 1985) [in Russian].

    MATH  Google Scholar 

  12. I. I. Gikhman and A. V. Skorokhod, Stochastic Differential Equations (Nauk. Dumka, Kiev, 1968) [in Russian].

    MATH  Google Scholar 

  13. L. Socha, Linearization Methods for Stochastic Dynamic Systems (Springer, Berlin, Heidelberg, Germany, 2008).

    Book  Google Scholar 

  14. B. M. Miller and A. R. Pankov, Theory of Stochastic Processes in Examples and Problems (Fizmatlit, Moscow, 2002) [in Russian].

    Google Scholar 

  15. X. Mao, Stochastic Differential Equations and Applications (Harwood, Chichester, UK, 2007).

    MATH  Google Scholar 

  16. F. Klebaner, Introduction to Stochastic Calculus with Applications (Imperial College Press, London, 2001).

    MATH  Google Scholar 

  17. E. Allen, Modeling with Ito Stochastic Differential Equations (Springer, Dordrecht, Netherlands, 2007).

    MATH  Google Scholar 

  18. L. Arnold, Stochastic Differential Equations: Theory and Applications (Wiley, New York, 1973).

    Google Scholar 

  19. Ph. Hartman, Ordinary Differential Equations (Soc. Ind. Appl. Math., Philadelphia, 1987).

    MATH  Google Scholar 

  20. M. Athans, “The matrix minimum principle,” Inform. Control 11, 592–606 (1968).

    Article  MathSciNet  Google Scholar 

  21. M. M. Khrustalev, D. S. Rumyantsev, and K. A. Tsar’kov, “Optimization of quasilinear stochastic control-nonlinear diffusion systems,” Autom. Remote Control 78, 1028 (2013).

    Article  MathSciNet  Google Scholar 

  22. I. N?rasell, “An extension of the moment closure method,” Theor. Populat. Biol., No. 64, 233–239 (2003).

  23. K. R. Ghusinga, M. Soltani, A. Lamperski, S. Dhople, and A. Singh, “Approximate moment dynamics for polynomial and trigonometric stochastic systems,” arXiv:1703.08841 (2017).

  24. A. Singh and J. P. Hespanha, “Moment closure techniques for stochastic models in population,” IEEE Trans. Autom. Control 54, 1193–1203 (2009).

    Article  Google Scholar 

  25. D. Schnoerr, G. Sanguinetti, and R. Grima, “Comparison of different moment-closure approximations for stochastic chemical kinetics,” J. Chem. Phys. 143 (18) (2015). https://aip.scitation.org/doi/10.1063/1.4934990.

    Article  Google Scholar 

  26. M. Soltani, C. A. Vargas-Garcia, and A. Singh, “Conditional moment closure schemes for studying stochastic dynamics of genetic circuits,” IEEE Trans. Biomed. Circuits Syst. 9, 518–526 (2015).

    Article  Google Scholar 

  27. V. S. Pugachev and I. N. Sinitsyn, Stochastic Differential Systems. Analysis and Filtering (Nauka, Moscow, 1990) [in Russian].

    MATH  Google Scholar 

  28. A. V. Panteleev, E. A. Rudenko, and A. S. Bortakovskii, Nonlinear Control Systems: Description, Analysis and Synthesis (Vuzovsk. Kniga, Moscow, 2008) [in Russian].

    Google Scholar 

  29. T. Khalifa, A. Barbata, M. Zasadzinski, and A. H. Souley, “Asymptotic stability in probability of a square root stochastic process,” in Proceedings of the 2016 IEEE 55th Conference on Decision and Control CDC (IEEE, Las Vegas, USA, 2016). https://ieeexplore.ieee.org/document/7799094/.

    Google Scholar 

  30. K. A. Rybakov, “Sufficient optimality conditions in the problem of control of diffusion-hopping systems,” in Proceedings of the All-Russia Workshop on Control Problems, 2014 (IPU RAN, Moscow, 2014), pp. 734–744.

  31. E. Onegin and M. Khrustalev, “Optimal stabilisation of a quasilinear stochastic system with controllable parameters,” in Proceedings of the 2018 14th International Conference on Stability and Oscillations of Nonlinear Control Systems (Pyatnitskiy’s Conference) (IEEE, Moscow, 2018). https://ieeexplore.ieee.org/document/8408384.

    Google Scholar 

  32. A. Levin, “Deriving closed-form solutions for gaussian pricing models: a systematic time-domain approach,” Int. J. Theor. Appl. Finance 1, 349–376 (1998).

    Article  Google Scholar 

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Correspondence to M. M. Khrustalev or K. A. Tsar’kov.

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Translated by A. Ivanov

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Khrustalev, M.M., Tsar’kov, K.A. Moment Characteristic Method in the Optimal Control Theory of Diffusion-Type Stochastic Systems. J. Comput. Syst. Sci. Int. 58, 684–694 (2019). https://doi.org/10.1134/S1064230719050071

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  • DOI: https://doi.org/10.1134/S1064230719050071

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