The Fischer information matrix calculation in the suppressed majority of cases is the most important and difficult part in the computing plan in engineering calculations at the problem solution of the experiments planning for the dynamic objects identification described by models in the states space. We will note that the unknown parameters which are subject to definition can be in the various combinations of the state and observationmatrixes. As a rule, the filters are used for the state vector definition and the fact that the exact values of the object parameters aren't known isn't considered. The errors connected with the dynamic objects parameters determination exert considerable impact on the estimates errors received at the state vector calculation. The steady-stateuse considerably simplifies the calculation technique of the Fischer information matrix and is relevant for the practical use. In this article the main ratios necessary for the elements calculation of the Fischer information matrix are considered in case the unknown parameters are both in a state matrix, and in thenoises  matrix. Within this work it is supposed that all transition processes have ended, the steady-state is considered. The Kalman filter with the updated sequence is used for the state vector estimation of this dynamic system. The calculation process of the Fischer information matrix elements in the steady-state can be represented into three main groups. One group of the equations is solved as the nonlinear algebraic equationssystem, the second group of the equations is solved as the linear algebraic equationssystem, the third group of the equations – as set of the linear equations, solvable consistently one after another.

1. Ljung L. System identification: theory for the user. New Jersey, Prentice Hall, 1987. 384 p. (Russ. ed.: L'yung L. Identifikatsiyasistem. Teoriyadlyapol'zovatelya.Translated from English. Moscow, Nauka Publ., 1991. 432 p.).

2. Eykhoff P. System identification: parameter and state estimation. London, John Wiley & Sons, 1974. 555 p. (Russ. ed.: Eikkhoff P. Osnovyidentifikatsiisistemupravleniya: otsenivanieparametrovisostoyaniya. Translated from English. Moscow, Mir Publ., 1975. 683 p.).

3. Mehra R.K. Optimal input signal for parameter estimation in dynamic system – survey and new results. IEEE Transactions on Automatic Control, 1974, vol. AC-19, no. 6, pp. 753–768.

4. Mehra R.K. On the identification of variences and adaptive Kalman filtering.IEEE Transactions on Automatic Control, 1970, vol. AC-15, no. 2, pp. 175–184.

5. Sage A.P., White C.C. Optimum system control. 2^nd ed. Englewood Cliffs, NJ, Prentice-Hall, 1977 (Russ. ed.: Seidzh E.P., Uait Ch.S. Optimal'noeupravleniesistemami. Moscow, Radio i sviaz' Publ., 1982. 392 p.).

6. Goodwin G.C., Payne R.L. Dynamic system identification: experiment design and data analysis. New York, Academic Press, 1977. 291 p.

7. Zabolotnov Yu.M. Optimal'noeupravlenienepreryvnymidinamicheskimisistemami [The optimum control of the continuous dynamic systems].Samara, Samarskiigosudarstvennyiaerokosmicheskiiuniversitet Publ., 2005.129 p.

8. Tsypkin Ya.Z. Informatsionnayateoriyaidentifikatsii [Information theory of identification]. Moscow, Nauka Publ., Fizmatlit Publ., 1995. 336 p.

9. Chernykh I.V. Simulink: sredasozdaniyainzhenernykhprilozhenii [Simulink: the environment of the engineering applications construction]. Ed. by V.G. Potemkin. Moscow, Dialog-MIFI Publ., 2003. 496 p.

10. Oshchepkov A.Yu. [Adaptive management of linear objects with inertia using of discrete fast algorithms].XII Vserossiiskoesoveshchaniepoproblemamupravleniya VSPU-2014 [Proceedings of the XII All-Russian meeting on problems of management VSPU–2014], Moscow, 16–19 June 2014, pp. 2332–2337.In Russian. Available at: http://vspu2014.ipu.ru/proceedings/vspu2014.zip (accessed 23.11.2017).

11. Khranilov V.P. [Internal operators identification of management models for technical systems design problems]. XII Vserossiiskoesoveshchaniepoproblemamupravleniya VSPU-2014 [Proceedings of the XII All-Russian meeting on problems of management VSPU–2014], Moscow, 16–19 June 2014, pp. 3281–3288.In Russian. Available at: http://vspu2014.ipu.ru/proceedings/vspu2014.zip (accessed 23.11.2017).

12. Voevoda A.A., Troshina G.V. VychislenieinformatsionnoimatritsyFisheradlyalineinykhstatsionarnykhdiskretnykhsistem s neizvestnymiparametrami v modelyakhdinamikiinablyudeniya [Fischer information matrix calculation for linear stationary discrete systems with unknown parameters in dynamics and supervision models]. SborniknauchnykhtrudovNovosibirskogogosudarstvennogotekhnicheskogouniversiteta – Transaction of scientific papers of the Novosibirsk state technical university, 2006, no. 2 (44), pp. 29–34.

13. Voevoda A.A., Troshina G.V. Ispol'zovanieinformatsionnoimatritsyFishe-raprivyboresignalaupravleniyadlyaotsenkiparametrovmodeleidinamikiinablyudeniyaob"ektovnevysokogoporyadka [The Fischer information matrix use at the control signal choice for the parameters estimation in the dynamic and measurement models of the low order objects]. SborniknauchnykhtrudovNovosibirskogogosudarstvennogotekhnicheskogouniversiteta – Transaction of scientific papers of the Novosibirsk state technical university, 2006, no. 3 (45), pp. 19–24.

14. Milovanov M.V., Troshina G.V. [The program module development for the dynamic object identification according to the steady-state].Naukaiobrazovanie v sovremennomobshchestve: vektorrazvitiya: sborniknauchnykhtrudovpomaterialammezhdunarodnoinauchno-prakticheskoikonferentsii 3 aprelya 2014 g. [Science and education in modern society: a vector of development: a collection of scientific papers on the basis of an international scientific and practical conference on April 3, 2014]. Moscow, AR-Konsalt Publ., 2014, pt. 4, pp. 114–116. (In Russian).

15. Voevoda A.A., Troshina G.V. Rekurrentnyimetodotsenivaniyaparametrov v dinamicheskomob"ekte [A recurrent method for parameter estimation in the dynamic object]. NauchnyivestnikNovosibirskogogosudarstvennogotekhnicheskogouniversiteta – Science bulletin of the Novosibirsk state technical university, 2016, no. 4 (65), pp. 7–18.

16. Voevoda A.A., Troshina G.V. O nekotorykhmetodakhfil'tratsii v zadacheidentifikatsii [About some filtration methods in the identification problem]. SborniknauchnykhtrudovNovosibirskogogosudarstvennogotekhnicheskogouniversiteta – Transaction of scientific papers of the Novosibirsk state technical university, 2014, no. 2 (76), pp. 16–25.

17. Voevoda A.A., Troshina G.V. Ob otsenkevektorasostoyaniyaivektoraparametrov v zadacheidentifikatsii [About parameters vector estimation and state vector estimation in identification problem]. SborniknauchnykhtrudovNovosibirskogogosudarstvennogotekhnicheskogouniversiteta – Transaction of scientific papers of the Novosibirsk state technical university, 2014, no. 4 (78), pp. 53–68.doi: 10.17212/2307-6879-2014-4-53-68.

18. Voevoda A.A., Troshina G.V. Modelirovaniefil'traKalmana s obnovlennoiposledovatel'nost'yu v srede Simulink [The modelling of the Kalman filter with the updated sequence in Simulink environment]. SborniknauchnykhtrudovNovosibirskogogosudarstvennogotekhnicheskogouniversiteta – Transaction of scientific papers of the Novosibirsk state technical university, 2015, no. 2 (80), pp. 7–17.doi: 10.17212/2307-6879-2015-2-7-17.

19. Troshina G.V. Vychislitel'nyeaspektyzadachivosstanovleniyavektorasostoyaniyadlyamodeli s netochnozadannymiparametrami [Computing aspects of problem of the state vector recovering for models with inexact given parameters].SborniknauchnykhtrudovNovosibirskogogosudarstvennogotekhnicheskogouniversiteta – Transaction of scientific papers of the Novosibirsk state technical university, 2008, no. 3 (53), pp. 25–34.

20. Troshina G.V. Ob ispol'zovaniifil'traKalmanapriidentifikatsiidinamicheskikhsistem [About Kalman filter using for dynamic systems identification].SborniknauchnykhtrudovNovosibirskogogosudarstvennogotekhnicheskogouniversiteta – Transaction of scientific papers of the Novosibirsk state technical university, 2014, no. 3 (77), pp. 37–52.

21. Troshina G.V. Modelirovaniedinamicheskikhob"ektov v srede Simulink. Ch. 2 [The dynamic objects modelling in Simulink environment. Pt. 2].SborniknauchnykhtrudovNovosibirskogogosudarstvennogotekhnicheskogouniversiteta – Transaction of scientific papers of the Novosibirsk state technical university, 2015, no. 4 (82), pp. 31–41.doi: 10.17212/2307-6879-2015-4-31-41.

22. Voevoda A.A., Troshina G.V. Stabilizatsiyaneminimal'nofazovogoob"ek-ta s ispol'zovaniem PI-regulyatora [The stabilization of not-minimum-phase object with the PI regulator use]. SborniknauchnykhtrudovNovosibirskogogosudarstvennogotekhnicheskogouniversiteta – Transaction of scientific papers of the Novosibirsk state technical university, 2015, no. 4 (82), pp. 21–30. doi:10.17212/2307-6879-2015-4-21-30.