Geodesic
Hierarchical optimal control with random resource constraints
Itogi nauki i tehniki. Sovremennaâ matematika i eë priloženiâ. Tematičeskie obzory, Proceedings of the All-Russian Scientific Conference «Differential Equations and Their Applications» dedicated to the 85th anniversary of Professor M. T. Terekhin. Ryazan State University named for S. A. Yesenin, Ryazan, May 17-18, 2019. Part 2, Tome 186 (2020), pp. 91-101.

Voir la notice de l'article provenant de la source Math-Net.Ru

We consider a method for solving the problem of optimal control of a composite dynamic system with random resource constraints. The method is based on the method of hierarchical optimization, which includes iterative solution of local problems and the coordination problem. To solve local problems, we apply the model predictive control. Taking into account random resource constraints leads to the solution of one-stage problems of stochastic programming.
Keywords: complex system, hierarchical optimal control, stochastic programming. 
@article{INTO_2020_186_a11,
     author = {O. Yu. Maryasin and A. S. Kolodkina},
     title = {Hierarchical optimal control with random resource constraints},
     journal = {Itogi nauki i tehniki. Sovremenna\^a matematika i e\"e prilo\v{z}eni\^a. Temati\v{c}eskie obzory},
     pages = {91--101},
     publisher = {mathdoc},
     volume = {186},
     year = {2020},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/INTO_2020_186_a11/}
}
TY  - JOUR
AU  - O. Yu. Maryasin
AU  - A. S. Kolodkina
TI  - Hierarchical optimal control with random resource constraints
JO  - Itogi nauki i tehniki. Sovremennaâ matematika i eë priloženiâ. Tematičeskie obzory
PY  - 2020
SP  - 91
EP  - 101
VL  - 186
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/INTO_2020_186_a11/
LA  - ru
ID  - INTO_2020_186_a11
ER  - 
%0 Journal Article
%A O. Yu. Maryasin
%A A. S. Kolodkina
%T Hierarchical optimal control with random resource constraints
%J Itogi nauki i tehniki. Sovremennaâ matematika i eë priloženiâ. Tematičeskie obzory
%D 2020
%P 91-101
%V 186
%I mathdoc
%U http://geodesic.mathdoc.fr/item/INTO_2020_186_a11/
%G ru
%F INTO_2020_186_a11
O. Yu. Maryasin; A. S. Kolodkina. Hierarchical optimal control with random resource constraints. Itogi nauki i tehniki. Sovremennaâ matematika i eë priloženiâ. Tematičeskie obzory, Proceedings of the All-Russian Scientific Conference «Differential Equations and Their Applications» dedicated to the 85th anniversary of Professor M. T. Terekhin. Ryazan State University named for S. A. Yesenin, Ryazan, May 17-18, 2019. Part 2, Tome 186 (2020), pp. 91-101. http://geodesic.mathdoc.fr/item/INTO_2020_186_a11/

[1] Artamonov A. G., Volodin V. M., Avdeev V. G., Matematicheskoe modelirovanie i optimizatsiya plazmo-khimicheskikh protsessov, Khimiya, M., 1989

[2] Lesdon L., Optimizatsiya bolshikh sistem, Nauka, M., 1975 | MR

[3] Messarovich M., Mako D., Takakhara I., Teoriya ierarkhicheskikh mnogourovnevykh sistem, Mir, M., 1973

[4] Singkh M., Titli A., Sistemy: dekompozitsiya, optimizatsiya i upravlenie, Mashinostroenie, M., 1986 | MR

[5] Yudin D. B., Matematicheskie metody upravleniya v usloviyakh nepolnoi informatsii: Zadachi i metody stokhasticheskogo programmirovaniya, KRASAND, M., 2017

[6] Farina M., Scattolini R., “Distributed predictive control: A non-cooperative algorithm with neighbor-to-neighbor communication for linear systems”, Automatica., 48:6 (2012), 1088–1096 | DOI | MR | Zbl

[7] Findeisen W., Control and coordination in hierarchical system, Wiley, New York, 1980 | MR

[8] Lamoudi M. Y., Alamir M. Beguery P., “Distributed constrained model predictive control based on bundle method for building energy management”, Proc. 50th IEEE Conf. on Decision and Control and European Control Conference (December 12-15, 2011, Orlando, FL, USA), IEEE, 2011, 8118–8124

[9] Mesbah A., Streif S., Findeisen R., Braatz R. D., “Stochastic nonlinear model predictive control with probabilistic constraints”, Am. Control Conf., 2014, 2413–2419 | MR

[10] Moroşan P. D., Buisson J., Bourdais R., “Distributed MPC for multi-zone temperature regulation with coupled constraints”, IFAC Proc. Vol., 44:1 (2011), 1552–-1557 | DOI

[11] Moroşan P. D., Buisson J., Bourdais R., “A distributed MPC strategy based on Benders decomposition applied to multi-source multizone temperature regulation”, J. Process Control., 21 (2011), 729–737 | DOI

[12] Scattolini R., “Architectures for distributed and hierarchical Model Predictive Control. A review”, J. Process Control., 19 (2009), 723–731 | DOI

[13] Wang L., Model Predictive Control System Design and Implementation using MATLAB, Springer-Verlag, London, 2006