Development of an algorithm for solving the problem of optimal control on a finite interval for a nonlinear system of a three-sector economic cluster

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.252866

Keywords:

optimal control problem, three-sector economic cluster, Lagrange multiplier method, nonlinear system, quadratic functional

Abstract

The problem of optimal control over a finite time interval for a mathematical model of a three-sector economic cluster is posed. The economic system is reduced by means of transformations to the optimal control problem for one class of nonlinear systems with coefficients depending on the state of the control object. Two optimal control problems for one class of nonlinear systems with and without control constraints are considered. The nonlinear objective functional in these problems depends on the control and state of the object. Then, using the results of solving optimal control problems on a finite interval, an algorithm for solving the problem for a nonlinear system of a three-sector economic cluster is developed. A nonlinear control based on the feedback principle using Lagrange multipliers of a special kind is found. The results obtained for nonlinear systems are used to construct the control parameters of a mathematical model of a three-sector economic cluster at a finite time interval with a given functional and various initial conditions. The results of the system state calculation are shown in the figures, the optimal controls satisfy the given constraints. The optimal distribution of labor and investment resources for a three-sector economic cluster is determined. They ensure that the system is brought into an equilibrium state and satisfy balance ratios. These results are useful for practice and are important because there are a number of optimal control problems when it is necessary to transfer a system from an initial state to a desired final state for a given time interval. Such problems often arise for an economic system when a certain level of development is required.

Author Biographies

Zainelkhriet Murzabekov, Al-Farabi Kazakh National University

Doctor of Technical Sciences, Professor

Department of Artificial Intelligence and Big Data

Marek Milosz, Lublin University of Technology

PhD, Professor, Head of Department, Head of Laboratory

Department of Software Engineering and Database Systems

Laboratory of Motion Analysis and Interface Ergonomics

Kamshat Tussupova, Al-Farabi Kazakh National University

PhD, Senior Lecturer

Department of Information Systems

Gulbanu Mirzakhmedova, Al-Farabi Kazakh National University

Master of MCM, Senior Lecturer

Department of Information Systems

References

  1. Pontryagin, L. S., Boltyanskii, V. G., Gamkrelidze, R. V., Mishchenko, E. F. (1962). The Mathematical Theory of Optimal Processes. Interscience Publishers, 360.
  2. Bellman, R., Kalaba, R. (1965). Dynamic programming and modern control theory. Academic Press.
  3. Krotov, V. F., Gurman, V. I. (1973). Metody i zadachi optimal'nogo upravleniya. Moscow: Nauka, 448.
  4. Porter, M. E. (2008). On competition. Boston: Harvard Business School Publishing.
  5. Kolemaev, V. A. (2008). Optimal'niy sbalansirovanniy rost otkrytoy trekhsektornoy ekonomiki. Prikladnaya ekonometrika, 3 (11), 15–42. Available at: http://pe.cemi.rssi.ru/pe_2008_3_15-42.pdf
  6. Zhang, J. S. (2011). The analytical solution of balanced growth of non-linear dynamic multi-sector economic model. Economic Modelling, 28 (1-2), 410–421. doi: https://doi.org/10.1016/j.econmod.2010.08.007
  7. Aseev, S. M., Besov, K. O., Kryazhimskii, A. V. (2012). Infinite-horizon optimal control problems in economics. Russian Mathematical Surveys, 67(2), 195–253. doi: https://doi.org/10.1070/rm2012v067n02abeh004785
  8. Klamka, J. (1999). Constrained controllability of dynamic systems. International Journal of Applied Mathematics and Computer Science, 9 (2), 231–244. Available at: https://bibliotekanauki.pl/articles/908304
  9. Klamka, J. (2002). Controllability of nonlinear discrete systems. Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301). doi: https://doi.org/10.1109/acc.2002.1025394
  10. Milosz, M., Murzabekov, Z., Tussupova, K., Usubalieva, S. (2018). Optimisation of Discrete Processes with Bounded Control. Information Technology And Control, 47 (4). doi: https://doi.org/10.5755/j01.itc.47.4.19933
  11. Mitkowski, W., Bauer, W., Zagórowska, M. (2017). Discrete-time feedback stabilization. Archives of Control Sciences, 27 (2), 309–322. doi: https://doi.org/10.1515/acsc-2017-0020
  12. Afanas’ev, V. N., Orlov, P. V. (2011). Suboptimal control of feedback-linearizable nonlinear plant. Journal of Computer and Systems Sciences International, 50 (3), 365–374. doi: https://doi.org/10.1134/s1064230711030026
  13. Huang, Y. (2017). Neuro‐observer based online finite‐horizon optimal control for uncertain non‐linear continuous‐time systems. IET Control Theory & Applications, 11 (3), 401–410. doi: https://doi.org/10.1049/iet-cta.2016.0966
  14. Huang, Y. (2018). Optimal guaranteed cost control of uncertain non‐linear systems using adaptive dynamic programming with concurrent learning. IET Control Theory & Applications, 12 (8), 1025–1035. doi: https://doi.org/10.1049/iet-cta.2017.1131
  15. Vamvoudakis, K. G., Miranda, M. F., Hespanha, J. P. (2016). Asymptotically Stable Adaptive–Optimal Control Algorithm With Saturating Actuators and Relaxed Persistence of Excitation. IEEE Transactions on Neural Networks and Learning Systems, 27 (11), 2386–2398. doi: https://doi.org/10.1109/tnnls.2015.2487972
  16. Yang, X., Liu, D., Wei, Q. (2014). Online approximate optimal control for affine non‐linear systems with unknown internal dynamics using adaptive dynamic programming. IET Control Theory & Applications, 8 (16), 1676–1688. doi: https://doi.org/10.1049/iet-cta.2014.0186
  17. Wang, H., Liu, X., Liu, K. (2016). Robust Adaptive Neural Tracking Control for a Class of Stochastic Nonlinear Interconnected Systems. IEEE Transactions on Neural Networks and Learning Systems, 27 (3), 510–523. doi: https://doi.org/10.1109/tnnls.2015.2412035
  18. Wang, H., Shi, P., Li, H., Zhou, Q. (2017). Adaptive Neural Tracking Control for a Class of Nonlinear Systems With Dynamic Uncertainties. IEEE Transactions on Cybernetics, 47 (10), 3075–3087. doi: https://doi.org/10.1109/tcyb.2016.2607166
  19. Dagdougui, H., Ouammi, A., Sacile, R. (2014). Optimal control of a network of power microgrids using the Pontryagin's minimum principle. IEEE Transactions on Control Systems Technology, 22 (5), 1942–1948. doi: https://doi.org/10.1109/tcst.2013.2293954
  20. Wang, D., He, H., Zhao, B., Liu, D. (2017). Adaptive near‐optimal controllers for non‐linear decentralised feedback stabilisation problems. IET Control Theory & Applications, 11 (6), 799–806. doi: https://doi.org/10.1049/iet-cta.2016.1383
  21. Afanas’ev, A. P., Dzyuba, S. M., Emelyanova, I. I. (2015). Analytical and Numerical Investigation for the Problem of Optimal Control of Nonlinear System via Quadratic Criteria. Procedia Computer Science, 66, 23–32. doi: https://doi.org/10.1016/j.procs.2015.11.005
  22. Dmitriev, M. G., Makarov, D. A. (2017). The stabilizing composite control in a weakly nonlinear singularly perturbed control system. 2017 21st International Conference on System Theory, Control and Computing (ICSTCC). doi: https://doi.org/10.1109/icstcc.2017.8107099
  23. Aipanov, S. A., Murzabekov, Z. N. (2014). Analytical solution of a linear quadratic optimal control problem with control value constraints. Journal of Computer and Systems Sciences International, 53 (1), 84–91. doi: https://doi.org/10.1134/s1064230713060026
  24. Murzabekov, Z., Miłosz, M., Tussupova, K. (2018). The Optimal Control Problem with Fixed-End Trajectories for a Three-Sector Economic Model of a Cluster. Lecture Notes in Computer Science, 382–391. doi: https://doi.org/10.1007/978-3-319-75417-8_36

Downloads

Published

2022-02-28

How to Cite

Murzabekov, Z., Milosz, M., Tussupova, K., & Mirzakhmedova, G. (2022). Development of an algorithm for solving the problem of optimal control on a finite interval for a nonlinear system of a three-sector economic cluster. Eastern-European Journal of Enterprise Technologies, 1(3(115), 43–52. https://doi.org/10.15587/1729-4061.2022.252866

Issue

Section

Control processes