On the modification of direct methods for solving optimal control problems of stationary thermal processes
Keywords:
stationary thermal process, control, system state, objective functional, gradient method, modified algorithm, efficiencyAbstract
Purpose. The aim of the study is to apply modified gradient-type methods to problems of optimal control of one-dimensional stationary thermal processes and to conduct a comparative analysis of the effectiveness of the classical and modified approaches using the example of solving specific problems. Design / Method / Approach. The research is focused on the development and numerical implementation of approximation-iteration algorithms based on the grid method for the analysis of controlled thermostatic systems modeled by differential equations with variable coefficients. For the numerical solution of the primary and adjoint boundary value problems, second-order accuracy difference schemes are used. To find the lower boundary of the objective functional, gradient-type minimization methods are used, both with and without control constraints. Findings. The proposed modified computational schemes demonstrate an increase in the efficiency of the classical grid method in terms of the amount of required computational costs and the accuracy of the obtained approximate solutions. Theoretical Implications. Expanding the possibilities of applying theoretically substantiated direct methods of accelerated convergence to solving optimal control problems of stationary thermal processes. Practical Implications. Creating an effective computational tool for solving optimal control problems of stationary thermal processes, which can be applied in practice. Originality / Value. Implementation of new computational schemes of accelerated convergence of modified gradient-type methods for the specified class of optimal control problems. Research Limitations / Future Research. The research limitations are due only to the properties of the programming language and software used. Further research involves applying the proposed modified approach to solving more complex optimal control problems, including multidimensional and phase-constrained ones. Article Type. Applied Research.
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Abidi, S., & Satouri, J. (2023). New numerical method for solving optimal control problem for the stationary Navier-Stokes equations. AIMS Mathematics, 8(9), 21484–21500. https://doi.org/10.3934/math.20231095
Balashova, S. D. (1996). On solving minimization problems using projection-iterative methods [In Russian]. Matematičeskie modeli i vyčislitelʹnye metody v prikladnyh zadačah, 99–104. https://e.surl.li/kdqhsa
Baldini, S., Barbi, G., Cervone, A., Giangolini, F., Manservisi, S., & Sirotti, L. (2025). Optimal Control of Heat Equation by Coupling FVM and FEM Codes. Mathematics, 13(2), 238. https://doi.org/10.3390/math13020238
Baranovskii, E. S., Brizitskii, R. V., & Saritskaia, Z. Yu. (2024). Boundary Value and Control Problems for the Stationary Heat Transfer Model with Variable Coefficients. Journal of Dynamical and Control Systems, 30(3). https://doi.org/10.1007/s10883-024-09698-w
Fontes, F. A. C. C., Halder, A., Becerril, J., & Kumar, P. R. (2019). Optimal Control of Thermostatic Loads for Planning Aggregate Consumption: Characterization of Solution and Explicit Strategies. IEEE Control Systems Letters, 3(4), 877–882. https://doi.org/10.1109/lcsys.2019.2918978
Gangl, P., Löscher, R., & Steinbach, O. (2025). Regularization and finite element error estimates for elliptic distributed optimal control problems with energy regularization and state or control constraints. Computers & Mathematics with Applications, 180, 242–260. https://doi.org/10.1016/j.camwa.2024.12.021
Hart, E. (2017). Models and projection-iterative modifications of the variational-grid methods in problems of elastic-plastic deformation of structurally inhomogeneous solids [Doctoral dissertation, in Ukrainian, Oles Honchar Dnipro National University]. https://nrat.ukrintei.ua/en/searchdoc/0517U000726
Hart, L. (2013). Projection-iterative realization of the method of conditional gradient of functional minimizing in Hilbert space [In Russian]. System research and information technologies, (3), 104-117. http://journal.iasa.kpi.ua/article/view/44151
Hart, L. (2017). Projection-iteration methods for solving operator equations and infinite-dimensional optimization problems [Doctoral dissertation, in Ukrainian, Oles Honchar Dnipro National University]. https://nrat.ukrintei.ua/en/searchdoc/0517U000442
Hart, L. (2022). Combined Approach to Solving the Neumann Problem for a Parametric Quasilinear Elliptic Equation. In International Symposium on Engineering and Manufacturing (pp. 316-328). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-03877-8_28
Hart, L., & Yatsechko, N. (2021). Numerical algorithms for solving an elliptic optimal control problem with a power-law nonlinearity. Artificial Intelligence, 26(2), 64–76. https://doi.org/10.15407/jai2021.02.064
Hou, J., Li, X., Wan, H., Sun, Q., Dong, K., & Huang, G. (2022). Real-time optimal control of HVAC systems: Model accuracy and optimization reward. Journal of Building Engineering, 50, 104159. https://doi.org/10.1016/j.jobe.2022.104159
Hu, M., Song, H., Wu, J., & Yang, J. (2024). Inexact primal-dual active set iteration for optimal distribution control of stationary heat or cold source. Journal of Global Optimization, 91(1), 235–253. https://doi.org/10.1007/s10898-024-01437-6
Karwa, R. (2020). Heat and mass transfer. Springer Nature. https://books.google.com/books?id=4lXsDwAAQBAJ
Kien, B. T., Rösch, A., Son, N. H., & Tuyen, N. V. (2023). FEM for Semilinear Elliptic Optimal Control with Nonlinear and Mixed Constraints. Journal of Optimization Theory and Applications, 197(1), 130–173. https://doi.org/10.1007/s10957-023-02187-3
Neittaanmaki, P., Sprekels, J., & Tiba, D. (2006). Optimization of elliptic systems: Theory and applications (Springer Monographs in Mathematics). Springer New York. https://doi.org/10.1007/b138797
Samarskii, A. A. (2001). The theory of difference schemes. CRC Press. https://doi.org/10.1201/9780203908518
Samarskiĭ, A. A., & Vabishchevich, P. N. (2007). Numerical Methods for Solving Inverse Problems of Mathematical Physics (Vol. 52). Walter de Gruyter. https://books.google.com/books?id=9IjbSaVdNaoC
Titouche, S., Spiteri, P., Messine, F., & Aidene, M. (2015). Optimal control of a large thermic process. Journal of Process Control, 25, 50–58. https://doi.org/10.1016/j.jprocont.2014.09.015
Vallejos, M. (2012). Multigrid methods for elliptic optimal control problems with pointwise state constraints. Numerical Mathematics: Theory, Methods and Applications, 5(1), 99-109. https://doi.org/10.4208/nmtma.2011.m12si06
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