Adaptive weight adjustment in multicriteria control of nonlinear systems based on a Lyapunov function

System Analysis and Control
Authors:
Abstract:

The paper develops an approach to the control of nonlinear dynamic systems within a multicriteria optimization framework in which criterion weights are adjusted during the control process. The method relies on a Lyapunov function that is included both in the performance functional and in trajectory constraints. A numerical algorithm is proposed for dynamic tuning of the criterion weights. The criteria are stability and control energy consumption, while their relative importance changes depending on the current system state and trajectory. Adaptation is performed based on the current value of the Lyapunov function and the system state, which enables automatic prioritization of objectives without operator intervention. A numerical analysis of the system behavior under different weight-adaptation schemes is carried out. For the considered models, the simulation results show that adaptive weight adjustment can provide faster decay of the Lyapunov function and lower control energy consumption compared with fixed scalarization. The method can be applied to control problems for nonlinear and multi-agent systems, including cyber-physical complexes, autonomous agents, and energy nodes.

  • References

    1. Zhadan V.G. Metody optimizatsii. Chast' II. Chislennye algoritmy [Optimization Methods. Part II. Numerical Algorithms]. Moscow: MFTI, 2015.

    2. Chernorutskii I.G. Metody optimizatsii v teorii upravleniia [Optimization methods in control theory]. St. Petersburg: Piter, 2004.

    3. Prokopenko N.Iu. Metody optimizatsii [Optimization methods]. Nizhny Novgorod: NNGASU, 2018.

    4. Kochenderfer M.J., Wheeler T.A. Algorithms for Optimization, 2nd ed. Cambridge, MA; London: MIT Press, 2025.

    5. Rostov N.V. Sequential multiobjective parametrical optimization of controllers for nonlinear control system. St. Petersburg State Polytechnical University Journal. Computer Science. Telecommunications and Control Systems, 2010, Vol. 113, No. 6, Pp. 44–50.

    6. Rostov N.V. Multiobjective parameter optimization of digital controllers with regard to the influence of nonlinearities and external disturbances. St. Petersburg State Polytechnical University Journal. Computer Science. Telecommunications and Control Systems, 2014, Vol. 193, No. 2, Pp. 91–98.

    7. Venkat A.N. Distributed Model Predictive Control: Theory and Applications, PhD dissertation. Madison: University of Wisconsin–Madison, 2006.

    8. Tang Y., Wang X. Optimal Output Consensus for Nonlinear Multiagent Systems with Both Static and Dynamic Uncertainties. IEEE Transactions on Automatic Control, 2021, Vol. 66, No. 4. Pp. 1733–1740. DOI: 10.1109/TAC.2020.2996978

    9. Fershtadt M.I., Shashikhin V.N. Multi-criteria control of large-scale nonlinear dynamical systems without linearization, based on Lyapunov functions. Computing, Telecommunications and Control, 2025, Vol. 18, No. 4, Pp. 112–122. DOI: 10.18721/JCSTCS.18410

    10. Shashikhin V.N., Budnik S.V., Golovina K.O. Control of the spectrum of Lyapunov characteristic exponents in nonlinear large-scale systems. Computing, Telecommunications and Control, 2021, Vol. 14, No. 4, Pp. 37–51. DOI: 10.18721/JCSTCS.14404

    11. Zhang C., Fu J. Multi-objective dynamic optimization of path-constrained switched systems. Automa-tica, 2024, Vol. 159, Art. no. 111326. DOI: 10.1016/j.automatica.2023.111326

    12. Tao M., Li Q., Yu J. Multi-Objective Dynamic Path Planning with Multi-Agent Deep Reinforcement Learning. Journal of Marine Science and Engineering, 2024, Vol. 13, No. 1, Art. no. 20. DOI: 10.3390/jmse13010020

    13. Lin J., He C., Tian Y., Pan L. Variable Reconstruction for Evolutionary Expensive Large-Scale Multiobjective Optimization and Its Application on Aerodynamic Design. IEEE/CAA Journal of Automatica Sinica, 2025, Vol. 12, No. 4, Pp. 719–733. DOI: 10.1109/JAS.2024.124947

    14. Zheng Y., Wang Y., Li S. Adaptive control Lyapunov function based model predictive control for continuous nonlinear systems. International Journal of Robust and Nonlinear Control, 2022, Vol. 33, No. 2, Pp. 1254–1266. DOI: 10.1002/rnc.6409

    15. Zheng X.-Y., Yan H.-S. Lyapunov-based stochastic model predictive control of stochastic nonlinear systems with input-delay using multi-dimensional Taylor network. Journal of the Franklin Institute, 2025, Vol. 362, No. 9, Art. no. 107694. DOI: 10.1016/j.jfranklin.2025.107694

    16. Baheri A. Distributionally robust Lyapunov–Barrier Networks for safe and stable control under uncertainty. Results in Control and Optimization, 2025, Vol. 19, Art. no. 100556. DOI: 10.1016/j.rico.2025.100556

    17. Stiti C., Benrabah M., Aouaichia A., Oubelaid A., Bajaj M., Tuka M.B., Kara K. Lyapunov-based neural network model predictive control using metaheuristic optimization approach. Scientific Reports, 2024, Vol. 14, Art. no. 18760. DOI: 10.1038/s41598-024-69365-9

    18. Nersesov S.G., Haddad W.M. Control vector Lyapunov functions for large-scale impulsive dynamical systems. Nonlinear Analysis: Hybrid Systems, 2007, Vol. 1, No. 2, Pp. 223–243. DOI: 10.1016/j.nahs.2006.10.006

    19. Chen Y.-H. Nonlinear Adaptive Optimal Control Design and Implementation for Trajectory Tracking of Four-Wheeled Mecanum Mobile Robots. Mathematics, 2024, Vol. 12, No. 24, Art. no. 4013. DOI: 10.3390/math12244013

    20. Yan Z., Zhang M., Zhou J., Yue L. Distributed Lyapunov-Based Model Predictive Control for AUV Formation Systems with Multiple Constraints. Journal of Marine Science and Engineering, 2024, Vol. 12, No. 3, Art. no. 363. DOI: 10.3390/jmse12030363

    21. Peitz S., Dellnitz M. A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction. Mathematical and Computational Applications, 2018, Vol. 23, No. 2, Art. no. 30. DOI: 10.3390/mca23020030

    22. Wen J., Li L., Wu Q., Li K., Lu J. Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions. Actuators, 2024, Vol. 13, No. 12, Art. no. 522. DOI: 10.3390/act13120522

    23. Ionescu C.M., Caruntu C.F., Cajo R., Ghita M., Crevecoeur G., Copot C. Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study. Processes, 2019, Vol. 7, No. 11, Art. no. 778. DOI: 10.3390/pr7110778

    24. El Sayed M.A., Farahat A.F., Elsisy M.A., Alsabaan M., Ibrahem M.I., Elwahsh H. Two TOPSIS-Based Approaches for Multi-Choice Rough Bi-Level Multi-Objective Nonlinear Programming Problems. Mathema-tics, 2025, Vol. 13, No. 8, Art. no. 1242. DOI: 10.3390/math13081242

    25. Nelyubin A.P., Podinovski V.V. Multicriteria Problems with Importance-Ordered Criteria Groups. Auto-mation and Remote Control, 2022, Vol. 83, Pp. 1108–1122. DOI: 10.1134/S0005117922070074

    26. Podinovski V.V., Potapov M.A. Analysis of the Sensitivity of Solutions of Multi-Criteria Problems Based on Parametric Partial Preference Relations. Automation and Remote Control, 2019, Vol. 80, Pp. 1294–1303. DOI: 10.1134/S0005117919070075

    27. Balandin D.V., Kogan M.M. Multicriteria Robust Generalized H2 and γ0 Controllers with Applica-tion to Stabilization of a Rotor in Electromagnetic Bearings. Automation and Remote Control, 2018, Vol. 79, Pp. 996–1012. DOI: 10.1134/S0005117918060024

    28. Demidenko O.M., Borchik E.M., Yakimov A.I. Multi-criterial optimization of resource distribution in the process of finished products. Problems of Physics, Mathematics and Technics, 2022, Vol. 52, No. 3, Pp. 90–96. DOI: 10.54341/20778708_2022_3_52_90

    29. Melnikov A.V., Karavaev A.A., Zheleznyakov A.O. Analysis of the main approaches to modeling management processes for special-purpose organizational and technical systems. Herald of Dagestan State Tech-nical University. Technical Sciences, 2024, Vol. 51, No. 1, Pp. 153–165. DOI: 10.21822/2073-6185-2024-51-1-153-165

    30. Belykh M.A. Formalization of a multi-criteria transport task with time constraints. Modeling, Optimi-zation and Information Technology, 2024, Vol. 12, No. 2, Pp. 1–9. DOI: 10.26102/2310-6018/2024.45.2.027

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Previous article