Lyapunov-based MPC for uncertain nonlinear systems with delayed measurements using recurrent multi-dimensional Taylor network.
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| Title: | Lyapunov-based MPC for uncertain nonlinear systems with delayed measurements using recurrent multi-dimensional Taylor network. |
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| Authors: | Yan, Hong-Sen1,2 (AUTHOR) hsyan@seu.edu.cn, Zheng, Xiao-Yi1,2 (AUTHOR) |
| Source: | International Journal of Control. May2026, Vol. 99 Issue 5, p1523-1544. 22p. |
| Subjects: | Closed loop system stability, Predictive control systems, Chemical process control, Nonlinear control theory, Uncertain systems |
| Abstract: | For nonlinear systems with measurement delays and uncertain disturbances, most of the existing control methods are not effective enough to determine a priori initial condition set for the desirable closed-loop stability of the controller, and especially some of them cannot be applied directly to this control problem. Within this context, a Lyapunov-based model predictive control (LMPC) approach is proposed here to ensure the stability of the closed-loop system. The time-varying measurement delay is modelled in the framework of the LMPC scheme by setting appropriate constraints for uncertain nonlinear systems with time-varying measurement delays. And the recurrent multi-dimensional Taylor network (RMTN) model is developed to predict future states. RMTN, being of simple structure and high efficiency, is intended to match continuous system dynamics. The proposed predictive controller allows the stability region to be shaped using the system parameters. And the Lyapunov-based controller is introduced to formulate the optimisation problem. The LMPC scheme using the RMTN model is theoretically proven to be capable of keeping the state of the closed-loop system in the stable region in the presence of time-varying measurement delay, uncertain disturbance, and modelling error. The effectiveness of the proposed approach is verified by a chemical process. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | For nonlinear systems with measurement delays and uncertain disturbances, most of the existing control methods are not effective enough to determine a priori initial condition set for the desirable closed-loop stability of the controller, and especially some of them cannot be applied directly to this control problem. Within this context, a Lyapunov-based model predictive control (LMPC) approach is proposed here to ensure the stability of the closed-loop system. The time-varying measurement delay is modelled in the framework of the LMPC scheme by setting appropriate constraints for uncertain nonlinear systems with time-varying measurement delays. And the recurrent multi-dimensional Taylor network (RMTN) model is developed to predict future states. RMTN, being of simple structure and high efficiency, is intended to match continuous system dynamics. The proposed predictive controller allows the stability region to be shaped using the system parameters. And the Lyapunov-based controller is introduced to formulate the optimisation problem. The LMPC scheme using the RMTN model is theoretically proven to be capable of keeping the state of the closed-loop system in the stable region in the presence of time-varying measurement delay, uncertain disturbance, and modelling error. The effectiveness of the proposed approach is verified by a chemical process. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00207179 |
| DOI: | 10.1080/00207179.2025.2592700 |
