Robust event-triggered distributed min–max model predictive control of continuous-time non-linear systems

• Published in: IET control theory & applications Vol. 14; no. 19; pp. 3320 - 3329
• Main Authors: Li, Anni, Sun, Jitao
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 21.12.2020
• Subjects: Brief Paper; continuous‐time nonlinear systems; control system synthesis; conventional model predictive control; developed min–max RETDMPC; distributed control; event‐triggered control; exploiting saving system resources; linear systems; minimax techniques; min–max model predictive control; min–max RETDMPC methodology; multiagent systems; multi‐agent systems; networked control systems; nonlinear control systems; parametric uncertainties; predictive control; robust control; robust event‐triggered; sensor networks; system state; uncertain systems; unknown uncertainties; multi-agent systems; conventional model predictive control; distributed control; uncertain systems; min–max RETDMPC methodology; parametric uncertainties; developed min–max RETDMPC; control system synthesis; system state; unknown uncertainties; linear systems; sensor networks; minimax techniques; exploiting saving system resources; event-triggered control; continuous-time nonlinear systems; robust event-triggered; nonlinear control systems; robust control; networked control systems; min–max model predictive control; predictive control; multiagent systems
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2020.0518

Due to the features of event-triggered control in exploiting and saving system resources, they have been widely applied in sensor networks, multi-agent systems, networked control systems and so on. In this study, the authors focused on robust event-triggered distributed model predictive control (RETDMPC). Subject to disturbances and parametric uncertainties, they first applied the min–max model to RETDMPC. The min–max RETDMPC methodology is used to guarantee the robustness of the system state by taking the worst possible case of unknown uncertainties into consideration. Furthermore, in this framework, a new cost function is developed in which unknown uncertainties are considered. Next, sufficient conditions are provided to ensure the feasibility and stability of their developed min–max RETDMPC. Finally, a practical example is given to illustrate the advantages of their algorithm by comparing to the conventional model predictive control.