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

Abstract

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.

Distributed Model Predictive Control for Linear Constrained Systems Based on Time-Varying Terminal Sets

“…However, the controllers related to the subsystem will not exchange information with each other to improve the system performance. In this case, distributed predictive control is proposed by some researchers [18], [19], which allows communication information sharing between subsystem controllers, so that the coupling between subsystems can be fully considered to make the system performance better. To the best of our knowledge, the online calculation load is still large.…”