Self-triggered nonlinear model predictive control for networked control systems

In this paper, we propose a self-triggered formulation of Model Predictive Control for continuous-time nonlinear networked control systems. Our control method derives not only when to execute control tasks but also provides the way to discretize the optimal control trajectory so as to alleviate the communication burden as much as possible. Stability analysis under the sample-and-hold implementation is also given in detail, which guarantees that the state converges to a terminal region where the local linear state feedback can stabilize the system. A simulation example verifies our proposed framework.