Self-triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection

摘要

In this paper, we propose a self-triggered formulation of Model PredictiveControl for continuous-time nonlinear input-affine networked control systems.Our control method specifies not only when to execute control tasks but alsoprovides a way to discretize the optimal control trajectory into severalcontrol samples, so that the reduction of communication load will be obtained.Stability analysis under the sample-and-hold implementation is also given,which guarantees that the state converges to a terminal region where the systemcan be stabilized by a local state feedback controller. Some simulationexamples validate our proposed framework.