Co-design of sampling pattern and control in self-triggered model predictive control for sampled-data systems

This paper studies the event-triggered model predictive control (MPC) problem for networked control systems with input constraints, where the control is of the sampled-data form. A novel self-triggered MPC (STMPC) method which enables the optimal design of sampling pattern and control law is proposed to reduce the conservatism of separate design of trigger and control law in existing approaches. The conditions on ensuring the algorithm feasibility and the closed-loop system stability are developed. In addition, an upper bound of the closed-loop system performance is derived which provides performance guarantee for the designed STMPC. Finally, simulation results are presented to verify the effectiveness of the proposed STMPC method.