Self-triggered model predictive control for networked control systems based on first-order hold

In this work, a new self-triggered model predictive control (STMPC) algorithm is proposed for continuous-time networked control systems. Compared with existing STMPC algorithms, the proposed STMPC is implemented based on linear interpolation (first-order hold) rather than the standard zero-order hold, which helps further reduce the difference between the self-triggered control signal and the original time-triggered counterpart and thus reduce the rate of triggering. Based on the first-order hold implementation, a self-triggering condition is derived and the corresponding theoretical properties of the closed-loop system are analyzed. Finally, the comparison between the proposed algorithm and the zero-order hold-based STMPC is carried out through both theoretical analysis and a simulation example to illustrate the effectiveness of the proposed method.