Optimal-Sampling-inspired Self-Triggered Control

Self-Triggered control is an appealing sampling strategy that promises to preserve the same control performance as traditional (periodic) sampling, yet consuming less computing resource by executing the controller less frequently. While the stability of self-Triggered controllers is often addressed, their capacity to actually reduce the cost is more difficult to be evaluated. Inspired by a recent result on the optimal density of the sampling instants that minimizes the control cost, in this paper we propose a new self-Triggered control strategy. The proposed sampling rule is extremely simple and effective. Significant cost reductions compared to the optimal periodic controller are shown even with larger minimum intersample separation. Thanks to the simplicity of the sampling rule, we also implemented the proposed self-Triggered controller over a physical plant. The experimental results are aligned with the theoretical ones.