Optimal Online Sampling Period Assignment: Theory and Experiments

In embedded systems, the computing resources are often scarce and several control tasks may have to share the same computer. In this brief, we assume that a set of feedback controllers should be implemented on a single-CPU platform. We study the problem of optimal sampling period assignment, where the goal is to assign sampling rates to the controllers so that the overall control performance is maximized. We derive expressions relating the expected cost over a finite horizon to the sampling period, the computational delay, and the amount of noise acting on the plant. Based on this, we develop a feedback scheduler that periodically assigns new sampling periods based on estimates of the current plant states and noise intensities. Extensive experiments show that online sampling period assignment can deliver significantly better control performance than the state-of-the-art, static period assignment.