Optimal On-Line Sampling Period Assignment

In embedded systems, the computing resources are often scarce and several control tasks may have to share the same computer. In this paper, 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. The approach is evaluated in extensive experiments, where three double-integrator electronic circuits are controlled by three concurrent tasks executing in a small real-time kernel. A fourth task, acting as feedback scheduler, periodically assigns new sampling periods, considering the current plant states and noise characteristics. The experiments show that on-line sampling period assignment can deliver significantly better control performance than the state-of-the-art, static period assignment.