Optimal On-line Sampling Period Assignment for Real-Time Control Tasks Based on Plant State Information

The paper presents a feedback scheduling strategy for multiple control tasks that uses feedback from the plant states to distribute the computing resources optimally among the tasks. Linear-quadratic controllers are analyzed, and expressions relating the expected cost to the sampling period and the plant state are derived and used for on-line sample-rate adjustments. In the case of minimum-variance control of multiple integrator processes, an exact expression for the optimal sampling periods is obtained. For the general case, an on-line optimization procedure is developed. The approach is exemplified on a set of controllers for first-order systems. The issues of computational delay and the choice of the feedback scheduler period are also discussed.