Dynamic Scheduling and Control-Quality Optimization of Self-Triggered Control Applications

Time-triggered periodic control implementations are over provisioned for many execution scenarios in which the states of the controlled plants are close to equilibrium. To address this inefficient use of computation resources, researchers have proposed self-triggered control approaches in which the control task computes its execution deadline at runtime based on the state and dynamical properties of the controlled plant. The potential advantages of this control approach cannot, however, be achieved without adequate online resource-management policies. This paper addresses scheduling of multiple self-triggered control tasks that execute on a uniprocessor platform, where the optimization objective is to find trade-offs between the control performance and CPU usage of all control tasks. Our experimental results show that efficiency in terms of control performance and reduced CPU usage can be achieved with the heuristic proposed in this paper.