The Self Triggered Task Model for Real-Time Control Systems

In this paper we present a control-based model for control tasks that allows each control task to trigger itself optimizing computing resources and control performance. Using this model, at each control task instance execution, the executing instance informs the scheduler when the next instance should be executed. The next instance execution point in time is dynamically obtained as a function of the utilization factor and control performance. Preliminary results show that control activities, at run time, are able to define self-execution patterns that dynamically balance optimal levels of control performance and resource