Self-triggered state feedback control of linear plants under bounded disturbances

This paper addresses the problem of self-triggered state feedback control for linear plants under bounded disturbances. In a self-triggered scenario, the controller is allowed to choose when the next sampling time should occur and does so based on the current sampled state and on a priori knowledge about the plant. The main contribution of this paper is the definition of a triggering strategy that allows for the consideration of a class of controllers that is much larger than that of static controllers with a zero-order hold of the last state measurement. This is done by resorting to a model-based control architecture whereby a model of the plant is used to simulate the time evolution of the plant´s state in between sampling times when no information about the plant´s actual state is available. An illustrative example with simulation results shows how the new triggering strategy along with a model-based controller design yield a clear gain in performance when compared to other strategies proposed so far.