Sparse event-triggered control of linear systems

In event-triggered control, a situation where the control input must be sparse often arises. Therefore, in this study, we propose sparse event-triggered control, meaning that the control input is sparse and updated in an event-triggered manner. First, we present a model-based method for sparse event-triggered control of linear systems, where the event condition is defined by a Lyapunov function. The resulting control input is proven to be sparse and the control system is confirmed to be asymptotically stable. Second, we extend it to a data-driven version, where the event condition is adaptively updated from online data on the state trajectory. Finally, we discuss the possibility of extending our framework to two cases of disturbance and nonlinear dynamics.