Regulation of Linear Systems Using Event-Based Detection Sensors

In this paper, we investigate the problem of regulating a continuous-time linear time-invariant (LTI) system to a desired point using discrete event measurements from signal change detection sensors with a logarithmic event generation threshold (trigger), which includes the recently developed neuromorphic vision sensors. Existing control algorithms typically only process periodic measurements, and thus, a new class of algorithms needs to be developed that can efficiently process the sensors' asynchronous discrete events for control tasks. Thus, we present a novel control design procedure that regulates the hybrid system, consisting of the continuous LTI system and a discrete-event signal change observation model, to a desired set point. Moreover, we provide the set of thresholds (sufficient conditions) for the given system to fulfill the regulation task. The effectiveness of our approach is illustrated on an unstable system.