Regulation of Linear Systems using Event-Based Detection Sensors

Prince Singh, Sze Yong, Emilio Frazzoli

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


In this paper, we investigate the problem of <formula><tex>$regulating$</tex></formula> a continuous-time linear time invariant (LTI) system to a desired point using <formula><tex>$discrete event$</tex></formula> 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 so a new class of algorithms needs to be developed that can efficiently process the sensors&#x0027; <formula><tex>$asynchronous$</tex></formula> discrete events for control tasks. Thus, we present a novel control design procedure that <formula><tex>$regulates$</tex></formula> 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.

Original languageEnglish (US)
JournalIEEE Transactions on Automatic Control
StateAccepted/In press - Jan 1 2018


  • Linear systems
  • Process control
  • Sensor systems
  • Task analysis
  • Vision sensors
  • Voltage control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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