Stabilization of linear continuous-time systems using neuromorphic vision sensors

Recently developed neuromorphic vision sensors have become promising candidates for agile and autonomous robotic applications primarily due to, in particular, their high temporal resolution and low latency. Each pixel of this sensor independently fires an asynchronous stream of 'retinal events' once a change in the light field is detected. Existing computer vision algorithms can only process periodic frames and so a new class of algorithms needs to be developed that can efficiently process these events for control tasks. In this paper, we investigate the problem of quadratically stabilizing a continuous-time linear time invariant (LTI) system using measurements from a neuromorphic sensor. We present an H_∞ controller that stabilizes a continuous-time LTI system and provide the set of stabilizing neuromorphic sensor based cameras for the given system. The effectiveness of our approach is illustrated on an unstable system.