Set-Point Regulation of Linear Continuous-Time Systems using Neuromorphic Vision Sensors
This work addresses the challenge of integrating neuromorphic sensors into control systems for robotics, offering a solution for low-latency, event-driven regulation.
The paper tackles the problem of regulating a continuous-time linear system to a desired set-point using neuromorphic vision sensors, which output asynchronous event streams instead of periodic frames. It presents an H∞ controller that achieves this regulation and identifies suitable sensor configurations, demonstrating effectiveness on an unstable system.
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 regulating a continuous-time linear time invariant (LTI) system to a desired point using measurements from a neuromorphic sensor. We present an $H_\infty$ controller that regulates the LTI system to a desired set-point and provide the set of neuromorphic sensor based cameras for the given system that fulfill the regulation task. The effectiveness of our approach is illustrated on an unstable system.