Set-Point Regulation of Linear Continuous-Time Systems using Neuromorphic Vision Sensors

TL;DR

This paper introduces an $H_ abla$ controller leveraging neuromorphic vision sensors for precise set-point regulation of linear continuous-time systems, demonstrating effectiveness on unstable systems with asynchronous event-based data processing.

Contribution

It develops a novel control approach using neuromorphic sensors and $H_ abla$ control design for linear systems, addressing the challenge of event-based measurement processing.

Findings

Successful regulation of an unstable system using neuromorphic sensors.

Identification of suitable neuromorphic cameras for control tasks.

Validation of the approach through simulation results.

Abstract

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.