# Design and implementation of a parsimonious neuromorphic PID for onboard altitude control for MAVs using neuromorphic processors

arXiv: https://arxiv.org/abs/2109.10199 · HTML: https://mlsanity.com/paper/2109.10199/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Event-driven Vision and Control for UAVs on a Neuromorphic Chip](https://mlsanity.com/paper/2108.03694/) (2021)
- [Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing](https://mlsanity.com/paper/1810.06224/) (2018)
- [Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor](https://mlsanity.com/paper/2011.00534/) (2020)

## Built on by (most cited first, 3 of 3)

- [Fully neuromorphic vision and control for autonomous drone flight](https://mlsanity.com/paper/2303.08778/) (2023)
- [Neuromorphic Attitude Estimation and Control](https://mlsanity.com/paper/2411.13945/) (2024)
- [Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems](https://mlsanity.com/paper/2504.00957/) (2025)

## Abstract (arXiv)

The great promises of neuromorphic sensing and processing for robotics have led researchers and engineers to investigate novel models for robust and reliable control of autonomous robots (navigation, obstacle detection and avoidance, etc.), especially for quadrotors in challenging contexts such as drone racing and aggressive maneuvers. Using spiking neural networks, these models can be run on neuromorphic hardware to benefit from outstanding update rates and high energy efficiency. Yet, low-level controllers are often neglected and remain outside of the neuromorphic loop. Designing low-level neuromorphic controllers is crucial to remove the standard PID, and therefore benefit from all the advantages of closing the neuromorphic loop. In this paper, we propose a parsimonious and adjustable neuromorphic PID controller, endowed with a minimal number of 93 neurons sparsely connected to achieve autonomous, onboard altitude control of a quadrotor equipped with Intel's Loihi neuromorphic chip. We successfully demonstrate the robustness of our proposed network in a set of experiments where the quadrotor is requested to reach a target altitude from take-off. Our results confirm the suitability of such low-level neuromorphic controllers, ultimately with a very high update frequency.