Neuromorphic Control using Input-Weighted Threshold Adaptation

Stroobants, S.; de Wagter, C.; de Croon, G.C.H.E.

doi:10.1145/3589737.3605963

Neuromorphic Control using Input-Weighted Threshold Adaptation

Title

Neuromorphic Control using Input-Weighted Threshold Adaptation

Author

Stroobants, S. (TU Delft Control & Simulation)
de Wagter, C. (TU Delft Control & Simulation)
de Croon, G.C.H.E. (TU Delft Control & Simulation)

Date

2023

Abstract

Neuromorphic processing promises high energy efficiency and rapid response rates, making it an ideal candidate for achieving autonomous flight of resource-constrained robots. It can be especially beneficial for complex neural networks as are used for high-level visual perception. However, fully neuromorphic solutions also need to tackle low-level control tasks. Remarkably, it is currently still challenging to replicate even basic low-level controllers such as proportional-integral-derivative (PID) controllers. Specifically, it is difficult to incorporate the integral and derivative parts. To address this problem, we propose a neuromorphic controller that incorporates proportional, integral, and derivative pathways during learning. Our approach includes a novel input threshold adaptation mechanism for the integral pathway. This Input-Weighted Threshold Adaptation (IWTA) introduces an additional weight per synaptic connection, which is used to adapt the threshold of the post-synaptic neuron. We tackle the derivative term by employing neurons with different time constants. We first analyze the performance and limits of the proposed mechanisms and then put our controller to the test by implementing it on a microcontroller connected to the open-source tiny Crazyflie quadrotor, replacing the innermost rate controller. We demonstrate the stability of our bio-inspired algorithm with flights in the presence of disturbances. The current work represents a substantial step towards controlling highly dynamic systems with neuromorphic algorithms, thus advancing neuromorphic processing and robotics. In addition, integration is an important part of any temporal task, so the proposed Input-Weighted Threshold Adaptation (IWTA) mechanism may have implications well beyond control tasks.

Subject

micro-air-vehicles (MAVs)
neuromorphic control
rate coding
spiking neural networks (SNNs)
threshold adaptation

To reference this document use:

http://resolver.tudelft.nl/uuid:ac4eb69e-8c82-4ac8-af6e-7a02083f6a1c

DOI

https://doi.org/10.1145/3589737.3605963

Publisher

Association for Computing Machinery (ACM)

ISBN

9798400701757

Source

ICONS 2023 - Proceedings of International Conference on Neuromorphic Systems 2023