Real-Time Implementation of a Spiking Neural Network-Based Control: An Application for the Ball and Plate System

  • Diego Chavez Arana
  • , Omar A. Garcia A
  • , Ignacio Rubio Scola
  • , Eduardo S. Espinoza
  • , Luis Rodolfo Garcia Carrillo
  • , Andrew Sornborger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a neuromorphic computing control architecture for the problem of stabilizing a benchmark sub-actuated system: the ball and plate platform. The proposed architecture makes use of Spiking Neural Networks (SNNs) as an alternative to traditional von Neumann computing. The Neural Engineering Framework (NEF) is adopted to encode the SNN-based controller to accomplish position and trajectory tasks. Simulation results and a real-time implementation of the proposed SNN controller are presented over a homemade ball and plate prototype. The effectiveness of the proposed neuromorphic controller is demonstrated, even in situations where the system is affected by external impulsive disturbances.

Original languageEnglish
Title of host publication2024 American Control Conference, ACC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3100-3105
Number of pages6
ISBN (Electronic)9798350382655
DOIs
StatePublished - 2024
Externally publishedYes
Event2024 American Control Conference, ACC 2024 - Toronto, Canada
Duration: 10 Jul 202412 Jul 2024

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Conference

Conference2024 American Control Conference, ACC 2024
Country/TerritoryCanada
CityToronto
Period10/07/2412/07/24

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