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

CHAVEZ ARANA, DIEGO; GARCIA A., OMAR A.; RUBIO SCOLA, IGNÁCIO EDUARDO JESUS; ESPINOZA, EDUARDO S.; GARCIA CARRILLO, LUIS RODOLFO; SORNBORGER, ANDREW T.

Toronto

Conferencia; 2024 American Control Conference (ACC); 2024

IEEE

We present a neuromorphic computing control architecture for the problem of stabilizing a benchmark subactuated system: the ball and plate platform. The proposed architecture makes use of Spiking Neural Networks (SNNs) as an alternative to traditional von Neumann computing. TheNeural 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.