Cruz, Anel Olivares (Polytechnic Metropolitan University of Hidalgo), Espinoza Quesada, Eduardo Steed (Center for Research and Advanced Studies of the National Polytec), Ramos Velasco, Luis Enrique (Universidad Autónoma del Estado de Hidalgo), Garcia, Omar Alejandro (New Mexico State University), Garcia Carrillo, Luis Rodolfo (New Mexico State University), Rubio Scola, Ignacio (CIFASIS-CONICET and National University of Rosario (UNR))

Biologically-Inspired Self-Tuning Neuromorphic PID Control for Real-Time Trajectory Tracking of a UAS

Scheduled for presentation during the Regular Session "Aerial and Space Robots" (WeBT4), Wednesday, July 16, 2025, 14:20−14:40, Room 108

Joint 10th IFAC Symposium on Mechatronic Systems and 14th Symposium on Robotics, July 15-18, 2025, Paris, France

This information is tentative and subject to change. Compiled on July 16, 2025

Abstract

This study presents a control framework for Unmanned Aerial Systems (UASs) that leverages the features of Spiking Neural Networks (SNNs). Towards this goal, we developed two neuromorphic controllers using Nengo – a prototyping environment for the deployment of biologically-plausible Neural Networks (NN). Initially, we implemented a neuromorphic Proportional-Integral-Derivative controller, which was subsequently enhanced with self-tuning capabilities. The proposed self-tuning controller is capable of dynamically adjusting its gains using a gradient descent optimization algorithm. The proposed controllers were assessed through a series of real-time experimental tests, where a DJI Tello UAS was tasked with performing trajectory tracking tasks.