Self-tuning neuromorphic controller for real-time UAS trajectory tracking based on prescribed error sensitivity

Abstract Inspired by a learning mechanism encountered in the mammal brain, this study presents a Neuromorphic Self-Tuning Proportional-Integral-Derivative (PID) controller for Unmanned Aerial Systems (UAS). The controller is derived from a Spiking Neuronal Network (SNN) and enhanced with a biologically plausible supervised learning rule known as Prescribed Error Sensitivity (PES). This control framework enables the UAS to maintain stability near singular regions, improving robustness to input perturbations, and reducing trajectory tracking error. A series of experimental tests consisting of real-time UAS trajectory tracking demonstrate the applicability and effectiveness of the proposed approach.