AI Insight
This paper presents a neuromorphic disturbance observer (NDO) that uses spike-based neural encoding instead of traditional continuous signals for control systems. The system mimics biological neurons by using integrate-and-fire dynamics and incorporates an adaptive threshold mechanism inspired by spike-frequency adaptation in real neurons. Simulations show the approach achieves robust control performance while reducing the number of spike events by over 57% compared to fixed-threshold methods in noisy conditions.
Why it matters
This bio-inspired approach could enable more energy-efficient robotic and autonomous systems by processing information only when events occur, rather than continuously. The reduced computational load and event-driven nature make it particularly suitable for resource-constrained applications like drones, prosthetics, and embedded control systems.
Understand the Science
arXiv:2606.05189v2 Announce Type: replace
Abstract: Biological neural systems achieve remarkable robustness and adaptability in uncertain environments through sparse, event-driven spike-based information processing and adaptive regulation. Inspired by this paradigm, this paper develops a neuromorhpic disturbance observer (NDO) and control framework that replaces conventional continuous-time signal representations with spike-timing encoding. Both disturbance estimates and control inputs are constructed via integrate-and-fire (IF) neuron dynamics from discrete spike events, yielding intrinsically event-driven updates. An adaptive-threshold triggering mechanism is inspired by spike-frequency adaptation (SFA), enabling history-dependent regulation of spike generation. Simulation results demonstrate that the proposed framework achieves neurally inspired robustness and adaptability, while the adaptive-threshold spiking scheme reduces spike events to 42.6% of the fixed-threshold case under noisy conditions.
Source: Bio-plausible Neuromorphic Disturbance Observer Based on Emulation Theory: Extended Version