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.

翻译：生物神经系统通过稀疏、事件驱动的脉冲式信息处理与自适应调节，在不确定环境中实现了显著的鲁棒性和适应性。受这一范式启发，本文提出一种用脉冲时序编码替代传统连续时间信号表征的神经形态干扰观测器与控制框架。干扰估计与控制输入均通过积分-触发（IF）神经元动力学从离散脉冲事件中构建，实现本质上的事件驱动更新。受脉冲频率适应（SFA）启发设计的自适应阈值触发机制，使得脉冲生成具备历史依赖性调节能力。仿真结果表明，所提框架具备神经启发的鲁棒性与适应性，且自适应阈值脉冲方案在噪声环境下可将脉冲事件数降至固定阈值方案的42.6%。