Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月21日

查看全文
  • Anel Olivares,
  • Eduardo S. Espinoza,
  • Luis E. Ramos,
  • Omar A. Garcia A.,
  • Luis Rodolfo Garcia Carrillo,
  • Andrew T. Sornborger

摘要

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.