Neuromorphic processing promises high energy efficiency and rapid response rates, making it an ideal candidate for achieving autonomous flight of resource-constrained robots. It will be especially beneficial for complex neural networks as are involved in high-level visual perception. However, fully neuromorphic solutions will also need to tackle low-level control tasks. Remarkably, it is currently still challenging to replicate even basic low-level controllers such as proportional-integral-derivative (PID) controllers. Specifically, it is difficult to incorporate the integral and derivative parts. To address this problem, we propose a neuromorphic controller that incorporates proportional, integral, and derivative pathways during learning. Our approach includes a novel input threshold adaptation mechanism for the integral pathway. This Input-Weighted Threshold Adaptation (IWTA) introduces an additional weight per synaptic connection, which is used to adapt the threshold of the post-synaptic neuron. We tackle the derivative term by employing neurons with different time constants. We first analyze the performance and limits of the proposed mechanisms and then put our controller to the test by implementing it on a microcontroller connected to the open-source tiny Crazyflie quadrotor, replacing the innermost rate controller. We demonstrate the stability of our bio-inspired algorithm with flights in the presence of disturbances. The current work represents a substantial step towards controlling highly dynamic systems with neuromorphic algorithms, thus advancing neuromorphic processing and robotics. In addition, integration is an important part of any temporal task, so the proposed Input-Weighted Threshold Adaptation (IWTA) mechanism may have implications well beyond control tasks.

翻译：神经形态处理具有高能效和快速响应的优势，使其成为资源受限机器人自主飞行的理想候选方案，尤其适用于涉及高级视觉感知的复杂神经网络。然而，全神经形态解决方案仍需应对低级控制任务。值得注意的是，当前复制比例积分微分（PID）控制器等基础低级控制器仍面临挑战，特别是积分项和微分项的整合困难。为解决该问题，我们提出一种在学习过程中整合比例、积分和微分路径的神经形态控制器。该方法为积分路径引入了一种新型输入阈值自适应机制：输入加权阈值自适应（IWTA）通过为每个突触连接增加额外权重，用于调整突触后神经元的阈值。对于微分项，我们采用具有不同时间常数的神经元进行处理。本文首先分析了所提出机制的性能与局限性，随后在连接至开源微型Crazyflie四旋翼飞行器的微控制器上实现该控制器，替换其最内层速率控制器进行测试。实验证明，在存在干扰的飞行环境中，该仿生算法具有稳定性。本研究是迈向利用神经形态算法控制高动态系统的重要一步，推动了神经形态处理与机器人技术的发展。此外，由于积分是任何时序任务的关键组成部分，所提出的输入加权阈值自适应（IWTA）机制或将对控制领域之外的广泛任务产生深远影响。