  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.
