Towards neuromorphic control: A spiking neural network based PID controller for UAV

In this work, we present a spiking neural network(SNN) based PID controller on a neuromorphic chip. On-chipSNNs are currently being explored in low-power AI applications.Due to potentially ultra-low power consumption, low latency,and high processing speed, on-chip SNNs are a promising toolfor control of power-constrained platforms, such as UnmannedAerial Vehicles (UAV). To obtain highly efficient and fast end-to-end neuromorphic controllers, the SNN-based AI architecturesmust be seamlessly integrated with motor control. Towards thisgoal, we present here the first implementation of a fully neu-romorphic PID controller. We interfaced Intel’s neuromorphicresearch chip Loihi to a UAV, constrained to a single degreeof freedom. We developed an SNN control architecture usingpopulations of spiking neurons, in which each spike carriesinformation about the measured, control, or error value, definedby the identity of the spiking neuron. Using this sparse code,we realize a precise PID controller. The P, I, and D gains of thecontroller are implemented as synaptic weights that can adaptaccording to an on-chip plasticity rule. In future work, theseplastic synapses can be used to tune and adapt the controllerautonomously