A Neuro-Inspired Spike-Based PID Motor Controller for Multi-Motor Robots with Low Cost FPGAs

Abstract

In this paper we present a neuro-inspired spike-based close-loop controller written in VHDL and implemented for FPGAs. This controller has been focused on controlling a DC motor speed, but only using spikes for information representation, processing and DC motor driving. It could be applied to other motors with proper driver adaptation. This controller architecture represents one of the latest layers in a Spiking Neural Network (SNN), which implements a bridge between robotics actuators and spike-based processing layers and sensors. The presented control system fuses actuation and sensors information as spikes streams, processing these spikes in hard real-time, implementing a massively parallel information processing system, through specialized spike-based circuits. This spike-based close-loop controller has been implemented into an AER platform, designed in our labs, that allows direct control of DC motors: the AER-Robot. Experimental results evidence the viability of the implementation of spike-based controllers, and hardware synthesis denotes low hardware requirements that allow replicating this controller in a high number of parallel controllers working together to allow a real-time robot control.

An AER handshake-less modular infrastructure PCB with x8 2.5Gbps LVDS serial links

“…Under this project we also developed an improved dynamic vision sensor [3], an AER convolution chip [20], a spike based filter for object detection and tracking [21], a scalable mesh network multi-PCB technique of spike convolutions for cortex operation emulation [9], and spike based motor controllers (proportional-integral-derivative [15] and neuro-inspired open-loop VITE [16]). The AER-Node board was designed to allow multi-PCB communication with conventional parallel-handshaked-AER chips (retinas and convolutions) or robots with the adequate motor interfaces.…”

Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE

“…The equivalence between the constants of the blocks in the block diagram and the parameters for the algorithm implementation is [21]: ${\omega }_{\mathit{\text{cut}}-\mathit{\text{off}}}=\frac{f_{\mathit{\text{CLK}}}}{2^{N_i-1}\left(1+IG\_FD\right)}$ $k=\frac{f_{\mathit{\text{CLK}}}}{2^{N_k-1}\left(1+IG\_FD\right)}$…”

“…Nevertheless, we need to adapt the spikes because the digital clock of the boards is fixed at 50 MHz resulting in a spike width of 20 ns and this signal is very fast and the spikes too short for the motors model of the robotic platform [21]. …”

Case study: Bio-inspired self-adaptive strategy for spike-based PID controller

“…Various approaches have been proposed in this field, e.g. neuromorphic chip [1], bio-inspired systems of visual object recognition and tracking [2], and neuro-inspired controller [3]. Bio-inspired computing uses a set of layers to process complex input data and produces predicted results.…”

“…In our previous work [3], a spike-based Proportional-Integrative-Derivative (PID) robot controller was proposed which employed the last layer of a spiking neural network (SNN) to control a motor. The controller converts motor sensor signals into spikes and feedbacks to the SNN to aid in making speed-control decisions.…”