General Dynamic Neural Networks for the Adaptive Tuning of an Omni-Directional Drive System for Reactive Swarm Robotics

Abstract

We demonstrate the use of general dynamic neural networks (GDNNs) for the online tuning of an omni-directional drive system for reactive swarm robots. The drive system used in this work consists of four motor-encoder-microcontroller modules each constituting a single-input single-output (SISO) proportional, integral, and differential (PID) control system. For a given target velocity, a neural network generates the parameters for each PID control system. In this paper, we evaluate and compare two different network structures for generating the PID parameters for the control systems using a hardware platform that we also presented in this paper. We analyze the performance of the system with respect to ISO performance indicators, our results show that both network structures are able to learn and tune the parameters for each PID control system to increase the accuracy of the drive system in comparison to fixed untuned PID parameters that are close to the output of a randomly initialized network.