Control of Single-Segment Continuum Robots: Reinforcement Learning vs. Neural Network based PID

Abstract

Continuum robots have been very popular in the recent days due to their wide spread applications in space, defence, medical, underwater, industries etc. Modelling of these types of robots is difficult due to their highly nonlinear dynamic characteristic which necessitates the need for model-less intelligent control. In this paper two intelligent model-less adaptive methods,Reinforcement Learning (RL) and Artificial Neural Network based proportional integral derivative ANN-PID control have been applied on a hardware continuum robot. Here the RL technique involves a continuous state discrete action Q learning method and the ANN-PID is implemented by a single neuron. Performance of both the methods are compared by implementing them on a hardware robot.