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 controlhave 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
