Abstract
This paper presents an online neural identification and control scheme in continuous-time for trajectory tracking of a robotic arm evolving in the vertical plane. A recurrent high-order neural network (RHONN) structure in a block strict-feedback form is proposed to identify online in a series-parallel configuration, using the filtered error learning law, the dynamics of the plant. Based on the RHONN identifier structure, a stabilizing controller is derived via integrator backstepping procedure. The performance of the neural control scheme proposed is tested on a two degrees of freedom robotic arm, of our own design and unknown parameters, powered by industrial servomotors.
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.
