Abstract
This paper presents a quaternion neural network-based controller for a robot manipulator that can be used to investigate the possibility of using quaternion neural networks in practical applications. The quaternion neural network, which synthesises the control input for tracking an end-effector of the robot manipulator to the desired trajectory, assumes the role of an adaptive-type servo controller in a control system. Two types of network, such as feed-forward quaternion neural network and a recurrent quaternion neural network, were used to design servo-level controller and their performances were compared. Numerical simulations for controlling a three-link robot manipulator are performed to evaluate the characteristics of the proposed controllers and to demonstrate the feasibility as well as the effectiveness of the proposed controllers.
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.
