Abstract
In this paper, an integral barrier Lyapunov function combined with the RBF neural network are introduced for a class of MIMO nonlinear systems with unknown nonlinearities. Under the premise of the system output and its first derivative of the time being measurable, by applying the integral barrier Lyapunov function, we have designed a controller based on the adaptive RBF neural network which uses a disturbance observer to compensate the error. The system is proved to be semi-globally uniformly ultimately bounded and the tracking error is convergent and bounded. Finally, we have made a simulation on a 2-DOF robotic manipulator systems to examine the effectiveness of the proposed design.
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.
