Abstract
We study the problem of output tracking for uncertain strict-feedback systems with output and derivative information. The unknown nonlinear terms in the system description are not linearly parametrized, but it is assumed that the optimal parameters that characterize a neural net based approximation to the nonlinearities lie within a known compact set. The proposed controllers, with output and derivative information, guarantee the boundedness of all signals in the closed-loop system, and, at the expense of increased control effort, the output tracking error can be driven to an arbitrarily small neighborhood of the origin arbitrarily fast. Also, the closed loop signals satisfy a relevant disturbance attenuation inequality, which, under certain conditions, implies asymptotic tracking.
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.
