Abstract
A direct nonlinear adaptive state regulator, for unknown dynamical systems that are modeled by recurrent neural networks is discussed. In an ideal case of complete model matching, the convergence of the state to zero plus boundedness of all signals in the closed loop is ensured. Moreover, the behavior of the closed loop system is analyzed for cases in which the true plant differs from the recurrent neural network model in the sense that it is of higher older, that was originally assumed. Modifications of the original control and update laws are provided, so that at least uniform ultimate boundedness is guaranteed.
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.
