Abstract

Based on radial basis function neural networks (RBFNNs), adaptive dynamic surface control (DSC) is investigated for a class of uncertain strict-feedback nonlinear systems in this paper. By introducing first-order filter and combining DSC with backstepping, the operation of differentiation is replaced by simpler algebraic operation. Furthermore, the explosion of complexity in traditional backstepping design is avoided. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system, with arbitrary small tracking error by appropriately choosing design constants.

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 call

Disclaimer: 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.