Abstract
The paper presents results developed to control sampled data nonlinear systems. It is shown that there are fundamental problems when attempting to use the standard feedback linearization procedure for sampled data systems. The solution needs some structural analysis and the introduction of state dependent parametrization. In case of unknown nonlinearities approximate models can be used in the form of spline surfaces, fuzzy logic or neural nets. Any of these approximation techniques are applicable to expand the control algorithm for adaptive systems.
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.
