Abstract
The theoretical study on synthesis of hierarchical neural controllers for nonlinear systems affine in control is presented. We first show that performance criteria based optimal neural controllers can be synthesized to approximately identify the switching manifold for control. We then show that the hierarchical neural controller can deal with system uncertainties in parameters which are fixed but unknown, and should perform reasonably well in theory. Further, the adaptive hierarchical neural controllers are developed to deal with systems uncertainties in parameters which are time varying, and it is shown that they are able to perform satisfactorily.
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.
