Abstract

A novel technique, using neural networks, is proposed for the adaptive control of a class of nonlinear nonminimum phase systems. Using Taylor expansion, the nonlinear system can be regarded as a linear nonminimum phase system with a measurable disturbance. Pole-placement is used to stabilize the system, and a neural network is used to approximate the nonlinear term. Feedforward compensation is used to eliminate steady tracking errors which are caused by the nonlinear term.

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.