Abstract
A robust self-organizing fuzzy-neural control (RSOFNC) system is proposed in this paper. The RSOFNC system is comprised of a self-structuring fuzzy neural network (SFNN) controller and a robust controller. The SFNN controller is the principal controller and the robust controller is designed to achieve L2 tracking performance. In the SFNN controller design, a SFNN with the asymmetric Gaussian membership functions is used to online approximate an ideal controller via the structure and parameter learning phases. The structure learning phase consists of the growing of membership functions and the pruning of fuzzy rules, and thus the SFNN can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. Finally, the proposed RSOFNC system is applied to control a second-order chaotic system. The simulation results show that the proposed RSOFNC system can achieve favorable tracking performance.
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.
