Abstract
A novel model-free adaptive control method is proposed for a class of discrete-time single input single output (SISO) nonlinear systems, where the equivalent dynamic linearization technique is used on the ideal nonlinear controller. With radial basis function neural network, the controller parameters are tuned on-line directly using the measured input and output data of the plant, when the plant model is unavailable. The stability of the proposed method is guaranteed by rigorous theoretical analysis, and the effectiveness and applicability are verified by numerical simulation and further demonstrated by the experiment on three tanks water level control process.
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 callMore From: IEEE Transactions on Neural Networks and Learning Systems
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.
