Abstract

A nonlinear model predictive controller based on iterative learning control (NMPILC) is proposed. The nonlinear plant dynamic is described by a fuzzy model which contains local liner models. Based on this model, model predictive control algorithm that utilizes past data along with real-time measurements is devised. This algorithm is developed to address the learning rate for a class of repetitive system with non-repetitive disturbances. The iterative learning control law is given. It is shown that the control performance of the proposed NMPILC can be greatly improved by using this on-linear model predictive iterative learning control algorithm.

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.