Abstract
AbstractIn this paper, for the nonlinear influencing factors such as uncertainty and external disturbance existing in the actual driving process of the car‐like mobile robot (CLMR), a data‐driven generalized predictive control (GPC) method based on interval type‐2 T‐S fuzzy neural network (IT2TSFNN) is proposed for the trajectory tracking of CLMR. The controlled auto‐regressive integrated moving average (CARIMA) model of the mobile robot is established by analyzing data samples and using IT2TSFNN. Then, a generalized predictive controller is designed for the CARIMA model. Also, the global convergence of IT2TSFNN is verified by the Stone‐Weirstrass theorem. Unlike most previous results, the proposed method does not rely on the mathematical model of the mobile robot but only on the historical data of its operation. Finally, the simulation results show that the proposed method can avoid repeatedly debugging parameters, deal with the influence of uncertain factors, and obtain higher tracking accuracy.
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.
