Abstract
This paper provides an adaptive Super-Twisting sliding mode control (STSMC) scheme based on a multi-feedback feature selection fuzzy neural network (MFFSFNN), aiming at tracking compensation current quickly and precisely and solving the harmonic current problem in the electrical grid. Because of its designed feedback loops and feature selection layer, it has the characteristics of signal filtering and feedback. Signal filtering helps MFFSFNN deal with external disturbances and model uncertainties with the capability of choosing valuable signals. Signal feedback plays an important role in expanding the learning dimension with the associative memory ability, thus raising approximation accuracy of the target system. Simulation study and comprehensive comparisons are given to demonstrate the effectiveness and superiority of performance using the proposed controller.
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.
