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 t...Show MoreMetadata
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.
Published in: 2022 34th Chinese Control and Decision Conference (CCDC)
Date of Conference: 15-17 August 2022
Date Added to IEEE Xplore: 14 February 2023
ISBN Information: