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Fast terminal sliding‐mode finite‐time tracking control with differential evolution optimization algorithm using integral chain differentiator in uncertain nonlinear systems
Author(s) -
Ma Ruizi,
Zhang Guoshan,
Krause Olav
Publication year - 2017
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3890
Subject(s) - differentiator , control theory (sociology) , robustness (evolution) , terminal sliding mode , nonlinear system , computer science , trajectory , sliding mode control , tracking (education) , parametric statistics , integral sliding mode , robust control , controller (irrigation) , mathematics , artificial intelligence , control (management) , bandwidth (computing) , pedagogy , computer network , chemistry , biology , psychology , biochemistry , quantum mechanics , agronomy , statistics , physics , astronomy , gene
Summary This paper presents a fast terminal sliding‐mode tracking control for a class of uncertain nonlinear systems with unknown parameters and system states combined with time‐varying disturbances. Fast terminal sliding‐mode finite‐time tracking systems based on differential evolution algorithms incorporate an integral chain differentiator (ICD) to feedback systems for the estimation of the unknown system states. The differential evolution optimization algorithm using ICD is also applied to a tracking controller, which provides unknown parametric estimation in the limitation of unknown system states for trajectory tracking. The ICD in the tracking systems strengthens the tracking controller robustness for the disturbances by filtering noises. As a powerful finite‐time control effort, the fast terminal sliding‐mode tracking control guarantees that all tracking errors rapidly converge to the origin. The effectiveness of the proposed approach is verified via simulations, and the results exhibit high‐precision output tracking performance in uncertain nonlinear systems.