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Neuroadaptive containment control of nonlinear multiagent systems with input saturations
Author(s) -
Zhao Lin,
Yu Jinpeng,
Yu Haisheng,
Lin Chong
Publication year - 2019
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.4520
Subject(s) - differentiator , backstepping , control theory (sociology) , nonlinear system , computer science , containment (computer programming) , bounded function , tracking error , compensation (psychology) , scheme (mathematics) , adaptive control , control (management) , control engineering , engineering , mathematics , filter (signal processing) , artificial intelligence , psychology , mathematical analysis , physics , quantum mechanics , psychoanalysis , computer vision , programming language
Summary This paper investigates the output containment tracking problem of nonlinear multiagent systems with mismatched uncertain dynamics and input saturations. A neural network–based distributed adaptive command filtered backstepping (CFB) scheme is given, which can guarantee that the containment tracking errors reach to the desired neighborhood of origin and all signals in the closed‐loop system are bounded. Note that error compensation system and virtual control laws established in CFB only use local information, so the given scheme is completely distributed. Moreover, the applied sliding mode differentiator (SMD) can make the outputs of SMD fast approximate the virtual signal and its derivative at each step of backstepping, which can further improve the control quality. Finally, a simulation example is given to show the effectiveness of the proposed scheme.