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A continuous distributed control algorithm for time‐varying networks of nonlinear agents with input saturation
Author(s) -
Wang Qingling,
Sun Changyin
Publication year - 2021
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.5494
Subject(s) - control theory (sociology) , nonlinear system , bounded function , saturation (graph theory) , artificial neural network , computer science , function (biology) , multi agent system , mathematics , algorithm , control (management) , artificial intelligence , physics , mathematical analysis , quantum mechanics , combinatorics , evolutionary biology , biology
In this article, we explore the possibility of designing a continuous control algorithm for time‐varying networks of high‐order nonlinear agents with input saturation. A class of neural network‐based (NN) distributed control algorithms are proposed for the consensus of nonlinear agents with input saturation and nonidentical unknown control directions (UCDs) under uniformly quasi‐strongly δ ‐connected graphs. An NN is used to cancel the unknown part of agent dynamics due to the well known NN function approximation property. Also, an adaptive smooth term is employed to compensate for the bounded disturbance, the approximation error. Moreover, an auxiliary system is designed to counteract the effect of input saturation. Furthermore, the control law is augmented with the Nussbaum‐type function to obtain an estimation of the UCDs. It is proven rigorously that the consensus of nonlinear agents with input saturation and nonidentical UCDs can be guaranteed, and the boundedness of all signals of closed‐loop systems is ensured. Finally, simulation examples are given to show the effectiveness of our theoretical results.