Distributed Adaptive Learning Tracking for Nonlinear Time-varying Multi-agent Systems With Unknown Control Directions and Dead-zone Inputs

This paper investigates the distributed tracking problem for second-order nonlinear time-varying multi-agent systems (SONTV-MASs) subject to unknown control directions (UCDs) and input dead-zone using adaptive iterative learning control (AILC). Through parameter recombination and mean value theorem,...

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Vydané v:International journal of control, automation, and systems Ročník 23; číslo 9; s. 2514 - 2528
Hlavní autori: Yang, Nana, Li, Suoping, Chen, Jinshu, Zhou, Yongqiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.09.2025
Springer Nature B.V
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Abstract This paper investigates the distributed tracking problem for second-order nonlinear time-varying multi-agent systems (SONTV-MASs) subject to unknown control directions (UCDs) and input dead-zone using adaptive iterative learning control (AILC). Through parameter recombination and mean value theorem, the system is remodeled. Based on these, time-varying gains are designed to avoid the global information related topology graph and distinctive multi-Nussbaum functions are introduced. Moreover, by constructing a weighted composite energy function (WCEF), it is strictly analyzed and proved that all followers completely track the leader in a finite time interval. Numerical simulations conducted on a multi-single-link manipulator system validate the theoretical results and demonstrate the algorithm’s effectiveness.
AbstractList This paper investigates the distributed tracking problem for second-order nonlinear time-varying multi-agent systems (SONTV-MASs) subject to unknown control directions (UCDs) and input dead-zone using adaptive iterative learning control (AILC). Through parameter recombination and mean value theorem, the system is remodeled. Based on these, time-varying gains are designed to avoid the global information related topology graph and distinctive multi-Nussbaum functions are introduced. Moreover, by constructing a weighted composite energy function (WCEF), it is strictly analyzed and proved that all followers completely track the leader in a finite time interval. Numerical simulations conducted on a multi-single-link manipulator system validate the theoretical results and demonstrate the algorithm’s effectiveness.
This paper investigates the distributed tracking problem for second-order nonlinear time-varying multiagent systems (SONTV-MASs) subject to unknown control directions (UCDs) and input dead-zone using adaptive iterative learning control (AILC). Through parameter recombination and mean value theorem, the system is remodeled. Based on these, time-varying gains are designed to avoid the global information related topology graph and distinctive multi-Nussbaum functions are introduced. Moreover, by constructing a weighted composite energy function (WCEF), it is strictly analyzed and proved that all followers completely track the leader in a finite time interval. Numerical simulations conducted on a multi-single-link manipulator system validate the theoretical results and demonstrate the algorithm’s effectiveness. KCI Citation Count: 0
Author Zhou, Yongqiang
Li, Suoping
Yang, Nana
Chen, Jinshu
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multi-agent systems
unknown control directions
dead-zone inputs
distributed tracking
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Snippet This paper investigates the distributed tracking problem for second-order nonlinear time-varying multi-agent systems (SONTV-MASs) subject to unknown control...
This paper investigates the distributed tracking problem for second-order nonlinear time-varying multiagent systems (SONTV-MASs) subject to unknown control...
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SubjectTerms Control
Control algorithms
Control systems
Design
Differential calculus
Engineering
Learning
Mechatronics
Multiagent systems
Regular Papers
Robotics
Topology
Tracking problem
제어계측공학
Title Distributed Adaptive Learning Tracking for Nonlinear Time-varying Multi-agent Systems With Unknown Control Directions and Dead-zone Inputs
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Volume 23
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