NDLI: Adaptive estimation-based TILC for the finite-time consensus control of non-linear discrete-time MASs under directed graph

Content Provider	IET Digital Library
Author	Lv, Yunkai Chi, Ronghu Feng, Yuanjing
Abstract	This work explores the consensus problems under the directed graph, variable learning gains, fast convergence and data-driven control framework comprehensively and proposes an adaptive estimation-based terminal iterative learning control for a nonlinear discrete-time multi-agent system (MAS) with a constant control input. A linear iteration-incremental model is built by using an iterative dynamic linearisation where the unknown partial derivatives are estimated iteratively using I/O data. The learning control law is designed with both a constant learning gain and an iteration-time-varying learning gain. The constant one can be selected properly according to the estimation of partial derivatives and the varying one can be estimated from iteratively utilising I/O data. The result has also been extended to the nonlinear MAS with time-varying control input and an extended adaptive estimation-based TILC is developed by using time-varying control input to enhance the control performance. A fast convergence of both the proposed methods is achieved by removing the unnecessary error constraints at other time instants than the endpoint. Both the proposed methods is apparently data-driven since no model information is involved. The proposed finite time consensus control methods are confirmed to be effective under the directed graph through mathematic proof and extensive simulations.
Starting Page	2516
Ending Page	2525
Page Count	10
ISSN	17518644
Volume Number	12
e-ISSN	17518652
Issue Number	Issue 18, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/12/18
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2018.5602
Journal	IET Control Theory & Applications
Publisher Date	2018-10-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Control Adaptive Estimation-based Terminal Iterative Learning Control Combinatorial Mathematics Constant Learning Gain Convergence Data-driven Control Directed Graph Discrete Control System Discrete Time System Extended Adaptive Estimation-based TILC Finite Time Consensus Control Finite-time Consensus Control Iteration-time-varying Learning Gain Iterative Dynamic Linearisation Technique Iterative Learning Control Iterative Method Learning Control Law Linear Iteration-incremental Model Linear System Linearisation Technique Multi-agent System Multiagent System Multivariable Control System Nonlinear Control System Nonlinear Discrete-time MASs Nonlinear Discrete-time Multi-agent System Nonlinear MAS Self Adjusting Control System Time Varying Control System Time Varying System Time-varying Control Input Unknown Partial Derivatives Variable Learning Gain
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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