 Consensus tracking for nonlinear multi-agent systems with unknown disturbance by using model free adaptive iterative learning controlYingchun Wang, Haifeng Li, Xiaojie Qiu and Xiangpeng Xie Applied Mathematics and Computation, 2020, vol. 365, issue C Abstract: In this paper, a distributed disturbance-compensation based model free adaptive iterative learning control (MFAILC) algorithm is proposed to achieve the consensus tracking of nonlinear multi-agent systems (MAS) with unknown disturbance. Here, both fixed and iteration varying topologies are considered. A general dynamic linearization model with disturbance input is first proposed to each agent along the iteration axis for nonlinear MAS. Due to the existence of unknown disturbance, an online disturbance estimation algorithm is proposed to estimate actual disturbance only based on the input/output (I/O) measurement data. Then, a distributed MFAILC method with disturbance compensation is developed such that consensus tracking errors are convergent. Last, the effectiveness of the developed method can be illustrated from the simulation examples. Keywords: Multi-agent systems (MAS); Consensus tracking; Model-free adaptive iterative learning control (MFAILC); Disturbance compensation, (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:365:y:2020:i:c:s0096300319306939 DOI: 10.1016/j.amc.2019.124701 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.