Paper Info
Title
Adaptive fuzzy iterative learning control with initial-state learning for coordination control of leader-following multi-agent systems.
Abstract
We propose a distributed adaptive fuzzy iterative learning control (ILC) algorithm to deal with coordination control problems in leader-following multi-agent systems in which each follower agent has unknown dynamics and a non-repeatable input disturbance. The ILC protocols are designed with distributed initial-state learning and it is not necessary to fix the initial value at the beginning of each iteration. A fuzzy logical system is used to approximate the nonlinearity of each follower agent. A fuzzy learning component is an important learning tool in the protocol, and combined time-domain and iteration-domain adaptive laws are used to tune the controller parameters. The protocol guarantees that the follower agents track the leader for the consensus problem and keep at a desired distance from the leader for the formation problem on [0,T]. Simulation examples illustrate the effectiveness of the proposed scheme.
Year
DOI
Venue
2014
10.1016/j.fss.2013.10.010
Fuzzy Sets and Systems
Keywords
Field
DocType
Multi-agent system,Fuzzy system,Adaptive iterative learning control,Nonlinear system
Consensus,Control theory,Computer science,Control theory,Fuzzy logic,Multi-agent system,Initial value problem,Artificial intelligence,Adaptive neuro fuzzy inference system,Iterative learning control,Fuzzy control system,Machine learning
Journal
Volume
ISSN
Citations 
248
0165-0114
17
PageRank 
References 
Authors
0.64
19
2
Name
Order
Citations
PageRank
Jun-Min LI139036.09
jinsha li2343.62