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Adaptive fuzzy iterative learning control with initial-state learning for coordination control of leader-following multi-agent systems

Authors
Journal
Fuzzy Sets and Systems
0165-0114
Publisher
Elsevier
Volume
248
Identifiers
DOI: 10.1016/j.fss.2013.10.010
Keywords
  • Multi-Agent System
  • Fuzzy System
  • Adaptive Iterative Learning Control
  • Nonlinear System
Disciplines
  • Computer Science
  • Design

Abstract

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.

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