Global iterative learning control based on fuzzy systems for nonlinear multi-agent systems with unknown dynamics

A new global fuzzy iterative learning scheme is proposed for nonlinear multi-agent systems with unknown dynamics. Unlike the traditional design scheme where the fuzzy systems are used as the feedback compensators, the fuzzy systems are used as the feedforward compensators to describe the unknown dynamics, which avoids the restriction on the states of the control systems. In this scheme, we design a hybrid fuzzy adaptive learning controller according to the characteristics of the network structure. On this basis, using the Nussbaum function, this paper extends the above global fuzzy iterative learning scheme to solve the consensus control problem of multi-agent systems with unknown control directions over the iterations. Finally, the effectiveness of the above hybrid learning protocols is verified through simulations. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a new global fuzzy iterative learning scheme for nonlinear multi-agent systems with unknown dynamics. Unlike traditional design schemes, where fuzzy systems are used as feedback compensators, this scheme utilizes fuzzy systems as feedforward compensators to describe the unknown dynamics, thus avoiding restrictions on the control system states. In this scheme, a hybrid fuzzy adaptive learning controller is designed based on the characteristics of the network structure. The effectiveness of this hybrid learning protocol is verified through simulations.