Distributed adaptive iterative learning control for the consensus tracking of heterogeneous nonlinear multi-agent systems

Abstract

This paper addresses the consensus tracking problem of leader-following heterogeneous multi-agent systems with iterative learning control. The model of heterogeneous multi-agent systems consists of first-order and second-order nonlinear dynamics. It is assumed that only a portion of following agents can receive the leader’s information. The radial basis function neural network is introduced to deal with the nonlinear terms of following agents. Then, the distributed adaptive iterative learning control protocols with neural network are designed for following agents with different dynamics. Meanwhile, the adaptive update control laws for the time-varying parameters are proposed. Theoretical analysis shows that the consensus tracking problem of the given multi-agent systems can be guaranteed on the time domain and iterative domain. Finally, the validity of theoretical results is verified by a simulation example.

Iterative learning based consensus control for distributed parameter type multi‐agent differential inclusion systems

“…Compared with ODMAS, the literature on DPMAS is rather limited. By introducing radial basis function neural networks and proposing distributed adaptive ILC protocols, 30 guaranteed the consensus of the given DPMAS. Using nearest neighbor knowledge, 31 proposed $$$ PI $$$‐type ILC protocols to study the consensus control problem of second‐order hyperbolic DPMAS.…”

Distributed Coordination for a Class of High‐Order Multiagent Systems Subject to Actuator Saturations by Iterative Learning Control

“…An adaptive neural network approximation scheme was utilized to design the control protocols of the first-, second-, and high-order uncertain MASs in [10][11][12]. References [13,14] used the adaptive idea to design the fully distributed control protocols by adjusting the protocols gains, so that the global information dependent on topology matrix can be avoided, and the promising technique is more and more popular among researchers for different purposes [15][16][17][18][19], where adaptive iterative learning control is utilized to handle the consensus for MASs over a finite time interval [20][21][22][23][24].…”

Consensus of multi-agent systems via prescribed-time sliding mode control method

“…Research on multi-agent systems (MASs) can be traced back decades; convergence time, switching topology, fault-tolerant cooperative control, and many other things are considered in Wang et al (2019), Zhou et al (2021), Sun et al (2021), Ren and Beard (2005), Deng and Sun (2020), Yu et al (2011), Zhou et al (2018), Li et al (2011), and Sun and Mu (2020). For the research of nonlinear MASs, the fractional-order differential equation is used to describe the agent in nonlinear MASs and the internal delay and coupling delay of MASs are studied in Wang and Yang (2017).…”