Iterative learning control for multi-agent systems with impulsive consensus tracking

Xiaokai Cao,Dong Shen,Michal Fečkan,JinRong Wang

doi:10.15388/namc.2021.26.20981

Abstract

In this paper, we adopt D-type and PD-type learning laws with the initial state of iteration to achieve uniform tracking problem of multi-agent systems subjected to impulsive input. For the multi-agent system with impulse, we show that all agents are driven to achieve a given asymptotical consensus as the iteration number increases via the proposed learning laws if the virtual leader has a path to any follower agent. Finally, an example is illustrated to verify the effectiveness by tracking a continuous or piecewise continuous desired trajectory.

Highlights

Multi-agent systems (MAS) have been widely used in various disciplines such as unmanned vehicles, wireless sensor networks, and communication networks in the past c 2021 Authors
To study the problem of uniform tracking of impulsive MAS is to study whether the agents can return to the predetermined trajectory through the information exchange after being disturbed by external environments
For a robot performing a trajectory tracking task over a finite time interval, iterative learning control (ILC) uses the error information measured during the previous or previous operations to correct the control input, such that the operation performance can be improved along the iteration axis

Summary

Introduction

Multi-agent systems (MAS) have been widely used in various disciplines such as unmanned vehicles, wireless sensor networks, and communication networks in the past. To study the problem of uniform tracking of impulsive MAS is to study whether the agents can return to the predetermined trajectory through the information exchange after being disturbed by external environments. Impulsive control approach is advantageous in simplicity and flexibility for such kind of systems because the standard continuous state information is not required As a consequence, this approach has been offered to study uniform tracking problem [9–11, 15, 27, 28, 31, 39] and adaptive consistency and synchronization problems [5, 7, 22–25, 29] for MASs. For a robot performing a trajectory tracking task over a finite time interval, iterative learning control (ILC) uses the error information measured during the previous or previous operations to correct the control input, such that the operation performance can be improved along the iteration axis.

Preliminaries and notation

Controllability results

An example

Conclusion