Design of data-driven mode-free iterative learning controller based higher order parameter estimation for multi-agent systems consistency tracking

In this study, an adaptive data-driven control protocol design scheme based on higher order parameter (HOP) estimation and iterative learning is proposed for a class of nonaffine multi-agent systems (MAS) with unknown nonlinearity to solve the consistency tracking problem of MAS with fixed and switching topology communications. The control protocol mainly comprises HOP estimation and an iterative learning controller. The iterative learning control (ILC) algorithm is mainly used for system synergy, and the adaptive control algorithm based on HOP estimation mainly completes the system stabilization. The main advantage of the control protocol is that it uses only the input/output (I/O) data of the agents to complete the consensus tracking task and does not require specifying the dynamical system of each agent. The control protocol has a modular design, in which the iterative learning and HOP estimation algorithms complement each other without affecting the structure of the controller. The effectiveness of the control protocol is demonstrated using two simulation experiments.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study proposes an adaptive data-driven control protocol design scheme for nonaffine multi-agent systems with unknown nonlinearity. The control protocol uses higher order parameter estimation and iterative learning to solve the consistency tracking problem of multi-agent systems with fixed and switching topology communications. It achieves consensus tracking by using only the input/output data of the agents, without requiring knowledge of their dynamical systems. The control protocol has a modular design that combines iterative learning and higher order parameter estimation algorithms. Its effectiveness is demonstrated through simulation experiments.