Quantised data-driven iterative learning bipartite consensus control for unknown heterogeneous linear MASs with varying trial lengths

Abstract

This paper aims to realise the robust output bipartite consensus for unknown heterogeneous linear time-varying multiagent systems (MASs) subject to varying trial lengths, unknown measurement disturbances and data quantisation. To this end, inspired by the idea of quantised control, a quantised data-driven adaptive iterative learning bipartite consensus (AILBC) method is proposed. Specifically, to address the problem of varying trial lengths, a distributed auxiliary output prediction system is constructed based on the agents' input-output (I/O) dynamic relationship. An adaptive update protocol is developed to estimate the measurement disturbances and unknown parameters of I/O dynamic relationship. Subsequently, a quantised distributed data-driven iterative learning control (ILC) approach based on the quantised output information is proposed for MASs to achieve robust bipartite consensus tracking, with an attempt to relax the need of explicit model information. The bipartite consensus tracking errors are ultimately bounded through rigorous analysis, and this result is further extended to switching topologies. Finally, numerical simulations are conducted to verify the validity of the AILBC method.

Keywords:

• Bipartite consensus
• data-driven iterative learning control (ILC)
• multiagent systems (MASs)
• data quantisation
• varying trial lengths

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, Hui Ma, upon reasonable request.

Additional information

Funding

This work was partially supported by the National Natural Science Foundation of China (62121004, 62033003, 61973091, 62203119), the Local Innovative and Research Teams Project of Guangdong Special Support Program (2019BT02X353), the Natural Science Foundation of Guangdong Province (2023A1515011527, 2022A1515011506), and the China Postdoctoral Science Foundation (BX20220095, 2022M710826).