Data-Driven Bearing-Based Formation Control via Internal Model Design

Yifei Li, Wenjie Liu, Xu Fang, Gang Wang, Lihua Xie, Jian Sun

Abstract: Bearing-based formation control aims to achieve a desired geometric configuration through prescribed bearing constraints among interconnected agents. Although extensively studied in integrator-based systems under idealized assumptions, its application to multi-leader multi-follower systems subject to external disturbances and unknown dynamics remains largely unexplored. This paper addresses this gap by presenting a data-driven state feedback controller based on the internal model principle. The leaders’ positions are generated by linear autonomous systems, allowing each leader to have a distinct and time-varying velocity. By incorporating both the leaders’ state signal and disturbance signal, the formation task is reformulated as a cooperative output regulation problem. Leveraging noisy input–state data, a data-dependent semidefinite program (SDP) is then formulated for each follower, whose solution directly yields a robust controller that ensures the desired formation and rejects disturbances. Numerical examples validate the effectiveness of the proposed method.

External IDs:dblp:conf/cdc/LiLFWXS25