Affiliation:
1. State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Engineering Science, College of Engineering Peking University Beijing China
Abstract
AbstractThis article proposes a localized data‐driven consensus framework for leader‐follower multi‐agent systems with unknown continuous‐time agent dynamics for both noiseless and noisy data scenarios. In this setting, each follower calculates its feedback control gain based on its locally sampled data, including the states, state derivatives, and inputs. We propose novel distributed control protocols that synchronize the distinct dynamic feedback gains and achieve leader‐follower consensus. Design methods are provided for the devised data‐based consensus control algorithms, which rely on low‐dimensional linear matrix inequalities. The validity of the developed algorithms is demonstrated via simulation examples.
Funder
National Science and Technology Major Project
National Natural Science Foundation of China