A Hierarchical Adaptive Observer Approach for Synchronization in Heterogeneous Multi-Agent Systems

This paper presents a novel observer-based synchronization framework designed for heterogeneous multi-agent systems operating under realistic conditions, such as discrete and noisy measurements, intermittent communication, and agent heterogeneity. The framework incorporates a hierarchical structure: a Filtered High-Gain Observer to reconstruct the leader's state in the presence of noisy and discrete measurements, a Distributed Observer which enables each follower to independently estimate the state of the leader for synchronization purposes, and a Adaptive Local Observer for each follower that simultaneously estimates its own states and unknown parameters. The local observer incorporates a closed-loop output predictor to address challenges arising from discrete measurements and ensure continuous estimation. Stability and robustness are shown using a Lyapunov-based approach, and simulation results validate the effectiveness of the proposed method to maintain precise coordination between vehicles. This approach improves performance, making it suitable for real-time vehicle platooning applications.