Abstract
This paper investigates the problem of prescribed-time formation control for multi-agent systems with directed communication topology, uncertain nonlinear dynamics, and non-vanishing random disturbances. To drive the formation error to zero within a prescribed time, a novel prescribed-time control lemma is developed. A distributed observer is designed to allow each follower to accurately estimate the leader's states within the prescribed time. Building on this, an observer-based prescribed-time formation control algorithm is proposed. The algorithm ensures that a disordered group of autonomous agents achieves the desired formation with zero error within the prescribed time, despite the presence of uncertain nonlinear dynamics and non-vanishing random disturbances. The prescribed time is arbitrarily predetermined a priori and independent of the agents' initial configurations and any other control parameters. Mathematically, the stability of the proposed control scheme is rigorously proven, where all observer and closed-loop system signals are bounded. Numerical simulations confirm the effectiveness of the proposed formation scheme.
| Original language | English |
|---|---|
| Pages (from-to) | 586-596 |
| Number of pages | 11 |
| Journal | IEEE/CAA Journal of Automatica Sinica |
| Volume | 13 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Mar 2026 |
Bibliographical note
Publisher Copyright:© 2014 Chinese Association of Automation.
Funding
This work was supported in part by the Fundamental Research Funds for the Central Universities (2025CDJZKKYJH-17, 2024CDJYDYL020), the Natural Science Foundation of Chongqing (CSTB2023NSCQ-LZX0026), the National Natural Science Foundation of China (W2411061, 624B2029), the Chongqing Municipal Commission of Economy and Informatization (68YJX-2025001001005), and the China Scholarship Council (202506050048).
Keywords
- Directed communication topology
- multi-agent system formation
- non-vanishing random disturbances
- prescribed-time control
- uncertain nonlinear dynamics
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