Recently, the containment control for multi-agent systems (MASs) with communication delays has been studied. However, in these existing results, the assumptions for delays are too strong, leading to deviations from reality; moreover, safety is rarely considered, especially under a surging number of collision threats and the state constraint. In this work, we focus on the containment control for MASs with nonuniform time-varying communication delays; the formation configuration is extended to a dynamic one with time-varying velocity, and the model of agents is extended to the Euler–Lagrange (EL) model with uncertainty. We present sufficient conditions for containment control in the above case by utilizing the Lyapunov–Krasovskii function and the linear matrix inequalities (LMIs). Moreover, we propose a robust control barrier function (CBF)-based obstacle avoidance approach that ensures MAS safety through optimal control, thus better synthesizing the conflict between safety and the original control goals. In addition, considering that model uncertainty can adversely affect CBFs, we propose an observer with a quantified error to provide CBFs with robustness against uncertainty. Finally, simulation results demonstrate the validity of the proposed method.

This research addresses the challenge of formation control among multiple homogeneous autonomous underwater helicopters (AUHs) in the presence of external disturbances and complex dynamic characteristics. The study introduces a novel approach by integrating both disturbance and state observers within the control law framework to manage external disturbances and the immeasurability of velocity, respectively. Concurrently, localized radial basis function neural networks (RBFNNs) of identical configurations are incorporated into the formation control law to assimilate model uncertainties. Building upon this integration, an experience-based formation control strategy is developed, leveraging accumulated knowledge to diminish computational demands while maintaining stipulated performance criteria. Furthermore, the incorporation of a finite-time prescribed performance control (FTPPC) technique enhances the learning process’s efficiency by expediting convergence. Numerical simulations are presented to validate the efficacy of the proposed methodology.