Zilong Song

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.

This paper focuses on the safety-critical fixed-time formation control of quadrotor UAVs with disturbance and obstacle collision risk. The control scheme is organized in a distributed manner, with the leader’s position and velocity being estimated simultaneously by a fixed-time distributed observer. Meanwhile, a disturbance observer that combines fixed-time control theory and sliding mode control is designed to estimate the external disturbance. Based on these techniques, we design a nominal control law to drive UAVs to track the desired formation in a fixed time. Regarding obstacle avoidance, we first construct safety constraints using control barrier functions (CBFs). Then, obstacle avoidance can be achieved by solving an optimization problem with these safety constraints, thus minimally affecting tracking performance. The main contributions of this process are twofold. First, an exponential CBF is provided to deal with the UAV model with a high relative degree. Moreover, a robust exponential CBF is designed for UAVs with disturbance, which provides robust safety constraints to ensure obstacle avoidance despite disturbance. Finally, simulation results show the validity of the proposed method.

This letter studies the bearing-only formation tracking control problem for multi-agent systems in which the target formation moves with a time-varying reference velocity. We consider both single-integrator and double-integrator models and propose two control laws for them that can achieve this formation tracking control using only bearing measurements, without any other measurements or communication between agents. Moreover, these two control laws can be extended to deal with the system with bounded disturbances. The convergence of the systems under these two control methods is proven by rigorous mathematical derivations and simultaneously validated by numerical simulations.

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.