This paper addresses distributed robust learning-based control for consensus formation tracking of multiple underwater vessels, in which the system parameters of the marine vessels are assumed to be entirely unknown and subject to the modeling mismatch, oceanic disturbances, and noises. Towards this end, graph theory is used to allow us to synthesize the distributed controller with a stability guarantee. Due to the fact that the parameter uncertainties only arise in the vessels' dynamic model, the backstepping control technique is then employed. Subsequently, to overcome the difficulties in handling time-varying and unknown systems, an online learning procedure is developed in the proposed distributed formation control protocol. Moreover, modeling errors, environmental disturbances, and measurement noises are considered and tackled by introducing a neurodynamics model in the controller design to obtain a robust solution. Then, the stability analysis of the overall closed-loop system under the proposed scheme is provided to ensure the robust adaptive performance at the theoretical level. Finally, extensive simulation experiments are conducted to further verify the efficacy of the presented distributed control protocol.

翻译：本文针对多水下航行器的协同编队跟踪问题，提出一种分布式鲁棒学习控制方法。该方法假设航行器系统参数完全未知，且存在建模失配、海洋扰动及噪声干扰。为此，首先引入图论理论以实现具有稳定性保障的分布式控制器综合。由于参数不确定性仅出现在航行器动力学模型中，故采用反步控制技术。为克服时变未知系统的处理困难，在所提出的分布式编队控制协议中开发了一种在线学习机制。此外，通过在控制器设计中引入神经动力学模型处理建模误差、环境扰动及测量噪声，从而获得鲁棒解。随后，对所提出方案下闭环系统的稳定性进行理论分析，确保其鲁棒自适应性能。最后，通过大量仿真实验进一步验证所提分布式控制协议的有效性。