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.

研究了一种适用于多个水下舰艇的分布式鲁棒学习控制策略，考虑到完全不知道舰艇的系统参数以及建模误差、海洋干扰和噪声，通过图论合成具有稳定性保证的分布式控制器，并采用回步控制技术处理动态模型中的参数不确定性，提出了在线学习过程来处理时变和未知系统，并引入神经动力学模型来解决建模误差、环境干扰和测量噪声问题，通过稳定性分析确保了理论层面上的鲁棒自适应性能，最后通过大量的仿真实验验证了所提出的分布式控制策略的有效性。