Niu Xiaobing, Gao Shengnan, Xu Zhibin, Feng Shiliang
School of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
China State Shipbuilding Corporation Limited, Beijing, China.
Front Neurorobot. 2022 Sep 29;16:1028656. doi: 10.3389/fnbot.2022.1028656. eCollection 2022.
This paper presents a distributed constant bearing guidance and model-free disturbance rejection control method for formation tracking of autonomous surface vehicles subject to fully unknown kinetic model. First, a distributed constant bearing guidance law is designed at the kinematic level to achieve a consensus task. Then, by using an adaptive extended state observer (AESO) to estimate the total uncertainties and unknown input coefficients, a simplified model-free kinetic controller is designed based on a dynamic surface control (DSC) design. It is proven that the closed-loop system is input-to-state stable The stability of the closed-loop system is established. A salient feature of the proposed method is that a cooperative behavior can be achieved without knowing any priori information. An application to formation control of autonomous surface vehicles is given to show the efficacy of the proposed integrated distributed constant bearing guidance and model-free disturbance rejection control.
本文提出了一种用于自主水面舰艇编队跟踪的分布式恒向制导和无模型干扰抑制控制方法,该方法适用于完全未知动力学模型的情况。首先,在运动学层面设计了一种分布式恒向制导律,以实现一致性任务。然后,通过使用自适应扩张状态观测器(AESO)来估计总不确定性和未知输入系数,基于动态面控制(DSC)设计了一种简化的无模型动力学控制器。证明了闭环系统是输入到状态稳定的,建立了闭环系统的稳定性。该方法的一个显著特点是,无需任何先验信息即可实现协同行为。给出了该方法在自主水面舰艇编队控制中的应用,以展示所提出的集成分布式恒向制导和无模型干扰抑制控制的有效性。