基于模型参考的多自主水下航行器自适应覆盖控制

针对海洋监测过程中由水下粘滞物等因素引起系统模型参数变化的情况,提出了一种基于模型参考的多自主水下航行器(autonomous underwater vehicles,AUV)自适应覆盖控制方法,旨在合理分配多AUV资源,提高目标海域的监测效率。首先根据给出的系统代价函数,应用质心Voronoi分配原则对目标海域进行最优分配,确定多AUV最优监测目标。在此基础上,利用基于模型参考的自适应控制方法,设计在AUV模型参数未知的情况下,多AUV的最优覆盖控制方法,使得覆盖网络能够从任意初始位置收敛到最优覆盖配置。最后通过仿真实验验证了上述算法的可行性与有效性。

Model based adaptive coverage control for multiple autonomous underwater vehicles

The model adaptive coverage control problems with unknown model parameters are studied. A novel model based adaptive coverage control strategy for multi-autonomous underwater vehicle (AUV) systems is proposed such that the coverage network would converge to the optimal deployment. Firstly, according to the proposed cost function and the Voronoi partition, the mission region is divided into subregions optimally with respect to the multi-AUV systems. Using this region partition and model based adaptive control as the theoretical foundation, an optimal adaptive coverage control algorithm with unknown model parameters is proposed to deploy the coverage network to the optimal positions. Finally, numerical simulations are provided to illustrate the feasibility and effectiveness of the proposed approaches.