自持式智能浮标的低功耗悬停控制方法

针对自持式智能浮标悬停精度与能耗之间的矛盾,借助地转海洋学实时观测阵(Array for real-time geo strophic oceanography,Argo)计划所观测的历史数据,提出了一种低功耗的浮标悬停控制方式。由浮标运动学模型可知,悬停时浮标外油囊体积只与悬停深度的海水密度、温度相关,因此利用Argo历史数据对目标海域进行密度、温度拟合曲线提取,得到了悬停深度与外油囊体积的关系。采用遗传算法对悬停过程进行了能耗最优的运动规划,最终得到悬停控制过程的具体运动控制方式。与双闭环模糊PID控制方法和径向基(Radial basis function,RBF)神经网络PID方法对比,在定深精度相当的情况下,所提出的方法能够将能耗降至1/50。最后,利用半实物仿真系统,对上述方法进行了实物验证,在不考虑拟合曲线带来的误差时,悬停的平均误差为2.62 m,能耗为3.214×10⁴J,能耗与理论值的误差仅为0.65%。表明了该方法的有效性和可靠性,以及综合考虑精度和能耗上的优势。

Low energy consumption depth control method of self-sustaining intelligent buoy

Aiming at the contradiction between the depth control accuracy and the energy consumption of the self-sustaining intelligent buoy, a low energy consumption depth control method based on historical array for real-time geostrophic oceanography (Argo)data is proposed.As known from the buoy kinematic model,the volume of the external oil sac only depends on the density and temperature of seawater at hovering depth.Hence,we use historical Argo data to extract the fitting curves of density and temperature,and obtain the relationship between the hovering depth and the volume of the external oil sac.Genetic algorithm is used to carry out the optimal energy consumption motion planning for the depth control process,and the specific motion strategy of depth control process is obtained.Compared with dual closed-loop fuzzy PID control method and radial basis function(RBF)-PID method,the proposed method reduces energy consumption to 1/50 with the same accuracy. Finally,a hardware-in-the-loop simulation system was used to verify this method.When the error caused by fitting curves is not considered,the average error is 2.62 m,the energy consumption is 3.214×104 J,and the error of energy consumption is only 0.65％.It shows the effectiveness and reliability of the method as well as the advantages of comprehensively considering the accuracy and energy consumption.