In actual application, the energy utilization rate of underwater glider directly affects the total voyage range. When underwater glider is used for executing exploration mission for a fixed point, the position that the glider resurfaces should be accurate enough. In this paper, we employ a multi-objective optimization method to determine the control parameters values that can maximize the position accuracy that the glider resurfaces and the energy utilization rate simultaneously. Especially, the optimization of this paper considers the effect of uncertain input errors. The control parameters include the net buoyancy adjustment amount and the movable mass block translation amount. The input errors include the control parameters errors, the motion depth error and the current. Based on the dynamic model of an underwater glider, we propose the calculation model and evaluation flow that are used for analyzing the glider position accuracy and energy utilization rate, considering the effect of uncertain input errors. Besides, a combinatorial experimental design method is proposed to calculate the performance evaluation parameters under different control parameters values. Then the radial basis function neural network is employed to establish the surrogate models of performance evaluation parameters to participate in the optimization calculation, which can improve the optimization efficiency. After optimization calculation based on the non-dominated sorting genetic algorithm II, we obtain a Pareto optimal set consisting of 257 sets of non-dominated solutions. Finally, the selection rule of optimal control parameters values is given, and the optimization results are validated under 3 sets of solutions. This research may be valuable for the improvement of the glider work quality.

在实际应用中，水下滑翔机的能源利用率直接影响到总航程。使用水下滑翔机执行固定点探测任务时，滑翔机浮出水面的位置要足够准确。在本文中，我们采用多目标优化方法来确定可以同时最大化滑翔机重新浮出水面的位置精度和能量利用率的控制参数值。特别是，本文的优化考虑了不确定输入误差的影响。控制参数包括净浮力调整量和可动质量块平移量。输入误差包括控制参数误差、运动深度误差和电流。基于水下滑翔机的动力学模型，考虑到不确定输入误差的影响，我们提出了用于分析滑翔机位置精度和能量利用率的计算模型和评估流程。此外，提出了一种组合实验设计方法，计算不同控制参数值下的性能评价参数。然后利用径向基函数神经网络建立性能评价参数的代理模型参与优化计算，提高优化效率。经过基于非支配排序遗传算法II的优化计算，我们得到一个由257组非支配解组成的帕累托最优集。最后给出了最优控制参数值的选择规则，优化结果在3套方案下得到验证。这项研究可能对滑翔机工作质量的提高有价值。