基于人工鱼群算法的波浪滑翔器艏向模型辨识与控制（英文）

Unmanned wave glider heading model identification and control by artificial fish swarm algorithm

针对"海洋漫步者"号波浪滑翔器的艏向运动模型辨识、控制参数优化问题,引入人工鱼群算法并将其应用于波浪滑翔器艏向模型参数辨识和控制中。首先,在简化假设条件下,将波浪滑翔器从刚柔多体系统简化为一个"推进器+浮体"刚性系统,进而建立了波浪滑翔器水平面运动数学模型;然后,采取经验方法结合参数辨识的策略获取了模型参数,即部分模型参数由经验方法估算得到;但是鉴于艏向控制的特殊性和重要性,艏向模型参数基于水池试验数据并利用人工鱼群算法进行辨识获得,从而充分地利用有限试验数据以真实刻画系统的动力学特性。针对该艏向运动模型,基于人工鱼群算法进行艏向S面控制器的参数优化,完成"海洋漫步者"号波浪滑翔器的艏向预报对比及海上控制试验。水池试验验证了艏向运动模型波浪滑翔器艏向运动的预测具有较高的精度,包括艏向角和转艏角速度。在水池试验和海洋试验中波浪滑翔器均展现了较好的艏向控制性能,试验结果验证了所提出方法的有效性。

We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified.