一种基于PILCO算法的智能浮体运动控制方法  

Motion Control Method of Autonomous Surface Vehicle Based on the PILCO Algorithm

作　　者：张尚 杨睿[1,2] 陈震 黎明[1,2] ZHANG Shang;YANG Rui;CHEN Zhen;LI Ming(College of Engineering,Ocean University of China,Qingdao 266100,China;Shandong Marine Intelligent Equip-ment Technology Engineering Research Center,Qingdao 266100,China)

机构地区：[1]中国海洋大学工程学院,山东青岛266100 [2]山东省海洋智能装备技术工程研究中心,山东青岛266100

出　　处：《水下无人系统学报》2021年第5期541-549,共9页Journal of Unmanned Undersea Systems

基　　金：国家自然科学基金项目资助(51709245);国家重点研究发展计划项目资助(2017YFC1405203).

摘　　要：随着人们对海洋探索的不断深入,开发一种自主性强、灵活度高、可重构的智能浮体(ASV)至关重要。文中以四推进器ASV为研究对象,建立了其动力学模型,基于概率推理的学习控制算法设计了控制器,并进行了定点控制和轨迹跟踪的仿真实验。仿真结果表明:ASV仅需进行少量的实验即可获得自主学习控制策略,在有水流扰动或采用近似动力学模型时,能够实现对其的运动控制,从而验证了文中算法的有效性。A highly autonomous,flexible,and reconfigurable autonomous surface vehicle(ASV)must be developed to fulfill the needs for ocean exploration.In this study,an ASV composed of four thrusters is analyzed by establishing the dynamic model of the ASV,designing its controller based on the probabilistic inference learning to control(PILCO)algorithm,and conducting simulation experiments of fixed-point control and trajectory tracking.The simulation results show that the ASV model can autonomously learn the control strategy in a small number of experiments and realize motion control during a water flow disturbance or when using an approximate dynamic model,thereby verifying the effectiveness of the proposed algorithm.

关 键 词：智能浮体 基于概率推理的学习控制 定点控制 轨迹跟踪 

分 类 号：U674.38[交通运输工程—船舶及航道工程] TP242.6[交通运输工程—船舶与海洋工程]