深海着陆车路径规划及跟踪控制方法  

Path Planning and Tracking Control Method of Deep-Sea Landing Vehicle

作　　者：周球 周悦[1] 孙洪鸣 郭威 吴凯 兰彦军 ZHOU Qiu;ZHOU Yue;SUN Hongming;GUO Wei;WU Kai;LAN Yanjun(School of Engineering,Shanghai Ocean University,Shanghai 201306,China;Institute of Deep-sea Science and Engineering,Chinese Academy of Sciences,Sanya 572000,Hainan,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]上海海洋大学工程学院,上海201306 [2]中国科学院深海科学与工程研究所,海南三亚572000 [3]中国科学院大学,北京100049

出　　处：《兵工学报》2023年第1期298-306,共9页Acta Armamentarii

基　　金：海南省重大科技计划项目(ZDKJ202016);海南省自然科学基金项目(2019RC260);三亚市院地科技合作项目(2019YD01);上海市水产动物良种创新与绿色养殖创新协同中心(A1-3605-21-000)。

摘　　要：为降低深海着陆车(DSLV)在复杂海底自主作业时规划路径长度及提高路径跟踪精度,提出一种变参数蚁群算法及自适应权重模型预测控制算法。改进了蚁群算法的启发算子和信息素挥发因子,减少规划路径长度和寻优迭代次数;基于DSLV运动学方程建立预测模型,并在跟踪目标函数中引入自适应权重调节思想。仿真结果表明:规划路径长度降低4.60%,跟踪精度提高47.6%;相比传统方法,新算法具有更好的性能,实现了短距离、高精度的路径规划及跟踪。A variable parameter ant colony optimization algorithm and an adaptive weight model predictive control algorithm are proposed to optimize the length of the path planned and improve the tracking accuracy of the deep-sea landing vehicle(DSLV) which operates autonomously on the complex seabed. The heuristic operator and pheromone evaporation factor of ant colony optimization are improved to reduce the length of the planned path and the number of iterations required to find the optimal path. Then, the prediction model is established based on the DSLV kinematics equation, and the idea of adaptive weight adjustment is introduced into the tracking objective function. The simulation results show that the planned path length is reduced by 4.60% and the tracking accuracy is improved by 47.6%. Compared with traditional methods, the proposed algorithms have better performance, realizing short-distance path planning and high-precision tracking.

关 键 词：深海着陆车 变参数蚁群算法 自适应权重 模型预测控制 路径跟踪 

分 类 号：TP242.3[自动化与计算机技术—检测技术与自动化装置]