事件触发的多AUV编队模型预测控制算法  

Event-triggered Model Predictive Control Algorithm for Multi-AUV Formation

作　　者：王琳玲 庞文 朱大奇 Wang Linling;Pang Wen;Zhu Daqi(Logistics Engineering College,Shanghai Maritime University,Shanghai 201306,China;School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海海事大学物流工程学院,上海201306 [2]上海理工大学机械工程学院,上海200093

出　　处：《兵工自动化》2023年第10期67-77,共11页Ordnance Industry Automation

基　　金：国家自然科学基金重点项目(62033009、U1706224);上海市科技创新行动计划(20510712300)。

摘　　要：针对复杂环境中自治水下机器人(autonomous underwater vehicle,AUV)编队的避障控制问题,提出一种基于事件触发的模型预测控制(model predictive control,MPC)算法。建立水下机器人运动模型,结合领航-跟随式队形控制方法,利用领航AUV的位置信息和编队期望队形得到虚拟AUV的航行轨迹及速度信息,将其作为跟随AUV的航行参考轨迹;对传统人工势场法(artificial potential field,APF)进行适应性改进,以满足AUV编队在障碍物环境中避障规划的需求;设计一种基于跟随AUV轨迹预测值与实际值误差的事件触发机制来减少求解优化问题的计算量,降低计算负担。结果表明:与其他算法相比,该算法仿真结果具有可行性和有效性。An event-triggered model predictive control(MPC)algorithm is proposed to solve the obstacle avoidance problem of autonomous underwater vehicle(AUV)formation in complex environment.Tablishing a motion model of the AUV,combining a leader-follower formation control method,and obtaining the navigation track and speed information of a virtual AUV by using the position information of the leader AUV and an expected formation of the formation,and taking the navigation track and the speed information as the navigation reference track of a following AUV;The traditional artificial potential field(APF)method is improved adaptively to meet the needs of AUV formation obstacle avoidance planning in obstacle environment;An event-triggered mechanism based on the error between the predicted and actual values of the following AUV trajectory is designed to reduce the amount of calculation for solving the optimization problem and reduce the computational burden.The simulation results show that the proposed algorithm is feasible and effective compared with other algorithms.

关 键 词：AUV 领航-跟随式 编队避障 事件触发 MPC APF 

分 类 号：TP242.3[自动化与计算机技术—检测技术与自动化装置]