Formation Control of an Underactuated Autonomous Undersea Vehicle Based on Distributed Model Predictive Control

GUO Yuanbo; LI Qi; MIN Boxu; GAO Jian; CHEN Yimin

doi:10.11993/j.issn.2096-3920.202204018

Volume 31 Issue 3

Jun 2023

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Article Navigation > Journal of Unmanned Undersea Systems > 2023 > 31(3): 405-412

GUO Yuanbo, LI Qi, MIN Boxu, GAO Jian, CHEN Yimin. Formation Control of an Underactuated Autonomous Undersea Vehicle Based on Distributed Model Predictive Control[J]. Journal of Unmanned Undersea Systems, 2023, 31(3): 405-412. doi: 10.11993/j.issn.2096-3920.202204018

Citation:

GUO Yuanbo, LI Qi, MIN Boxu, GAO Jian, CHEN Yimin. Formation Control of an Underactuated Autonomous Undersea Vehicle Based on Distributed Model Predictive Control[J]. Journal of Unmanned Undersea Systems, 2023, 31(3): 405-412. doi: 10.11993/j.issn.2096-3920.202204018

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Formation Control of an Underactuated Autonomous Undersea Vehicle Based on Distributed Model Predictive Control

doi: 10.11993/j.issn.2096-3920.202204018

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Received Date: 2022-04-24
Rev Recd Date: 2022-06-18

Available Online: 2022-09-26

Abstract

Abstract

Compared with centralized model predictive control, distributed model predictive control(DMPC) is characterized by lower computational complexity and stronger fault tolerance and robustness, and it is widely used in multiagent formation control. In this study, an underactuated autonomous undersea vehicle(AUV) formation control method based on DMPC is proposed. Based on local neighbor information, the cost function and constraints of predictive control are constructed for each AUV controller, and the optimal control input in a certain time domain is solved by using an optimization algorithm. To solve the obstacle avoidance problem and communication delay problem that may exist in the formation system, obstacle avoidance methods based on distance and relative line of sight, as well as a waiting mechanism for problem solving after receiving all neighbor information, are designed. The simulation results demonstrate that, by using the method proposed in this study, the multi-AUV formation can remain stable under the conditions of obstacles and communication delays.
- autonomous undersea vehicle,
- distributed model predictive control,
- obstacle avoidance in formation,
- communication delay

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References(19)

References

[1]	姚金艺, 曾庆军, 周启润, 等. 全驱动AUV系统路径跟踪设计与实现[J]. 水下无人系统学报, 2019, 27(4): 452-458. Yao Jinyi, Zeng Qingjun, Zhou Qirun, et al. Design and implementation of a path tracking system for fully actuated AUV[J]. Journal of Unmanned Undersea Systems, 2019, 27(4): 452-458.
[2]	张鑫明, 韩明磊, 余益锐, 等. 潜艇与UUV协同作战发展现状及关键技术[J]. 水下无人系统学报, 2021, 29(5): 497-508. Zhang Xinming, Han Minglei, Yu Yirui, et al. Development and key technologies of submarine-UUV cooperative operation[J]. Journal of Unmanned Undersea Systems, 2021, 29(5): 497-508.
[3]	Yan Z, Yang Z, Yue L, et al. Discrete-time coordinated control of leader following multiple AUVs under switching topologies and communication delays[J]. Ocean Engineering, 2019, 172: 361-372. doi: 10.1016/j.oceaneng.2018.12.018
[4]	Liang H T, Fu Y F, Gao J, et al. Finite-time velocity-observed based adaptive output-feedback trajectory tracking formation control for underactuated unmanned underwater vehicles with prescribed transient performance[J]. Ocean Engineering, 2021, 233: 109071. doi: 10.1016/j.oceaneng.2021.109071
[5]	Li G N, Dong H Y, Xu H L. Triangle formation control of multi-auvs with communication constraints[J]. Applied Mechanics and Materials, 2014, 3335(596): 631-635.
[6]	Yuan J, Zhang F L, Zhou Z H. Finite-time formation control for autonomous underwater vehicles with limited speed and communication range[J]. Applied Mechanics and Materials, 2014, 3006(511-512): 909-912.
[7]	李正平, 鲜斌. 基于虚拟结构法的分布式多无人机鲁棒编队控制[J]. 控制理论与应用, 2020, 37(11): 2423-2431. doi: 10.7641/CTA.2020.00080 Li Zhengping, Xian Bin. Robust distributed formation control of multiple unmanned aerial vehicles based on virtual structure[J]. Control Theory& Applications, 2020, 37(11): 2423-2431. doi: 10.7641/CTA.2020.00080
[8]	刘安东, 秦冬冬. 基于虚拟结构法的多移动机器人分布式预测控制[J]. 控制与决策, 2021, 36(5): 1273-1280. Liu Andong, Qin Dongdong. Distributed predictive control of multiple mobile robots based on virtual structure method[J]. Control and Decision, 2021, 36(5): 1273-1280.
[9]	Yan X, Jiang D P, Miao R L, et al. Formation control and obstacle avoidance algorithm of a multi-USV system based on virtual structure and artificial potential field[J]. Journal of Marine Science and Engineering, 2021, 9(2): 161-177. doi: 10.3390/jmse9020161
[10]	Ahn C, Kim Y. Point targeting of multisatellites via a virtual structure formation flight scheme[J]. Journal of Guidance, Control, and Dynamics, 2012, 32(4): 1330-1344.
[11]	袁健, 唐功友. 采用一致性算法与虚拟结构的多自主水下航行器编队控制[J]. 智能系统学报, 2011, 6(3): 248-253. Yuan Jian, Tang Gongyou. Formation control of autonomous underwater vehicles with consensus algorithms and virtual structure[J]. CAAI Transactions on Intelligent Systems, 2011, 6(3): 248-253.
[12]	Yang S, Xi L P. Quadrotor formation control method based on graph and consistency theory[J]. International Journal of Advanced Network, Monitoring and Controls, 2018, 3(2): 54-61. doi: 10.21307/ijanmc-2018-032
[13]	Zhang N, Zhang X F. Application of multi-agent consistency analysis based on finite state machine in driverless vehicles formation control[J]. IFAC-Papers on Line, 2018, 51(31): 732-737. doi: 10.1016/j.ifacol.2018.10.166
[14]	张广洁, 严卫生, 高剑. 基于模型预测控制的欠驱动AUV直线路径跟踪[J]. 水下无人系统学报, 2017, 25(2): 82-88. Zhang Guangjie, Yan Weisheng, Gao Jian. Straight line tracking of underactuacted AUV based on model predictive control[J]. Journal of Unmanned Undersea Systems, 2017, 25(2): 82-88.
[15]	赵超轮, 戴邵武, 赵国荣, 等. 基于分布式模型预测控制的无人机编队控制[J]. 控制与决策, 2022, 37(7): 1763-1771. Zhao Chaolun, Dai Shaowu, Zhao Guorong, et al. Formation control of multi-UAV based on distributed model predictive control algorithm[J]. Control and Decision, 2022, 37(7): 1763-1771.
[16]	顾海艳, 陈亮, 王多点. 基于模型预测控制的无人机时空协同航迹规划[J]. 计算机工程与应用, 2021, 57(23): 270-279. Gu Haiyan, Chen Liang, Wang Duodian. Space-time cooperative path planning for multi-UAV using model predictive control[J]. Computer Engineering and Applications, 2021, 57(23): 270-279.
[17]	Cai Z H, Zhou H, Zhao J, et al. Formation control of multiple unmanned aerial vehicles by event-triggered distributed model predictive control[J]. IEEE Access, 2018, 6: 55614-55627. doi: 10.1109/ACCESS.2018.2872529
[18]	Cai Z H, Wang L H, Zhao J, et al. Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs[J]. Chinese Journal of Aeronautics, 2020, 33(3): 1037-1056. doi: 10.1016/j.cja.2019.07.016
[19]	Liu C, Hu Q, Wang X. Distributed guidance-based formation control of marine vehicles under switching topology[J]. Applied Ocean Research, 2021, 106: 102465. doi: 10.1016/j.apor.2020.102465