深海着陆车路径规划及跟踪控制方法

doi:10.12382/bgxb.2022.0089

摘要/Abstract

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。

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

Abstract:

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.

Key words: deep-sea landing vehicle, variable parameter ant colony optimization, adaptive weight, model predictive control, path tracking

中图分类号:

TP242.3

周球, 周悦, 孙洪鸣, 郭威, 吴凯, 兰彦军. 深海着陆车路径规划及跟踪控制方法[J]. 兵工学报, 2023, 44(1): 298-306.

ZHOU Qiu, ZHOU Yue, SUN Hongming, GUO Wei, WU Kai, LAN Yanjun. Path Planning and Tracking Control Method of Deep-Sea Landing Vehicle[J]. Acta Armamentarii, 2023, 44(1): 298-306.

图/表 12

图1 双侧电机驱动示意图

Fig.1 Schematic diagram of the dual motor drive

图2 DSLV差速运动示意图

Fig.2 Diagram of DSLV differential motion

图3 VACO-AWMPC总体设计框架

Fig.3 Overall design framework of VACO-AWMPC

图4 MPC路径跟踪示意图

Fig.4 Schematic diagram of MPC path tracking

图5 路径规划结果对比

Fig.5 Comparison of path planning results

表1 仿真实验结果平均值对比

Table 1 Comparison of mean values of simulation results

算法及减少率	平均路径长度/m	平均收敛迭代次数
ACO	46.70	99
VACO	44.53	43
减少率/%	4.60	56.57

表2 预测模型参数及配置

Table 2 Parameters and configuration of the predictive model

参数	数值	参数	数值
d/m	1.37	$u ˜ m a x$	[1.5,0.8]
D/m	1.61	Δ $u ˜ m i n$	[-0.01,-0.05]
r_d/m	0.1	Δ $u ˜ m a x$	[0.01,0.05]
N_c	5	ξ_max	[30,30,∞]
N_p	10	ξ_min	[0,0,-∞]
Q	diag(6,6,0.1)	L/m	0.5
T/s	0.23	a₁	40
v_r/(m·s^-1)	0.35	b₁	7.41
$u ˜ m i n$	[-1.5,-0.8]	s₁、s_r	0.13

表2 预测模型参数及配置

Table 2 Parameters and configuration of the predictive model

参数	数值	参数	数值
d/m	1.37	$u ˜ m a x$	[1.5,0.8]
D/m	1.61	Δ $u ˜ m i n$	[-0.01,-0.05]
r_d/m	0.1	Δ $u ˜ m a x$	[0.01,0.05]
N_c	5	ξ_max	[30,30,∞]
N_p	10	ξ_min	[0,0,-∞]
Q	diag(6,6,0.1)	L/m	0.5
T/s	0.23	a₁	40
v_r/(m·s^-1)	0.35	b₁	7.41
$u ˜ m i n$	[-1.5,-0.8]	s₁、s_r	0.13

图6 路径跟踪结果对比

Fig.6 Comparison of path tracking results

图7 状态量跟踪控制效果对比

Fig.7 Comparison of state quantity tracking control effects

图8 状态量误差变化对比

Fig.8 Comparison of state quantity error change

图9 控制量变化效果对比

Fig.9 Comparison of control quantity change effect

图10 跟踪距离误差对比

Fig.10 Comparison of tracking distance error

参考文献 23

[1]	张奇峰, 张运修, 张艾群. 深海小型爬行机器人研究现状[J]. 机器人, 2019, 41(2):250-264. doi: 10.13973/j.cnki.robot.180231
	ZHANG Q F, ZHANG Y X, ZHANG A Q. The research status of small deep-sea crawling robots[J]. Robot, 2019, 41(2): 250-264. (in Chinese)
[2]	孙洪鸣, 郭威, 周悦, 等. 全海深着陆车机构设计及其潜浮运动性能分析[J]. 机器人, 2020, 42(2):207-214. doi: 10.13973/j.cnki.robot.190375
	SUN H M, GUO W, ZHOU Y, et al. Mechanism design of full-sea deep landing vehicle and analysis of its submersible and snorkeling performance[J]. Robotics, 2020, 42(2): 207-214. (in Chinese)
[3]	白国星, 孟宇, 刘立, 等. 基于可变预测时域及速度的车辆路径跟踪控制[J]. 中国机械工程, 2020, 31(11): 1277-1284.
	BAI G X, MENG Y, LIU L, et al. Vehicle path tracking control based on variable prediction time domain and speed[J]. China Mechanical Engineering, 2020, 31(11): 1277-1284. (in Chinese)
[4]	BRUSCHETTA M, MARAN F, BEGHI A. A fast implementation of MPC-based motion cueing algorithms for mid-size road vehicle motion simulators[J]. Vehicle System Dynamics, 2017, 55(6) : 802-826. doi: 10.1080/00423114.2017.1280173 URL
[5]	REN Y, ZHENG L, KHAJEPOUR A. Integrated model predictive and torque vectoring control for path tracking of 4-wheel-driven autonomous vehicles[J]. IET Intelligent Transport Systems, 2019, 13(1): 98-107. doi: 10.1049/iet-its.2018.5095 URL
[6]	张志达, 郑玲, 张紫微, 等. 基于自适应模型预测的智能汽车横向轨迹跟踪控制[J]. 中国公路学报, 2022, 35(7):305-316. doi: 10.19721/j.cnki.1001-7372.2022.07.026
	ZHANG Z D, ZHENG L, ZHANG Z W, et al. Lateral trajectory tracking control of intelligent vehicles based on adaptive model prediction[J]. Chinese Journal of Highways, 2022, 35(7):305-316. (in Chinese)
[7]	BENJAMIN G, LUTZ G, MORITZ W. Lateral vehicle trajectory optimization using constrained linear time-varying MPC[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18 (6): 1586-1595.
[8]	姚绪梁, 王晓伟. 基于MPC导引律的AUV路径跟踪和避障控制[J]. 北京航空航天大学学报, 2020, 46(6):1053-1062.
	YAO X L, WANG X W. AUV path tracking and obstacle avoidance control based on MPC guidance law[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(6): 1053-1062. (in Chinese)
[9]	徐兴, 汤赵, 王峰, 等. 基于变权重系数的分布式驱动无人车轨迹跟踪[J]. 中国公路学报, 2019, 32(12): 36-45. doi: 10.19721/j.cnki.1001-7372.2019.12.004
	XU X, TANG Z, WANG F, et al. Distributed driving unmanned vehicle trajectory tracking based on variable weight coefficients[J]. China Journal of Highway and Transport, 2019, 32(12): 36-45. (in Chinese)
[10]	郭威, 孙洪鸣, 徐高飞, 等. 全海深着陆车着底冲击特性与集中应力结构改进[J]. 中国机械工程, 2021, 32 (7):867-874.
	GUO W, SUN H M, XU G F, et al. Bottom impact characteristics and structural improvement of concentrated stress for full sea depth landing vehicle[J]. China Mechanical Engineering, 2021, 32(7):867-874. (in Chinese)
[11]	李睿, 项昌乐, 王超, 等. 自动驾驶履带车辆鲁棒自适应轨迹跟踪控制方法[J]. 兵工学报, 2021, 42(6):1128-1137. doi: 10.3969/j.issn.1000-1093.2021.06.002
	LI R, XIANG C L, WANG C, et al. Robust adaptive trajectory tracking control method for autonomous tracked vehicles[J]. Acta Armamentarii, 2021, 42(6): 1128-1137. (in Chinese)
[12]	樊娟娟. 基于预测控制的并联式混合动力汽车能量管理策略研究[D]. 济南: 山东大学, 2012.
	FAN J J. Research on energy management strategy of parallel hybrid vehicles based on predictive control[D]. Jinan: Shandong University, 2012. (in Chinese)
[13]	魏立新, 张钰锟, 孙浩, 等. 基于改进蚁群和DWA算法的机器人动态路径规划[J]. 控制与决策, 2022, 37(9):2211-2216.
	WEI L X, ZHANG Y K, SUN H, et al. Robot dynamic path planning based on improved ant colony and DWA algorithm[J]. Control and Decision, 2022, 37(9):2211-2216. (in Chinese)
[14]	DORIGO M. Optimization, learning and natural algorithms[D]. Milano,Italy: Politecnico di Milano, 1992.
[15]	夏光, 李嘉诚, 唐希雯, 等. 平衡重式叉车防侧翻模型预测控制研究[J]. 中国机械工程, 2021, 32(8): 987-996.
	XIA G, LI J C, TANG X W, et al. Research on anti-rollover model predictive control of counterweight forklift[J]. China Mechanical Engineering, 2021, 32(8): 987-996. (in Chinese)
[16]	胡家铭, 胡宇辉, 陈慧岩, 等. 基于模型预测控制的无人驾驶履带车辆轨迹跟踪方法研究[J]. 兵工学报, 2019, 40(3):456-463. doi: 10.3969/j.issn.1000-1093.2019.03.002
	HU J M, HU Y H, CHEN H Y, et al. Research on trajectory tracking method of unmanned tracked vehicle based on model predictive control[J]. Acta Armamentarii, 2019, 40(3): 456-463. (in Chinese)
[17]	蔡英凤, 李健, 孙晓强, 等. 智能汽车路径跟踪混合控制策略研究[J]. 中国机械工程, 2020, 31(3): 289-298.
	CAI Y F, LI J, SUN X Q, et al. Research on hybrid control strategy of intelligent vehicle path following[J]. China Mechanical Engineering, 2020, 31(3): 289-298. (in Chinese)
[18]	孙涛, 夏维, 李道飞. 基于模型预测控制的协同式自适应巡航控制系统[J]. 中国机械工程, 2017, 28(4): 486-491.
	SUN T, XIA W, LI D F. Cooperative adaptive cruise control system based on model predictive control[J]. China Mechanical Engineering, 2017, 28(4): 486-491. (in Chinese)
[19]	张亮修, 吴光强, 郭晓晓. 自主车辆线性时变模型预测路径跟踪控制[J]. 同济大学学报(自然科学版), 2016, 44(10):1595-1603.
	ZHANG L X, WU G Q, GUO X X. Autonomous vehicle linear time-varying model predictivePath following control[J]. Journal of Tongji University(Natural Science Edition), 2016, 44(10): 1595-1603. (in Chinese)
[20]	王浩杰. 自动泊车系统路径规划与跟踪算法研究[D]. 大连: 大连理工大学, 2021.
	WANG H J. Research on path planning and tracking algorithm of automatic parking system[D]. Dalian: Dalian University of Technology, 2021. (in Chinese)
[21]	韩庆珏, 刘少军. 深海履带车的路径跟踪控制算法[J]. 中南大学学报(自然科学版), 2015, 46(2):472-478.
	HAN Q J, LIU S J. Path following control algorithm for deep sea tracked vehicles[J]. Journal of Central South University (Natural Science Edition), 2015, 46(2): 472-478. (in Chinese)
[22]	张玮, 马焱, 赵捍东, 等. 基于改进烟花-蚁群混合算法的智能移动体避障路径规划[J]. 控制与决策, 2019, 34(2):335-343.
	ZHANG W, MA Y, ZHAO H D, et al. Path Planning of intelligent moving objects to avoid obstacles based on improved firework-ant colony hybrid algorithm[J]. Control and Decision, 2019, 34(2): 335-343. (in Chinese)
[23]	ELBANHAWI M, SIMIC M, JAZAR R. Randomized bidirectional B-spline parameterization motion planning[J]. IEEE Transactions on Intelligent Transporation Systems, 2016, 17(2): 406-419.