深海起重机系统的实时轨迹规划方法

王岳; 孙宁; 吴易鸣; 梁潇; 陈鹤; 方勇纯

doi:10.16383/j.aas.c200262

深海起重机系统的实时轨迹规划方法

doi: 10.16383/j.aas.c200262

王岳^1,,
孙宁^1,,
吴易鸣^1,,
梁潇^1,,
陈鹤^2,,
方勇纯^1,

1.
南开大学人工智能学院机器人与信息自动化研究所, 天津市智能机器人技术重点实验室天津 300350
2.
河北工业大学人工智能与数据科学学院天津 300350

基金项目: 国家自然科学基金(U1706228, 61873134), 国家重点研发计划(2018YFB1309000)资助

详细信息

作者简介:
王岳：南开大学机器人与信息自动化研究所硕士研究生. 主要研究方向为欠驱动控制系统. E-mail: yuew@mail.nankai.edu.cn

孙宁：南开大学机器人与信息自动化研究所教授. 主要研究方向为欠驱动机器人(包括各类吊车), 气动人工肌肉等系统的控制及应用. 本文通信作者. E-mail: sunn@nankai.edu.cn

吴易鸣：南开大学机器人与信息自动化研究所博士研究生. 主要研究方向为欠驱动控制系统. E-mail: ymwu@mail.nankai.edu.cn

梁潇：南开大学机器人与信息自动化研究所讲师. 主要研究方向为无人机系统的运动规划和非线性控制. E-mail: liangx@nankai.edu.cn

陈鹤：河北工业大学人工智能与数据科学学院讲师. 主要研究方向为机电一体化控制, 桥式起重机和轮式移动机器人. E-mail: chenh@hebut.edu.cn

方勇纯：南开大学机器人与信息自动化研究所教授. 主要研究方向为非线性控制, 视觉伺服, 欠驱动系统控制和基于原子力显微镜的纳米系统. E-mail: fangyc@nankai.edu.cn

计量
- 文章访问数: 1656
- HTML全文浏览量: 335
- PDF下载量: 328
- 被引次数: 0
出版历程
- 收稿日期: 2020-04-29
- 网络出版日期: 2021-10-27
- 刊出日期: 2021-12-23

Real-time Motion Planning of Deep Sea-oriented Flexible Crane Systems

WANG Yue^1
,,
SUN Ning^1
,,
WU Yi-Ming^1
,,
LIANG Xiao^1
,,
CHEN He^2
,,
FANG Yong-Chun^1
,

1.
Institute of Robotics and Automatic Information Systems (IRAIS), College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics (tjKLIR), Nankai University, Tianjin 300350
2.
College of Artificial Intelligence, Hebei University of Technology, Tianjin 300350

Funds: Supported by National Natural Science Foundation of China (U1706228, 61873134) and National Key Research and Development Program of China (2018YFB1309000)

More Information

Author Bio:
WANG Yue　Master student at the Institute of Robotics and Automatic Information Systems, Nankai University. Her research interest covers control of underactuated systems

SUN Ning　Professor at the Institute of Robotics and Automatic Information Systems, Nankai University. His research interest covers control of underactuated robotic systems (including cranes), and pneumatic artificial muscle systems. Corresponding author of this paper

WU Yi-Ming　Ph.D. candidate at the Institute of Robotics and Automatic Information Systems, Nankai University. Her research interest covers control of underactuated systems

LIANG Xiao　Lecturer at the Institute of Robotics and Automatic Information System, Nankai University. His research interest covers motion planning and nonlinear control of unmanned aerial vehicle systems

CHEN He　Lecturer at the School of Artificial Intelligence, Hebei University of Technology. His research interest covers control of mechatronics, overhead cranes, and wheeled mobile robots

FANG Yong-Chun　Professor at the Institute of Robotics and Automatic Information Systems, Nankai University. His research interest covers nonlinear control, visual servoing, control of underactuated systems, and atomic force microscope (AFM)-based nano-systems

摘要

摘要: 近年来, 随着海洋资源的不断开发与海洋工程的全球化推进, 深海起重机得到了广泛应用, 其控制问题也引起研究人员的极大关注. 在深海作业环境中, 由于吊运过程受到水流作用力的影响, 负载摆动幅度增大, 系统状态量间非线性耦合关系增强, 使系统控制难度加大. 为此, 本文针对深海起重机系统提出了一种实时轨迹规划方法. 具体而言, 通过分析系统动力学特性和状态变量之间复杂的耦合关系, 提出了一种实时规划轨迹的方法, 并从理论上证明了该方法可在使台车准确快速到达指定位置的同时, 有效抑制负载摆动. 最后, 一系列仿真结果证明了所提方法的良好性能.
- 深海起重机 /
- 欠驱动系统 /
- 实时轨迹规划 /
- 非线性耦合 /
- 摆动抑制
Abstract: In recent years, with the continuous exploitation of marine resources and the global development of marine engineering, deep sea-oriented crane systems have been widely used, and their control problems have also attracted great attention. Under the deep sea working environment, since the transportation process is influenced by the hydrodynamic force, the payload＇s vibration amplitude increases, and the nonlinear coupling relationship between state variables becomes stronger, making the control problem more challenging. To this end, this paper proposes a real-time trajectory planning method for the deep sea crane system. Specifically, by analyzing the dynamics and the complex coupling relationship between state variables, a real-time trajectory planning method is proposed, and it is theoretically proved that this method can achieve fast and accurate trolley positioning and effective cargo swing suppression. Finally, numerical simulation results indicate the satisfactory performance of the designed method.
- Deep sea-oriented crane /
- underactuated system /
- real-time trajectory planning /
- nonlinear coupling /
- anti-vibration

HTML全文

图 1 深海柔性起重机系统

Fig. 1 The flexible deep sea crane system

下载: 全尺寸图片幻灯片

图 2 实时轨迹规划示意图

Fig. 2 Schematic diagram of real-time trajectory planning

下载: 全尺寸图片幻灯片

图 3 参考位移、速度、加速度轨迹

Fig. 3 The reference displacement, velocity, and acceleration trajectories

下载: 全尺寸图片幻灯片

图 4 仿真对比结果

Fig. 4 Comparison results

下载: 全尺寸图片幻灯片

图 5 负载摆动三维仿真图

Fig. 5 Three-dimensional diagram of the vibration $ w(y,t) $

下载: 全尺寸图片幻灯片

图 6 含初始扰动的仿真对比结果

Fig. 6 Simulation results with initial disturbance

下载: 全尺寸图片幻灯片

图 7 含中间扰动的仿真对比结果

Fig. 7 Simulation results with intermediate disturbance

下载: 全尺寸图片幻灯片

图 8 验证所提方法实时性的仿真结果

Fig. 8 Simulation results to verify the real-time performance of the proposed method

下载: 全尺寸图片幻灯片

图 9 与输入整形方法的仿真对比结果

Fig. 9 Simulation results compared with input shaping method

下载: 全尺寸图片幻灯片

表 1 系统参数

Table 1 System parameters

参数	物理意义	单位
$m_r$	负载质量	kg
$m_t$	台车质量	kg
$d$	负载截面直径	m
$l$	负载长度	m
$E$	杨氏模量	GPa
$c$	粘性阻尼系数	N·s/m
$\rho_w$	水密度	kg/m³
$C_a$	附加质量系数	—
$C_d$	阻力系数	—

下载: 导出CSV

表 2 系统参数仿真值

Table 2 Simulation values of system parameters

参数	取值	单位
$m_r$	0.37	kg
$m_t$	38.0	kg
$d$	0.008	m
$l$	1	m
$E$	248.3	GPa
$c$	0.6	N·s/m
$\rho_w$	1000	kg/m³
$C_a$	0.93	—
$C_d$	1.28	—

下载: 导出CSV

表 3 无外部扰动时量化指标对比结果

Table 3 Comparison results of quantitative indices without external disturbance

	正向摆动幅度 (m)	反向摆动幅度 (m)	进入相对稳态时间 (s)
定位参考轨迹	–0.278	0.032	3.7
本文规划轨迹	–0.142	0.024	3.1

下载: 导出CSV

表 4 与输入整形方法的量化指标对比结果

Table 4 Comparison results of quantitative indices with input shaping method

	正向摆动幅度 (m)	反向摆动幅度 (m)	进入相对稳态时间 (s)
输入整形方法	–0.183	0.052	4.3
本文规划方法	–0.142	0.024	3.1

下载: 导出CSV

参考文献(33)

[1]	刘殿通, 易建强, 谭民. 一类非线性系统的自适应滑模模糊控制. 自动化学报, 2004, 30(1): 144−150 Liu Dian-Tong, Yi Jian-Qiang, Tan Min. Adaptive sliding mode fuzzy control for a class of nonlinear systems. Acta Automatica Sinica, 2004, 30(1): 144−150
[2]	武宪青, 何熊熊. 欠驱动RTAC系统的自适应耦合控制器设计. 自动化学报, 2015, 41(5): 1047−1052 Wu Xian-Qing, He Xiong-Xiong. Adaptive coupling controller design for underactuated RTAC systems. Acta Automatica Sinica, 2015, 41(5): 1047−1052
[3]	魏萃, 柴天佑, 贾瑶, 王良勇. 补偿信号法驱动的Pendubot自适应平衡控制. 自动化学报, 2019, 45(6): 1146−1156 Wei Cui, Chai Tian-You, Jia Yao, Wang Liang-Yong. Compensation signal driven adaptive balance control of the pendubot. Acta Automatica Sinica, 2019, 45(6): 1146−1156
[4]	He W, Meng T T, He X Y, Ge S S. Unified iterative learning control for flexible structures with input constraints. Automatica, 2018, 96: 326−336 doi: 10.1016/j.automatica.2018.06.051
[5]	赖旭芝, 张镇, 吴敏. 欠驱动三连杆机械臂能量解耦控制策略. 自动化学报, 2011, 37(1): 67−73 doi: 10.3724/SP.J.1004.2011.00067 Lai Xu-Zhi, Zhang Zhen, Wu Min. Energy decoupling control strategy for three-link underactuated manipulator. Acta Automatica Sinica, 2011, 37(1): 67−73 doi: 10.3724/SP.J.1004.2011.00067
[6]	Sun W, Lin J W, Su S F, Wang N, Er M. Reduced adaptive fuzzy decoupling control for lower limb exoskeleton. IEEE Transactions on Cybernetics, 2020, DOI: 10.1109/TCYB.2020.2972582
[7]	梁潇, 方勇纯, 孙宁. 平面四旋翼无人飞行器运送系统的轨迹规划与跟踪控制器设计. 控制理论与应用, 2015, 32(11): 1430−1438 Liang Xiao, Fang Yong-Chun, Sun Ning. Trajectory planning and tracking controller design for a planar quadrotor unmanned aerial vehicle transportation system. Control Theory and Applications, 2015, 32(11): 1430−1438
[8]	武宪青, 徐可心, 张益波. 基于输出反馈的欠驱动TORA系统的有界输入控制. 自动化学报, 2020, 46(1): 200−204 Wu Xian-Qing, Xu Ke-Xin, Zhang Yi-Bo. Output-based feedback control of underactuated TORA systems by bounded inputs. Acta Automatica Sinica, 2020, 46(1): 200−204
[9]	胡洲, 王志胜, 甄子洋. 带输入饱和的欠驱动吊车非线性信息融合控制. 自动化学报, 2014, 40(7): 1522−1527 Hu Zhou, Wang Zhi-Sheng, Zhen Zi-Yang. Nonlinear information fusion control for underactuated cranes with input saturation. Acta Automatica Sinica, 2014, 40(7): 1522−1527
[10]	Tuan L A, Lee S G. Modeling and advanced sliding mode controls of crawler cranes considering wire rope elasticity and complicated operations. Mechanical Systems and Signal Processing, 2018, 103: 250−263 doi: 10.1016/j.ymssp.2017.09.045
[11]	孙宁, 方勇纯, 王鹏程, 张雪波. 欠驱动三维桥式吊车系统自适应跟踪控制器设计. 自动化学报, 2010, 36(9): 1287−1294 doi: 10.3724/SP.J.1004.2010.01287 Sun Ning, Fang Yong-Chun, Wang Peng-Cheng, Zhang Xue-Bo. Adaptive trajectory tracking control of underactuated 3-dimensional overhead crane systems. Acta Automatica Sinica, 2010, 36(9): 1287−1294 doi: 10.3724/SP.J.1004.2010.01287
[12]	Rigatos G, Siano P, Abbaszadeh M. Nonlinear H-infinity control for 4-DOF underactuated overhead cranes. Transactions of the Institute of Measurement and Control, 2018, 40(7): 2364−2377 doi: 10.1177/0142331217703702
[13]	孙宁, 方勇纯, 陈鹤. 欠驱动桥式吊车消摆跟踪控制. 控制理论与应用, 2015, 32(3): 326−333 doi: 10.7641/CTA.2015.31139 Sun Ning, Fang Yong-Chun, Chen He. Antiswing tracking control for underactuated bridge cranes. Control Theory and Applications, 2015, 32(3): 326−333 doi: 10.7641/CTA.2015.31139
[14]	Peng J H, Huang J, Singhose W. Payload twisting dynamics and oscillation suppression of tower cranes during slewing motions. Nonlinear Dynamics, 2019, 98(2): 1041−1048 doi: 10.1007/s11071-019-05247-4
[15]	Zhang M H, Zhang Y F, Ouyang H M, Ma C H, Cheng X G. Adaptive integral sliding mode control with payload sway reduction for 4-DOF tower crane systems. Nonlinear Dynamics, 2020, 99: 2727−2741 doi: 10.1007/s11071-020-05471-3
[16]	Yang T, Sun N, Chen H, Fang Y C. Neural network-based adaptive antiswing control of an underactuated ship-mounted crane with roll motions and input dead zones. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(3): 901−914 doi: 10.1109/TNNLS.2019.2910580
[17]	欧阳慧珉, 王健, 张广明, 梅磊, 邓歆. 基于新型滑模算法的双摆旋转起重机消摆跟踪控制. 自动化学报, 2019, 45(7): 1344−1353 Ouyang Hui-Min, Wang Jian, Zhang Guang-Ming, Mei Lei, Deng Xin. Tracking and anti-sway control for double-pendulum rotary cranes using novel sliding mode algorithm. Acta Automatica Sinica, 2019, 45(7): 1344−1353
[18]	Qian D W, Tong S W, Lee S G. Fuzzy-logic-based control of payloads subjected to double-pendulum motion in overhead cranes. Automation in Construction, 2016, 65(C): 133−143
[19]	Fang Y, Hu J, Liu W H, Shao Q Q, Qi J, Peng Y H. Smooth and time-optimal S-curve trajectory planning for automated robots and machines. Mechanism and Machine Theory, 2019, 137: 127−153 doi: 10.1016/j.mechmachtheory.2019.03.019
[20]	Wang H, Heng W, Huang J H, Zhao B, Quan L. Smooth point-to-point trajectory planning for industrial robots with kinematical constraints based on high-order polynomial curve. Mechanism and Machine Theory, 2019, 139: 284−293 doi: 10.1016/j.mechmachtheory.2019.05.002
[21]	Yin Z, He W, Yang C G. Tracking control of a marine surface vessel with full-state constraints. International Journal of Systems Science, 2017, 48(3): 535−546 doi: 10.1080/00207721.2016.1193255
[22]	于欣波, 贺威, 薛程谦, 孙永坤, 孙长银. 基于扰动观测器的机器人自适应神经网络跟踪控制研究. 自动化学报, 2019, 45(7): 1307−1324 Yu Xin-Bo, He Wei, Xue Cheng-Qian, Sun Yong-Kun, Sun Chang-Yin. Disturbance observer-based adaptive neural network tracking control for robots. Acta Automatica Sinica, 2019, 45(7): 1307−1324
[23]	王鹏程, 方勇纯, 江紫亚. 一种直接基于摆角约束的欠驱动桥式吊车轨迹规划方法. 自动化学报, 2014, 40(11): 2414−2419 doi: 10.1016/S1874-1029(14)60397-9 Wang Peng-Cheng, Fang Yong-Chun, Jiang Zi-Ya. A direct swing constraint-based trajectory planning method for underactuated overhead cranes. Acta Automatica Sinica, 2014, 40(11): 2414−2419 doi: 10.1016/S1874-1029(14)60397-9
[24]	孙宁, 方勇纯, 钱彧哲. 带有状态约束的双摆效应吊车轨迹规划. 控制理论与应用, 2014, 31(7): 974−980 doi: 10.7641/CTA.2014.40054 Sun Ning, Fang Yong-Chun, Qian Yu-Zhe. Motion planning for cranes with double pendulum effects subject to state constraints. Control Theory and Applications, 2014, 31(7): 974−980 doi: 10.7641/CTA.2014.40054
[25]	陈鹤, 方勇纯, 孙宁, 钱彧哲. 基于伪谱法的双摆吊车时间最优消摆轨迹规划策略. 自动化学报, 2016, 42(1): 153−160 Chen He, Fang Yong-Chun, Sun Ning, Qian Yu-Zhe. Pseudospectral method based time optimal anti-swing trajectory planning for double pendulum crane systems. Acta Automatica Sinica, 2016, 42(1): 153−160
[26]	Sun N, Wu Y M, Chen H, Fang Y C. An energy-optimal solution for transportation control of cranes with double pendulum dynamics: Design and experiments. Mechanical Systems and Signal Processing, 2018, 102: 87−101 doi: 10.1016/j.ymssp.2017.09.027
[27]	Boscariol P, Richiedei D. Robust point-to-point trajectory planning for nonlinear underactuated systems: Theory and experimental assessment. Robotics and Computer-Integrated Manufacturing, 2018, 50: 256−265 doi: 10.1016/j.rcim.2017.10.001
[28]	Zhang M H, Ma X, Rong X W, Tian X C, Li Y B. Error tracking control for underactuated overhead cranes against arbitrary initial payload swing angles. Mechanical Systems and Signal Processing, 2017, 84: 268−285 doi: 10.1016/j.ymssp.2016.07.028
[29]	Liu Z Q, Yang T, Sun N, Fang Y C. An antiswing trajectory planning method with state constraints for 4-DOF tower cranes: Design and experiments. IEEE Access, 2019, 7: 62142−62151 doi: 10.1109/ACCESS.2019.2915999
[30]	Otto S, Seifried R. Real-time trajectory control of an overhead crane using servo-constraints. Multibody System Dynamics, 2018, 42(1): 1−17 doi: 10.1007/s11044-017-9569-4
[31]	Li F, Zhang C H, Sun B. A minimum-time motion online planning method for underactuated overhead crane systems. IEEE Access, 2019, 7: 54586−54594 doi: 10.1109/ACCESS.2019.2912460
[32]	Shah U H, Hong K S, Choi S H. Open-loop vibration control of an underwater system: Application to refueling machine. IEEE/ASME Transactions on Mechatronics, 2017, 22(4): 1622−1632 doi: 10.1109/TMECH.2017.2706304
[33]	Sun N, Fang Y C, Chen H, Fu Y, Lu B. Nonlinear stabilizing control for ship-mounted cranes with ship roll and heave movements: design, analysis, and experiments. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 48(10): 1781−1793