基于航向补偿的水下滑翔机路径跟踪控制方法

桑宏强; 于佩元; 孙秀军

doi:10.11993/j.issn.2096-3920.2019.05.009

基于航向补偿的水下滑翔机路径跟踪控制方法

doi: 10.11993/j.issn.2096-3920.2019.05.009

桑宏强^1,2,
于佩元¹,
孙秀军^3,4*,

1.
天津工业大学机械工程学院, 天津, 300387
2.
天津市现代机电装备技术重点实验室, 天津, 300387
3.
中国海洋大学物理海洋教育部重点实验室, 山东青岛, 266100
4.
青岛海洋科学与技术国家实验室海洋动力过程与气候功能实验室, 山东青岛, 266237

基金项目: 国家重点研发计划重点专项(2017YFC0305902); 青岛海洋科学与技术国家实验室“问海计划”项目(2017WHZ ZB0101); 天津市自然科学基金重点项目(18JCZDJC40100); 天津市高等学校创新团队培养计划(TD13-5037).

详细信息

通讯作者:
孙秀军(1981-), 男, 博士, 教授, 主要研究方向为海洋移动观测平台技术.

中图分类号: U674.941; TP242
计量
- 文章访问数: 506
- HTML全文浏览量: 21
- PDF下载量: 216
- 被引次数: 0
出版历程
- 收稿日期: 2019-10-30
- 修回日期: 2019-11-15
- 刊出日期: 2019-10-31

Path Tracking Control Method of Underwater Glider Based on Heading Compensation

1.
School of Mechanical Engineering, Tianjin Polytechnic University, Tianjin 300387, China
2.
Tianjin Key Laboratory of Advanced Mechatronic Equipment Technology, Tianjin 300387, China
3.
Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China
4.
Laboratory of Marine Dynamics and Climate Function, Pilot National Laboratory for Marine Science and TechnologyQingdao, Qingdao 266237, China

摘要

摘要: 针对水下滑翔机在内部模型非线性和外界环境干扰下的水平路径跟踪控制问题, 文中以水下滑翔机Petrel-II 200动力学模型作为闭环控制系统仿真平台, 提出一种包含积分视向导航(ILOS)、基于航向补偿(HC)的滑模控制(SMC)及粒子滤波(PF)的路径跟踪控制方法。通过ILOS算法实时更新水下滑翔机的期望航向角, 基于航向补偿的滑模控制算法用于消除航向控制中的稳态误差, 在反馈回路引入粒子滤波器削弱过程噪声及测量噪声的干扰, 给出完整的路径跟踪控制模型, 并从不同方面进行了仿真验证。由数值仿真结果可知, 与传统的比例-积分-微分(PID)控制相比, 文中所提方法在方波航向跟踪中航向平均误差减小80.14%, 均方根误差减小4.1%; 正弦航向中最大航向误差减小40.9%, 标准差减小3.6%, 同时避免了舵角输出的高频震荡, 有效地降低了能耗。在滤波仿真中, 粒子滤波可以滤除80%的固定航向噪声与90%随机航向噪声。在路径跟踪仿真中, 所提方法能有效地对期望路径进行跟踪。上述仿真结果验证了所设计路径跟踪控制方法的有效性。
- 水下滑翔机 /
- 积分视向导航算法 /
- 粒子滤波 /
- 滑模控制 /
- 航向补偿
Abstract: This paper focuses on the problem of horizontal path tracking control for underwater glider under internal model nonlinearity and external environment disturbances. A dynamic model of underwater glider Petrel-II 200 is established as the simulation platform of closed-loop control system, and a path tracking control method including integral light-of-sight(ILOS), sliding mode control(SMC) with heading compensation(HC), and particle filter(PF) is proposed. The desired heading angle of the underwater glider is updated in real time by the ILOS algorithm. The SMC algorithm based on HC is used to eliminate the steady state error in the heading control. The PF is introduced into the feedback loop to reduce the interference of process noise and measurement noise. The complete path tracking control model is verified by numerical simulation. According to the numerical simulation results, the proposed method reduces the mean heading error and the root mean square error in square wave heading tracking by 80.14% and 4.1%, respectively, compared with the traditional proportional-integral-derivative(PID) control. Also, the maximum heading error and the standard deviation in sinusoidal heading are reduced by 40.9% and 3.6%, respectively. The high frequency oscillation of the rudder angle output is also avoided, which effectively reduces the energy consumption. In the filtering simulation, PF can filter out 80% of fixed heading noise and 90% of random heading noise, and in the path tracking simulation, the proposed method can effectively track the desired path. These numerical simulation results verify the effectiveness of the proposed path tracking control method.
- underwater glider /
- integral light-of-sight(ILOS) /
- particle filter(PF) /
- sliding mode control(SMC) /
- heading compensation(HC)

HTML全文

参考文献(17)

[1]	Wang Y, Zhang Y, Zhang M, et al. Design and Flight Performance of Hybrid Underwater Glider with Controllable Wings[J]. International Journal of Advanced Robotic Systems, 2017, 14(3): 1-12.
[2]	Bessa W M, Dutra M S, Kreuzer E. Depth Control of Remotely Operated Underwater Vehicles Using an Adaptive Fuzzy Sliding Mode Controller[J]. Robotics & Autonomous Systems, 2008, 56(8): 670-677.
[3]	Xiang X, Yu C, Zhang Q, et al. Robust Fuzzy 3D Path Following for Autonomous Underwater Vehicle Subject to Uncertainties[J]. Computers and Operations Research, 2017, 84: 165-177.
[4]	Sahu B K, Subudhi B. Potential Function-based Path-following Control of an Autonomous Underwater Vehicle in an Obstaclerich Environment[J]. Transactions of the Institute of Measurement and Control, 2017, 39(8): 1236-1252.
[5]	Liang X, Qu X, Hou Y, et al. Three-dimensional Path Following Control of Underactuated Autonomous Under-water Vehicle Based on Damping Backstepping[J]. International Journal of Advanced Robotic Systems, 2017, 14(4): 1-9.
[6]	Peng Z, Wang J, Wang D. Distributed Containment Maneuvering of Multiple Marine Vessels via Neurodynamics-Based Output Feedback[J]. IEEE Transactions on Industrial Electronics, 2017, 64(5): 3831-3839.
[7]	Xu D, Shi Y, Ji Z. Model Free Adaptive Discrete-time Integral Sliding Mode Constrained Control for Autonomous 4WMV Parking Systems[J]. IEEE Transactions on Indus- trial Electronics, 2017, 65(1): 834-843.
[8]	Martinez A, Hernandez L, Sahli H, et al. Model-aided Navigation with Sea Current Estimation for an Autonomous Underwater Vehicle[J]. International Journal of Advanced Robotic Systems, 2015, 12(7): 1-14.
[9]	Fossen T I. Handbook of Marine Craft Hydrodynamics and Motion Control[M]. Chichester: John Wiley & Sons, 2011.
[10]	孙秀军. 混合驱动水下滑翔器动力学建模及运动控制研究[D]. 天津: 天津大学, 2011.
[11]	Sang H, Zhou Y, Sun X, et al. Heading Tracking Control with an Adaptive Hybrid Control for Under Actuated Underwater Glider[J]. Isa Transactions, 2018, 80: 554-563.
[12]	Lekkas A, Fossen T I. Line-of-Sight Guidance for Path Following of Marine Vehicles[M]. Saarbrcken: Lambert Academic Publishing, 2013.
[13]	Borhaug E, Pavlov A, Pettersen K Y. Integral LOS Control for Path Following of Underactuated Marine Surface Vessels in the Presence of Constant Ocean Currents[C]//IEEE Conference on Decision & Control. Cancun, Mexico: IEEE, 2008: 4984-4991.
[14]	Mahapatra S, Subudhi B. Design of a Steering Control Law for an Autonomous Underwater Vehicle Using Nonlinear State Feedback Technique[J]. Nonlinear Dynamics, 2017, 90(2): 837-854.
[15]	Miao J, Wang S, Zhao Z, et al. Spatial Curvilinear Path Following Control of Underactuated AUV with Multiple Uncertainties[J]. Isa Transactions, 2017, 67: 107-130.
[16]	Shojaei K, Dolatshahi M. Line-of-sight Target Tracking Control of Underactuated Autonomous Underwater Ve-hicles[J]. Ocean Engineering, 2017, 133: 244-252.
[17]	Cui R, Zhang X, Cui D. Adaptive Sliding-mode Attitude Control for Autonomous Underwater Vehicles with Input Nonlinearities[J]. Ocean Engineering, 2016, 123: 45-54.