视觉AGV的运动控制策略研究

刘忠强; 孟文俊; 杨正茂

doi:10.13433/j.cnki.1003-8728.2015.0720

视觉AGV的运动控制策略研究

doi: 10.13433/j.cnki.1003-8728.2015.0720

1.
太原科技大学机械工程学院, 太原 030024;
2.
四川工程职业技术学院车辆工程系, 德阳 618000

基金项目:

国家自然科学基金项目（51075289）与山西省国际科技合作计划项目（2010081039）资助

详细信息

作者简介:
刘忠强（1987-），助教，硕士研究生，研究方向为现代物料搬运技术与设备、智能车辆等，liu_zhongqiang@163.com

通讯作者:
孟文俊，教授，博士生导师，tyustmwj@126.com

计量
- 文章访问数: 182
- HTML全文浏览量: 35
- PDF下载量: 12
- 被引次数: 0
出版历程
- 收稿日期: 2013-05-23
- 刊出日期: 2015-07-05

Researching Motion Control Strategy for Visual AGV

1.
School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China;
2.
Department of Vehicle Engineering, Sichuan Engineering Technical College, Deyang, 618000, China

摘要

摘要: 为了解决视觉AGV路径跟踪存在较严重的抖振问题，提出了一种免疫粒子群算法，通过其对滑模控制律参数进行优化，以达到更加精确、稳定地运动控制要求。介绍了基本粒子群算法，并引入免疫机理对其进行改进；分析了视觉AGV运动学模型，并设计了离散滑模控制律；在MATLAB软件中分别对直线、圆周两种轨迹进行了仿真。仿真试验证明：优化后的滑模控制器性能得到明显地提高，最大侧向偏差可以控制在0.038 m以内。
- AGV /
- 滑模控制 /
- 粒子群算法 /
- 路径跟踪
Abstract: In order to solve the severe buffeting problems of path tracking for visual automated guided vehicle (AGV), an immune particle swarm optimization method is presented to optimize the parameters of sliding mode control law to satisfy achieve more accurate and stable control requirements. Firstly, this paper describes the basic particle swarm optimization and improves it with the introduction of immune mechanism. Then the kinematics model of the visual AGV is analyzed and discrete sliding mode control law is designed. Finally, the simulation of straight-line path and circular path is made with the MATLAB software. The simulation results show that the performance of optimized sliding mode controller is significantly improved.
- automated guided vehicle (AGV) /
- computer simulation /
- controllers /
- design

HTML全文

参考文献(10)

[1]	叶锦华,李迪,叶峰,等.不确定非完整AGV的自适应反演滑模控制[J].华南理工大学学报,2011,39(12):32-37 Ye J H, Li D, Ye F, et al. Adaptive backstepping sliding mode control of uncertain nonholonomic AGV[J]. Journal of South China University of Technology, 2011,39(12):32-37 (in Chinese)
[2]	Keighobadi J, Mohamadi Y. Fuzzy sliding mode control of a non-holonomic wheeled mobile robot[C]// Proceedings of the International Multi-Conference of Engineers and Computer Scientists, Hong Kong, 2011
[3]	Chen C Y, Li T H S, Yeh Y C. EP-based kinematic control and adaptive fuzzy sliding-mode dynamic control for wheeled mobile robots[J]. Information Sciences, 2009,179(1-2):180-195
[4]	王宗义,李艳东,朱玲.非完整移动机器人的双自适应神经滑模控制[J].机械工程学报,2010,46(23):16-22 Wang Z Y, Li Y D, Zhu L. Dual adaptive neural sliding mode control of nonholonomic mobile robot[J]. Journal of Mechanical Engineering, 2010,46(23):16-22 (in Chinese)
[5]	焦俊,陈无畏,王继先,等.基于遗传算法的自动引导车自适应变结构控制[J].农业机械学报,2008,39(7):114-118 Jiao J, Chen W W, Wang J X, et al. Automated guided vehicle self- adaptive variable structure control based on genetic algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery, 2008,39(7):114-118 (in Chinese)
[6]	罗志凡,卢耀祖,张氢,等.自主移动小车轨迹跟踪的参数优化及仿真研究[J].计算机仿真,2006,23(8):149-152 Luo Z F, Lu Y Z, Zhang Q, et al. Tracking control parameter optimization and simulation of automatic guided vehicle[J]. Computer Simulation, 2006,23(8):149- 152 (in Chinese)
[7]	Khanesar M A, Teshnehlab M, Shoorehdeli M A. A novel binary particle swarm optimization[C]// Proceedings of the 15th Mediterranean Conference on Control & Automation, Athens-Greece, 2007
[8]	Zhang J R, Zhang J, Lok T M, et al. A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training[J]. Applied Mathematics and Computation, 2007,185(2):1026-1037
[9]	Lane J, Engelbrecht A, Gain J. Particle swarm optimization with spatially meaningful neighbors[C]// Proceedings of IEEE Swarm Intelligence Symposium, St. Louis MO USA: 2008:1-8
[10]	Nino-Suarez P A, Aranda-Bricaire E, Velasco-Villa M. Discrete-time sliding mode path-tracking control for a wheeled mobile robot[C]// Proceedings of the 45th IEEE Conference on Decision & Control, San Diego, CA, USA, 2006:3052-3057