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论文:2024,Vol:42,Issue(2):368-376 |
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引用本文: |
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史国庆, 程嘉毅, 张建东, 杨啟明, 吴勇, 武凡. 基于反馈线性化的广义预测控制机械臂轨迹跟踪算法[J]. 西北工业大学学报 |
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SHI Guoqing, CHENG Jiayi, ZHANG Jiandong, YANG Qiming, WU Yong, WU Fan. A trajectory tracking algorithm of generalized predictive control manipulator based on feedback linearization[J]. Journal of Northwestern Polytechnical University |
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基于反馈线性化的广义预测控制机械臂轨迹跟踪算法 |
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史国庆1, 程嘉毅1, 张建东1, 杨啟明1, 吴勇1, 武凡2 |
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1. 西北工业大学 电子信息学院, 陕西 西安 710072; 2. 成都飞机设计研究所, 四川 成都 610041 |
摘要: |
分析了机械臂轨迹跟踪控制问题的特点,建立了二自由度机械臂动力学模型。为解决广义预测控制(generalized predictive control,GPC)算法难以适用于非线性系统的问题,在现有的GPC算法基础上,设计了基于反馈线性化的广义预测控制(feedback linearization-generalized predictive control,FL-GPC)算法框架,即底层为线性系统预测控制,非线性项使用预估值来进行代替,高层为迭代修正预估量,使用迭代计算的方式对非线性项进行预估。使用FL-GPC算法对二自由度机械臂的静态、动态轨迹跟踪任务进行了仿真。仿真结果表明,算法可以进行有效的机械臂轨迹跟踪控制。 |
关键词:
轨迹跟踪
非线性系统
广义预测控制
反馈线性化
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A trajectory tracking algorithm of generalized predictive control manipulator based on feedback linearization |
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SHI Guoqing1, CHENG Jiayi1, ZHANG Jiandong1, YANG Qiming1, WU Yong1, WU Fan2 |
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1. School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China; 2. Chengdu Aircraft Design & Research Institute, Chengdu 610041, China |
Abstract: |
This paper analyzes the characteristics of the trajectory tracking control problem of the manipulator and establishes a two-degree-of-freedom manipulator dynamic model. In order to solve the problem that generalized predictive control(GPC) algorithm is difficult to apply to nonlinear systems, a feedback linearization-based generalized predictive control(FL-GPC) algorithm framework is designed. The bottom layer of the algorithm is the linear system predictive control and the non-linear term is replaced by the estimated value. The upper level is iteratively revised estimates and the non-linear term is estimated using the iterative calculation method. Finally, the FL-GPC algorithm is used to simulate the static and dynamic trajectory tracking tasks of a two-degree-of-freedom manipulator. Simulation results show that the algorithm can perform effective manipulator trajectory tracking control. |
Key words:
trajectory tracking
nonlinear system generalized
predictive control
feedback linearization
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收稿日期: 2023-02-22
修回日期:
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DOI: 10.1051/jnwpu/20244220368 |
基金项目: 陕西省重点研发计划(2022GY-089)与陕西省自然科学基础研究计划(2022JQ-593)资助 |
通讯作者: 张建东(1974—),副教授 e-mail:jdzhang@nwpu.edu.cn
Email:jdzhang@nwpu.edu.cn |
作者简介: 史国庆(1974—),副教授
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相关功能 |
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作者相关文章 |
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史国庆 在本刊中的所有文章 |
程嘉毅 在本刊中的所有文章 |
张建东 在本刊中的所有文章 |
杨啟明 在本刊中的所有文章 |
吴勇 在本刊中的所有文章 |
武凡 在本刊中的所有文章 |
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参考文献: |
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[1] 马克W·斯庞, 赛斯·哈钦森, M·维德雅萨加. 机器人建模和控制[M]. 北京:机械工业出版社, 2016 MARK W·Spang, SETH Hutchinson, M·Vaderjasaga. Robot modeling and control[M]. Beijing: Machinery Industry Press, 2016 (in Chinese) [2] PETER Corke. 机器人学、机器视觉与控制[M]. 北京:电子工业出版社, 2016 PETER Corke. Robotics, Machine Vision and Control[M]. Beijing: Electronics Industry Press, 2016 (in Chinese) [3] 丁学恭. 机器人控制研究[M]. 杭州:浙江大学出版社, 2006 DING Xuegong. Research on robot control[M]. Hangzhou: Zhejiang University Press, 2006 (in Chinese) [4] CERVANTES I, ALVARE-RAMIREZ J. On the PID tracking control of robot manipulators[J]. Systems & Control Letters, 2001, 42(1): 37-46 [5] 张铁, 洪景东, 李秋奋, 等. 基于BP神经网络的机器人波动摩擦力矩修正方法[J]. 工程科学学报, 2019, 41(8): 1085-1091 ZHANG Tie, HONG Jingdong, LI Qiufen, et al. Wave friction correction method for a robot based on BP neural net-work[J]. Journal of Engineering Science, 2019,41(8): 1085-1091 (in Chinese) [6] 马宇豪, 梁雁冰. 一种基于六次多项式轨迹规划的机械臂避障算法[J]. 西北工业大学学报, 2020, 38(2): 392-400 MA Yuhao, LIANG Yanbing. An obstacle avoidance algorithm for manipulator based on sixth-order polynomial trajectory planning[J]. Journal of Northwest Polytechnical University, 2020,38(2): 392-400 (in Chinese) [7] YANRU L, YAN Z. Two-DOF manipulator trajectory tracking control based on unfalsified control[C]//The 27th Chinese Cont-rol and Decision Conference, Qingdao, 2015: 4563-4566 [8] ABDALLA A Y, ABDALLA T Y, CHYAID A M. Grasshopper algorithm based fuzzy system for trajectory tracking of robot manipulator[C]//2022 International Conference on Electrical, Computer and Energy Technologies, Prague, 2022: 1-5 [9] ZHANG X, GU J, ASAD M U, et al. Beetle bee algorithm applied to trajectory tracking control of omni manipulator[C]//2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, Lahore, 2022: 1-5 [10] SINGH R, PRASAD L B. Optimal trajectory tracking of robotic manipulator using ant colony optimization[C]//2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, Gorakhpur, 2018: 1-6 [11] SHAOMING L, RUIPENG L. Research on trajectory tracking control of multiple degree of freedom manipulator[C]//2017 32nd Youth Academic Annual Conference of Chinese Association of Automation, Hefei, 2017: 218-222 [12] JUAN W, YANG H, XIE H. Control of manipulator trajectory tracking based on improved RBFNN[C]//2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, 2009: 142-145 [13] MIRÓJ V, WHITE A S, GILL R. On-line time-optimal algorithm for manipulator trajectory planning[C]//1997 European Control Conference, Brussels, 1997: 2611-2616 [14] ATALAR-AYYLLDLZ B, KARAHAN O. Tuning of fractional order pid controller using CS algorithm for trajectory tracking control[C]//2018 6th International Conference on Control Engineering & Information Technology, Istanbul, 2018: 1-6 [15] ZHANG L, CHENG L. An adaptive neural network control method for robotic manipulators trajectory tracking[C]//2019 Chinese Control and Decision Conference, Nanchang, 2019: 4839-4844 [16] WIDYIANTO A, YAZID E, MIRDANIES M, et al. Optimization of PD controller using ACO for the trajectory tracking of a ship-mounted two-DOF manipulator system[C]//2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering, Yogyakarta, 2022: 634-638 [17] LAMPINEN S, NIEMI J, MATTILA J. Flow-bounded trajectory-scaling algorithm for hydraulic robotic manipulators[C]//2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Boston, 2020: 619-624 [18] ZHU Q, WANG J, ZHANG W A, et al. A Geometry based IK solver and b-spline method for trajectory tracking of 5-DOF manipulators[C]//2018 37th Chinese Control Conference, Wuhan, 2018: 3865-3870 [19] 杨易旻. 基于极限学习的系统辨识方法及应用研究[D]. 长沙:湖南大学, 2013 YANG Yimin. Researches on extreme learning theory for system identification and applications[D]. Changsha: Hunan University, 2013 (in Chinese) [20] 于欣波, 贺威, 薛程谦, 等. 基于扰动观测器的机器人自适应神经网络跟踪控制研究[J]. 自动化学报, 2019, 45(7): 1307-1324 YU Xinbo, HE Wei, XUE Chengqian, et al. Disturbance observer-based adaptive neural network tracking control for robots[J]. Acta Automatica Sinica, 2019,45(7): 1307-1324 (in Chinese) [21] 王雷坤. 凿岩机器人钻臂运动轨迹控制研究[D]. 赣州:江西理工大学, 2019 WANG Leikun. Research on trajectory control of drilling arm of rock drilling robot[D]. Ganzhou: Jiangxi University of Science and Technology, 2019 (in Chinese) [22] 崔敏其. SCARA机器人的拉格朗日动力学建模[J]. 机械设计与制造, 2013(12): 76-78 CUI Minqi. Dynamical modeling of SCARA robot based on lagrange formulation[J]. Mechanical Design and Manufacturing, 2013 (12): 76-78 (in Chinese) [23] 周岗, 姚琼荟, 陈永冰, 等. 基于输入输出线性化的船舶全局直线航迹控制[J]. 控制理论与应用, 2007(1): 117-121 ZHOU Gang, YAO Qionghui, CHEN Yongbing, et al. Global straight-line tracking control of ships based on input-output linearization[J]. Control Theory and Application, 2007 (1): 117-121 (in Chinese) [24] 李铁山, 杨盐生, 郑云峰. 不完全驱动船舶航迹控制输入输出线性化设计[J]. 系统工程与电子技术, 2004(7): 945-948 LI Tieshan, YANG Yansheng, ZHENG Yunfeng. Input-output linearization designs for straight-line tracking control of undera-ctuated ships[J]. System Engineering and Electronics, 2004(7): 945-948 (in Chinese) [25] 帅鑫, 李艳君, 吴铁军. 一种柔性机械臂末端轨迹跟踪的预测控制算法[J]. 浙江大学学报, 2010, 44(2): 259-264 SHUAI Xin, LI Yanjun, WU Tiejun. Real time predictive control algorithm for endpoint trajectory tracking of flexible mani-pulator[J]. Journal of Zhejiang University, 2010, 44(2): 259-264 (in Chinese) [26] SHOHEI Hagane, LIZ Katherine Rincon Ardila, TAKUMA Katsumata, et al. Adaptive generalized predictive controller and cartesian force control for robot arm using dynamics and geometric identification[J]. Journal of Robotics and Mechatronics, 2018, 30(6): 927-942 [27] 程林云, 张雷, 宋晓娜. 基于RBF神经网络的机械臂自适应控制方法[J]. 计算机测量与控制, 2019, 27(7): 79-84 CHENG Linyun, ZHANG Lei, SONG Xiaona. Adaptive control method of manipulator based on RBF neural network[J]. Computer Measurement and Control, 2019, 27(7): 79-84 (in Chinese) |
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