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考虑输出约束的冗余驱动绳索并联机器人预设性能控制

陈正升 程玉虎 王雪松

陈正升, 程玉虎, 王雪松. 考虑输出约束的冗余驱动绳索并联机器人预设性能控制. 自动化学报, 2022, 48(7): 1704−1717 doi: 10.16383/j.aas.c210949
引用本文: 陈正升, 程玉虎, 王雪松. 考虑输出约束的冗余驱动绳索并联机器人预设性能控制. 自动化学报, 2022, 48(7): 1704−1717 doi: 10.16383/j.aas.c210949
Chen Zheng-Sheng, Cheng Yu-Hu, Wang Xue-Song. Prescribed performance control of redundantly-actuated cable driving parallel robots subjected to output constraint. Acta Automatica Sinica, 2022, 48(7): 1704−1717 doi: 10.16383/j.aas.c210949
Citation: Chen Zheng-Sheng, Cheng Yu-Hu, Wang Xue-Song. Prescribed performance control of redundantly-actuated cable driving parallel robots subjected to output constraint. Acta Automatica Sinica, 2022, 48(7): 1704−1717 doi: 10.16383/j.aas.c210949

考虑输出约束的冗余驱动绳索并联机器人预设性能控制

doi: 10.16383/j.aas.c210949
基金项目: 国家自然科学基金(61903347, 61976215, 62176259), 江苏省自然科学基金(BK20200086)资助
详细信息
    作者简介:

    陈正升:中国矿业大学信息与控制工程学院讲师. 2016年获得哈尔滨工业大学博士学位. 主要研究方向为机器人动力学建模与控制. E-mail: chenzhengsheng@cumt.edu.cn

    程玉虎:中国矿业大学信息与控制工程学院教授. 2005年获得中国科学院自动化研究所博士学位. 主要研究方向为机器学习, 智能优化与控制. E-mail: chengyuhu@163.com

    王雪松:中国矿业大学信息与控制工程学院教授. 2002年获得中国矿业大学博士学位. 主要研究方向为机器学习, 模式识别. 本文通信作者. E-mail: wangxuesongcumt@163.com

Prescribed Performance Control of Redundantly-actuated Cable Driving Parallel Robots Subjected to Output Constraint

Funds: Supported by National Natural Science Foundation of China (61903347, 61976215, 62176259) and Natural Science Foundation of Jiangsu Province (BK20200086)
More Information
    Author Bio:

    CHEN Zheng-Sheng Lecturer at the School of Information and Control Engineering, China University of Mining and Technology. He received his Ph.D. degree from Harbin Institute of Technology in 2016. His main research interest is dynamic modeling and control of robotics

    CHENG Yu-Hu Professor at the School of Information and Control Engineering, China University of Mining and Technology. He received his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2005. His research interest covers machining learning, intelligent optimization and control

    WANG Xue-Song Professor at the School of Information and Control Engineering, China University of Mining and Technology. She received her Ph.D. degree from China University of Mining and Technology in 2002. Her research interest covers machine learning and pattern recognition. Corresponding author of this paper

  • 摘要: 提出一种考虑输出约束的冗余驱动绳索并联机器人(Redundantly-actuated cable driving parallel robots, RCDPRs)预设性能有限时间控制算法. 首先, 采用Newton-Euler方程推导系统动力学模型, 并建立绳索拉力优化模型保证系统正常工作; 其次, 将输出约束问题转化为位置跟踪误差的坐标变换问题, 设计给定时间衰减函数与非对称变换函数, 将约束形式的跟踪误差转化为无约束变量, 实现给定时间的输出约束; 然后, 针对滑模控制的抖振问题, 在预设性能控制中采用模型不确定与扰动估计器进行扰动估计, 并通过自适应方法对扰动估计误差进行补偿; 以此为基础, 提出一种基于精度驱动且在分段点处三阶连续的终端滑模面进行控制算法设计; 最后, 采用Lyapunov函数证明算法的有限时间收敛特性, 并以7自由度冗余驱动绳索并联机器人为控制对象进行仿真研究, 对算法进行验证.
  • 图  1  冗余驱动绳索并联机器人结构原理图

    Fig.  1  Structural schematic diagram of the RCDPR

    图  2  带有7个驱动绳索的RCDPR

    Fig.  2  The RCDPR with 7 driving cables

    图  3  位置与角度跟踪误差

    Fig.  3  Positional and angular tracking errors

    图  4  速度跟踪误差

    Fig.  4  Velocity tracking error

    图  5  冗余驱动绳索并联机器人驱动力矩及其差值

    Fig.  5  Torques and torque deviations of the RCDPR

    图  6  采用本文算法时RCDPR各绳索拉力

    Fig.  6  RCDPR's pull forces of the proposed controller

    图  7  采用本文算法时自适应项系数

    Fig.  7  Adaptive coefficients of the proposed controller

    图  8  采用ASTUDESMC时自适应项系数

    Fig.  8  Adaptive coefficients of ASTUDESMC

    图  9  基于跟踪误差e的滑模面数值

    Fig.  9  Sliding mode surface for tracking error e

    图  10  基于跟踪误差z的滑模面数值

    Fig.  10  Sliding mode surface for tracking error z

    表  1  RCDPR运动学参数(mm)

    Table  1  Kinematic parameters of the RCDPR (mm)

    参数数值参数数值
    b1[0, 0, 1000]a1[−150, −100, 50]
    b2[100, 0, 1000]a2[150, −100, 50]
    b3[1000, 1000, 1000]a3[150, 100, 50]
    b4[0, 1000, 1000]a4[−150, 100, 50]
    b5[500, 0, 0]a5[0, −100, −50]
    b6[1000, 1000, 0]a6[150, 100, −50]
    b7[0, 1000, 0]a7[−150, 100, 50]
    r20
    下载: 导出CSV

    表  2  RCDPR惯性参数

    Table  2  Inertial parameters of the RCDPR

    参数描述数值
    mp动平台质量1.67 kg
    Ip动平台转动惯量矩阵diag{2.78×104, 5.56×104, 7.26×104} kg·mm2
    Ib滑轮转动惯量1.2×104 kg·mm2
    下载: 导出CSV

    表  3  各控制器参数

    Table  3  Parameters of all controllers

    控制器参数
    PPSMCK1, K2, α1, α2, α3, Δ11, Δ12, Δ13, Δ14, Δ15, Δ16, β4, β5, K3, γ0, T, λ1, λ2, Te, ε1i, ε2i, μ0, μ, k100I6, 50I6, 0.5, 1.5, 1.8, 0.01, 0.01, 0.01, 0.001, 0.001, 0.001, 5, 5, 10I6, 10, 0.001 s, 0.001, 0.01, 0.2 s, 0.5, 1, [0.2, 0.2, 0.2, 0.02, 0.02, 0.02]T, 0.002[1, 1, 1, 0.01, 0.01, 0.01]T, 30
    ASTUDESMCk1, k2, αi, T, ε, $ {\omega _1}\sqrt {{{{\gamma _1}} \mathord{\left/ {\vphantom {{{\gamma _1}} 2}} \right. } 2}} $, λ+4ε2100I6, 50I6, 0.5, 0.001 s, 0.5, 500, 10
    TDENFTSMCρ1, ρ2, α1i, α2i, k1, k2, L100I6, 50I6, 0.5, 1.8, 0.001 s
    UDESMCc, 1/a, k160, 0.001 s, 20
    下载: 导出CSV
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  • 收稿日期:  2021-10-08
  • 录用日期:  2022-01-11
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  • 刊出日期:  2022-07-01

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