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一类移动机械臂系统的鲁棒H 跟踪控制

孙倩 郑琳铄 张学文 贾英民

孙倩, 郑琳铄, 张学文, 贾英民. 一类移动机械臂系统的鲁棒H∞ 跟踪控制. 自动化学报, 2025, 51(10): 1−13 doi: 10.16383/j.aas.c250151
引用本文: 孙倩, 郑琳铄, 张学文, 贾英民. 一类移动机械臂系统的鲁棒H 跟踪控制. 自动化学报, 2025, 51(10): 1−13 doi: 10.16383/j.aas.c250151
Sun Qian, Zheng Lin-Shuo, Zhang Xue-Wen, Jia Ying-Min. Robust H∞ tracking control for a class of mobile manipulator systems. Acta Automatica Sinica, 2025, 51(10): 1−13 doi: 10.16383/j.aas.c250151
Citation: Sun Qian, Zheng Lin-Shuo, Zhang Xue-Wen, Jia Ying-Min. Robust H tracking control for a class of mobile manipulator systems. Acta Automatica Sinica, 2025, 51(10): 1−13 doi: 10.16383/j.aas.c250151

一类移动机械臂系统的鲁棒H 跟踪控制

doi: 10.16383/j.aas.c250151 cstr: 32138.14.j.aas.c250151
基金项目: 国家自然科学基金(62227810, 62133001), 国家重点基础研究发展计划(973计划) (2012CB821200, 2012CB821201)资助
详细信息
    作者简介:

    孙倩:北京航空航天大学自动化科学与电气工程学院博士研究生. 主要研究方向为机器人系统的鲁棒与智能控制. E-mail: mssunqian@buaa.edu.cn

    郑琳铄:北京航空航天大学自动化科学与电气工程学院博士研究生. 主要研究方向为移动机械臂的路径规划与视觉伺服控制. E-mail: zhengls@buaa.edu.cn

    张学文:北京航空航天大学自动化科学与电气工程学院博士研究生. 主要研究方向为移动机械臂的鲁棒协调控制. E-mail: xuewenzhang@buaa.edu.cn

    贾英民:北京航空航天大学自动化科学与电气工程学院教授. 主要研究方向为鲁棒与自适应控制, 智能控制及其在机器人系统中的应用. 本文通信作者. E-mail: ymjia@buaa.edu.cn

Robust H Tracking Control for a Class of Mobile Manipulator Systems

Funds: Supported by National Natural Science Foundation of China (62227810, 62133001), and National Basic Research Program of China (973 Program) (2012CB821200, 2012CB821201)
More Information
    Author Bio:

    SUN Qian Ph.D. candidate at the School of Automation Science and Electrical Engineering, Beihang University. Her main research interest is robust and intelligent control in robotic systems

    ZHENG Lin-Shuo Ph.D. candidate at the School of Automation Science and Electrical Engineering, Beihang University. His research interest covers path planning and visual servoing control of mobile manipulators

    ZHANG Xue-Wen Ph.D. candidate at the School of Automation Science and Electrical Engineering, Beihang University. His main research interest is robust coordinated control of mobile manipulators

    JIA Ying-Min Professor at the School of Automation Science and Electrical Engineering, Beihang University. His research interest covers robust and adaptive control, intelligent control and their applications in robotic systems. Corresponding author of this paper

  • 摘要: 针对存在参数不确定性、外部扰动和输入饱和约束的移动机械臂跟踪控制问题, 提出一种基于自适应动态规划的鲁棒$H_{\infty} $控制方案. 首先, 通过设计神经网络辨识器, 对跟踪误差动力学中的不确定性进行在线估计. 然后, 考虑外部扰动、目标运动扰动和辨识误差, 将鲁棒$H_{\infty} $控制转化为零和博弈问题进行求解, 并在值函数中引入广义非二次泛函来处理输入饱和约束. 进一步, 构建评价网络逼近最优值函数, 获得近似最优控制律及最坏情况下的总扰动估计, 实现闭环系统跟踪误差和评价网络权值估计误差的一致最终有界. 仿真结果验证了所提方案的有效性.
  • 图  1  移动机械臂系统示意图

    Fig.  1  Schematic of mobile manipulator systems

    图  2  状态估计误差的时间响应

    Fig.  2  Time response of state estimation error

    图  9  LSMC方法作用下控制输入的时间响应

    Fig.  9  Time response of control input with LSMC method

    图  3  评价网络权值估计的时间响应

    Fig.  3  Time response of weight estimation of critic network

    图  4  $H_\infty $控制方案作用下位置和姿态跟踪误差的时间响应

    Fig.  4  Time response of position and attitude tracking error with $H_\infty $ control scheme

    图  5  LSMC方法作用下位置和姿态跟踪误差的时间响应

    Fig.  5  Time response of position and attitude tracking error with LSMC method

    图  6  $H_\infty $控制方案作用下相对线速度和角速度的时间响应

    Fig.  6  Time response of relative velocity and angular velocity with $H_\infty $ control scheme

    图  7  LSMC方法作用下相对线速度和角速度的时间响应

    Fig.  7  Time response of relative velocity and angular velocity with LSMC method

    图  8  $H_\infty $控制方案作用下控制输入的时间响应

    Fig.  8  Time response of control input with $H_\infty $ control scheme

    表  1  两种方法的控制成本对比

    Table  1  Comparison of control costs between two methods

    控制方案 0 ~ 5 s 0 ~ 20 s
    $ H_\infty $ 控制方案 5.1867 × 103 1.6188 × 104
    LSMC方法 6.3742 × 103 1.7338 × 104
    下载: 导出CSV
  • [1] Zeng Y D, Zhang D Y, Chien S Y, Tju H S, Wiesse C, Cao F. Task sensing and adaptive control for mobile manipulator in indoor painting application. IEEE/ASME Transactions on Mechatronics, 2024, 29(4): 2956−2963 doi: 10.1109/TMECH.2024.3399787
    [2] Zhang S J, Cheng S H, Jin Z L. Variable trajectory impedance: A super-twisting sliding mode control method for mobile manipulator based on identification model. IEEE Transactions on Industrial Electronics, 2025, 72(1): 610−619 doi: 10.1109/TIE.2024.3383042
    [3] Xian C X, Zhao Y, Wen G H, Chen G R. Robust event-triggered distributed optimal coordination of heterogeneous systems over directed networks. IEEE Transactions on Automatic Control, 2024, 69(7): 4522−4537 doi: 10.1109/TAC.2023.3335797
    [4] Wen G H, Zhao Y, Duan Z S, Yu W W, Chen G R. Containment of higher-order multi-leader multi-agent systems: A dynamic output approach. IEEE Transactions on Automatic Control, 2016, 61(4): 1135−1140 doi: 10.1109/TAC.2015.2465071
    [5] Huang Y, Jia Y M. Nonlinear robust H control for spacecraft body-fixed hovering around noncooperative target via modified θ-D method. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(5): 2451−2463 doi: 10.1109/TAES.2018.2890351
    [6] Wu S N, Chu W M, Ma X, Radice G, Wu Z G. Multi-objective integrated robust H control for attitude tracking of a flexible spacecraft. Acta Astronautica, 2018, 151: 80−87 doi: 10.1016/j.actaastro.2018.05.062
    [7] Wang Z, Li Y. Rigid spacecraft nonlinear robust H attitude controller design under actuator misalignments. Nonlinear Dynamics, 2023, 111: 15037−15054 doi: 10.1007/s11071-023-08620-6
    [8] Zuo Y, Wang Y N, Liu X Z, Yang S X, Huang L H, Wu X R, et al. Neural network robust H tracking control strategy for robot manipulators. Applied Mathematical Modelling, 2010, 34(7): 1823−1838 doi: 10.1016/j.apm.2009.09.026
    [9] da Cruz Figueredo L F, Adorno B V, Ishihara J Y. Robust H kinematic control of manipulator robots using dual quaternion algebra. Automatica, 2021, 132: Article No. 109817 doi: 10.1016/j.automatica.2021.109817
    [10] Song G I, Park H Y, Kim J H. The H robust stability and performance conditions for uncertain robot manipulators. IEEE/CAA Journal of Automatica Sinica, 2025, 12(1): 270−272 doi: 10.1109/JAS.2024.124701
    [11] Alinezhad H S, Esfanjani R M. Nonlinear H control for synchronization of networked manipulators subject to delayed communication. Journal of the Franklin Institute, 2022, 359(2): 999−1017 doi: 10.1016/j.jfranklin.2021.11.025
    [12] Liu H T, Tian X H, Wang G, Zhang T. Finite-time H control for high-precision tracking in robotic manipulators using backstepping control. IEEE Transactions on Industrial Electronics, 2016, 63(9): 5501−5513 doi: 10.1109/TIE.2016.2583998
    [13] 黄琳. 稳定性与鲁棒性的理论基础. 北京: 科学出版社, 2003. 502−511

    Huang Lin. Theoretical Foundation of Stability and Robustness. Beijing: Science Press, 2003. 502−511
    [14] Wang D, Gao N, Liu D R, Li J N, Lewis F L. Recent progress in reinforcement learning and adaptive dynamic programming for advanced control applications. IEEE/CAA Journal of Automatica Sinica, 2024, 11(1): 18−36 doi: 10.1109/JAS.2023.123843
    [15] Lewis F L, Vrabie D, Vamvoudakis K G. Reinforcement learning and feedback control: Using natural decision methods to design optimal adaptive controllers. IEEE Control Systems Magazine, 2012, 32(6): 76−105 doi: 10.1109/MCS.2012.2214134
    [16] 王鼎. 基于学习的鲁棒自适应评判控制研究进展. 自动化学报, 2019, 45(6): 1031−1043

    Wang Ding. Research progress on learning-based robust adaptive critic control. Acta Automatica Sinica, 2019, 45(6): 1031−1043
    [17] Wei Q L, Song R Z, Yan P F. Data-driven zero-sum neuro-optimal control for a class of continuous-time unknown nonlinear systems with disturbance using ADP. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(2): 444−458 doi: 10.1109/TNNLS.2015.2464080
    [18] Xue S, Luo B, Liu D R. Event-triggered adaptive dynamic programming for zero-sum game of partially unknown continuous-time nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(9): 3189−3199 doi: 10.1109/TSMC.2018.2852810
    [19] Xue S, Luo B, Liu D R, Yang Y. Constrained event-triggered H control based on adaptive dynamic programming with concurrent learning. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(1): 357−369 doi: 10.1109/TSMC.2020.2997559
    [20] Rizvi S A A, Lin Z L. Output feedback Q-learning for discrete-time linear zero-sum games with application to the H-infinity control. Automatica, 2018, 95: 213−221 doi: 10.1016/j.automatica.2018.05.027
    [21] Hu G J, Guo J G, Guo Z Y, Cieslak J, Henry D. ADP-based intelligent tracking algorithm for reentry vehicles subjected to model and state uncertainties. IEEE Transactions on Industrial Informatics, 2023, 19(4): 6047−6055 doi: 10.1109/TII.2022.3171327
    [22] Dong B, An T J, Zhu X Y, Li Y C, Liu K P. Zero-sum game-based neuro-optimal control of modular robot manipulators with uncertain disturbance using critic only policy iteration. Neurocomputing, 2021, 450: 183−196 doi: 10.1016/j.neucom.2021.04.032
    [23] 高为炳. 非线性控制系统导论. 北京: 科学出版社, 1988. 500−501

    Gao Wei-Bing. Introduction to Nonlinear Control Systems. Beijing: Science Press, 1988. 500−501
    [24] Wang D, Mu C X, Liu D R, Ma H W. On mixed data and event driven design for adaptive-critic-based nonlinear H control. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(4): 993−1005 doi: 10.1109/TNNLS.2016.2642128
    [25] Hu C, Wang Z, Bu X W, Zhao J, Na J, Gao H B. Optimal tracking control for autonomous vehicle with prescribed performance via adaptive dynamic programming. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(9): 12437−12449 doi: 10.1109/TITS.2024.3384113
    [26] Yang T, Sun N, Chen H, Fang Y C. Adaptive optimal motion control of uncertain underactuated mechatronic systems with actuator constraints. IEEE/ASME Transactions on Mechatronics, 2023, 28(1): 210−222 doi: 10.1109/TMECH.2022.3192002
    [27] Wang Y X, An T J, Dong B, Zhu M C, Li Y C. Adaptive dynamic programming-based finite-time optimal backstepping force/position control of reconfigurable robot manipulators via pareto optimal. IEEE Transactions on Automation Science and Engineering, 2025, 22: 10660−10671 doi: 10.1109/TASE.2025.3527569
    [28] Spong M W, Hutchinson S, Vidyasagar M. Robot Modeling and Control. Hoboken: John Wiley & Sons, 2006. 50−53
    [29] Siciliano B, Khatib O. Springer Handbook of Robotics (Second Edition). Cham: Springer International Publishing, 2016. 851−853
    [30] Li Z J, Ge S S. Fundamentals in Modeling and Control of Mobile Manipulators. Boca Raton: CRC Press, 2013. 17−40
    [31] 霍伟. 机器人动力学与控制. 北京: 高等教育出版社, 2005. 55−63

    Huo Wei. Robot Dynamics and Control. Beijing: Higher Education Press, 2005. 55−63
    [32] 高为炳. 变结构控制理论基础. 北京: 中国科学技术出版社, 1990. 265−267

    Gao Wei-Bing. Fundamentals of Variable Structure Control Theory. Beijing: China Science and Technology Press, 1990. 265−267
    [33] 黄琳. 系统与控制理论中的线性代数(上册)(第二版). 北京: 科学出版社, 2018. 225−228

    Huang Lin. Linear Algebra in System and Control Theory: Volume I (Second Edition). Beijing: Science Press, 2018. 225−228
    [34] Abu-Khalaf M, Lewis F L. Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach. Automatica, 2005, 41(5): 779−791 doi: 10.1016/j.automatica.2004.11.034
    [35] 黄琳. 最优控制理论讲义. 北京: 科学出版社, 2021. 52−59

    Huang Lin. Lectures on Optimal Control Theory. Beijing: Science Press, 2021. 52−59
    [36] 贾英民. 鲁棒H 控制. 北京: 科学出版社, 2007. 57−67

    Jia Ying-Min. Robust H Control. Beijing: Science Press, 2007. 57−67
    [37] Kong L H, Zhang S, Yu X B. Approximate optimal control for an uncertain robot based on adaptive dynamic programming. Neurocomputing, 2021, 423: 308−317 doi: 10.1016/j.neucom.2020.10.012
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  • 收稿日期:  2025-04-14
  • 录用日期:  2025-07-21
  • 网络出版日期:  2025-09-24

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