A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  > In Press, Accepted Manuscript
H. Wang, J. Peng, F. Zhang, and Y. Wang, “High-order control barrier function-based safety control of constrained robotic systems: An augmented dynamics approach,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124524
Citation: H. Wang, J. Peng, F. Zhang, and Y. Wang, “High-order control barrier function-based safety control of constrained robotic systems: An augmented dynamics approach,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124524
shu

High-Order Control Barrier Function-Based Safety Control of Constrained Robotic Systems: An Augmented Dynamics Approach

doi: 10.1109/JAS.2024.124524
Funds:  This work was supported in part by the National Natural Science Foundation of China (62273311, 61773351), Henan Provincial Science Foundation for Distinguished Young Scholars (242300421051)
More Information
    Abstract
  • Although constraint satisfaction approaches have achieved fruitful results, system states may lose their smoothness and there may be undesired chattering of control inputs due to switching characteristics. Furthermore, it remains a challenge when there are additional constraints on control torques of robotic systems. In this article, we propose a novel high-order control barrier function (HoCBF)-based safety control method for robotic systems subject to input-output constraints, which can maintain the desired smoothness of system states and reduce undesired chattering vibration in the control torque. In our design, augmented dynamics are introduced into the HoCBF by constructing its output as the control input of the robotic system, so that the constraint satisfaction is facilitated by HoCBFs and the smoothness of system states is maintained by the augmented dynamics. This proposed scheme leads to the quadratic program (QP), which is more user-friendly in implementation since the constraint satisfaction control design is implemented as an add-on to an existing tracking control law. The proposed closed-loop control system not only achieves the requirements of real-time capability, stability, safety and compliance, but also reduces undesired chattering of control inputs. Finally, the effectiveness of the proposed control scheme is verified by simulations and experiments on robotic manipulators.

     

    • FullText(HTML)
  • loading
    • 1 [Online]. Available: https://youtu.be/yj1e7aKD4oQ
    References(41)
  • [1]
    Y. Yang, J. Han, Z. Liu, Z. Zhao and K.-S. Hong, “Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints,” IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 2, pp. 501–511, 2023. doi: 10.1109/JAS.2023.123213
    [2]
    L. Han, H. Yuan, W. Xu and Y. Huang, “Modified Dynamic Movement Primitives: Robot Trajectory Planning and Force Control Under Curved Surface Constraints,” IEEE Trans. Cybernetics, vol. 53, no. 7, pp. 4245–4258, 2023. doi: 10.1109/TCYB.2022.3158029
    [3]
    L. Kong, W. He, W. Yang, Q. Li and O. Kaynak, “Fuzzy Approximation-Based Finite-Time Control for a Robot With Actuator Saturation Under Time-Varying Constraints of Work Space,” IEEE Trans. Cybernetics, vol. 51, no. 10, pp. 4873–4884, 2021. doi: 10.1109/TCYB.2020.2998837
    [4]
    V. Azimi and S. Hutchinson, “Robust Adaptive Control Lyapunov Barrier Function for Non-collocated Control and Safety of Underactuated Robotic Systems,” Int. Journal of Robust and Nonlinear Control, vol. 32, no. 13, pp. 7363–7390, 2022. doi: 10.1002/rnc.6239
    [5]
    G. Zong, Y. Wang, H.R. Karimi and K. Shi, “Observer-Based Adaptive Neural Tracking Control for a Class of Nonlinear Systems With Prescribed Performance and Input Dead-Zone Constraints,” Neural Networks, vol. 147, pp. 126–135, 2022. doi: 10.1016/j.neunet.2021.12.019
    [6]
    Y. Wu, W. Niu, L. Kong, X. Yu and W. He, “Fixed-Time Neural Network Control of a Robotic Manipulator With Input Deadzone,” ISA Transactions, vol. 135, pp. 449–461, 2023. doi: 10.1016/j.isatra.2022.09.030
    [7]
    T. Yang, Y. Chen, N. Sun, L. Liu, Y. Qin and Y. Fang, “Learning-Based Error-Constrained Motion Control for Pneumatic Artificial Muscle-Actuated Exoskeleton Robots With Hardware Experiments,” IEEE Trans. Automation Science and Engineering, vol. 19, no. 4, pp. 3700–3711, 2022. doi: 10.1109/TASE.2021.3131034
    [8]
    C. Zhu, Y. Jiang and C. Yang, “Fixed-Time Neural Control of Robot Manipulator With Global Stability and Guaranteed Transient Performance,” IEEE Trans. Industrial Electronics, vol. 70, no. 1, pp. 803–812, 2023. doi: 10.1109/TIE.2022.3156037
    [9]
    S. Lu, M. Chen, Y. Liu and S. Shao, “Adaptive NN Tracking Control for Uncertain MIMO Nonlinear System With Time-Varying State Constraints and Disturbances,” IEEE Trans. Neural Networks and Learning Systems, vol. 34, no. 10, pp. 7309–7323, 2023. doi: 10.1109/TNNLS.2022.3141052
    [10]
    A. Kazemipour, M. Khatib, K.A. Khudir, C. Gaz and A. De Luca, “Kinematic Control of Redundant Robots With Online Handling of Variable Generalized Hard Constraints,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 9279–9286, 2022. doi: 10.1109/LRA.2022.3190832
    [11]
    Y. Zhang and C. Hua, “Composite Learning Finite-Time Control of Robotic Systems With Output Constraints,” IEEE Trans. Industrial Electronics, vol. 70, no. 2, pp. 1687–1695, 2023. doi: 10.1109/TIE.2022.3161796
    [12]
    W. Sun, Y. Wu and X. Lv, “Adaptive Neural Network Control for Full-State Constrained Robotic Manipulator With Actuator Saturation and Time-Varying Delays,” IEEE Trans. Neural Networks and Learning Systems, vol. 33, no. 8, pp. 3331–3342, 2022. doi: 10.1109/TNNLS.2021.3051946
    [13]
    G. Zhu, H. Li, X. Zhang, C. Wang, C.-Y. Su and J. Hu, “Adaptive Consensus Quantized Control for a Class of High-Order Nonlinear Multi-Agent Systems With Input Hysteresis and Full State Constraints,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 9, pp. 1574–1589, 2022. doi: 10.1109/JAS.2022.105800
    [14]
    Y. Hu, H. Yan, H. Zhang, M. Wang and L. Zeng, “Robust Adaptive Fixed-Time Sliding-Mode Control for Uncertain Robotic Systems With Input Saturation,” IEEE Trans. Cybernetics, vol. 53, no. 4, pp. 2636–2646, 2023. doi: 10.1109/TCYB.2022.3164739
    [15]
    K. Zhao, Y. Song, C.L.P. Chen and L. Chen, “Control of Nonlinear Systems Under Dynamic Constraints: A Unified Barrier Function-Based Approach,” Automatica, vol. 119, p. 109102, 2020. doi: 10.1016/j.automatica.2020.109102
    [16]
    S. Lu, M. Chen, Y. Liu and S. Shao, “SDO-Based Command Filtered Adaptive Neural Tracking Control for MIMO Nonlinear Systems With Time-Varying Constraints,” IEEE Trans. Cybernetics, 2023. doi: 10.1109/TCYB.2023.3325456
    [17]
    K. Yong, M. Chen, Y. Shi and Q. Wu, “Flexible Performance-Based Robust Control for a Class of Nonlinear Systems With Input Saturation,” Automatica, vol. 122, p. 109268, 2020. doi: 10.1016/j.automatica.2020.109268
    [18]
    Y. Yang, J. Tan and D. Yue, “Prescribed Performance Control of One-DOF Link Manipulator With Uncertainties and Input Saturation Constraint,” IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 1, pp. 148–157, 2019. doi: 10.1109/JAS.2018.7511099
    [19]
    B. Helian, Z. Chen and B. Yao, “Constrained Motion Control of an Electro- Hydraulic Actuator Under Multiple Time-Varying Constraints,” IEEE Trans. Industrial Informatics, vol. 19, no. 12, pp. 11878–11888, 2023. doi: 10.1109/TII.2023.3249760
    [20]
    W.S. Cortez, D. Oetomo, C. Manzie and P. Choong, “Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Grasping,” IEEE Trans. Control Systems Technology, vol. 29, no. 2, pp. 530–545, 2021. doi: 10.1109/TCST.2019.2952317
    [21]
    S. Zhang, D. Zhai, Y. Xiong, J. Lin and Y. Xia, “Safety-Critical Control for Robotic Systems With Uncertain Model via Control Barrier Function,” Int. Journal of Robust and Nonlinear Control, vol. 33, no. 6, pp. 3661–3676, 2023. doi: 10.1002/rnc.6585
    [22]
    Q. Nguyen and K. Sreenath, “Robust Safety-Critical Control for Dynamic Robotics,” IEEE Trans. Automatic Control, vol. 67, no. 3, pp. 1073–1088, 2022. doi: 10.1109/TAC.2021.3059156
    [23]
    A. D. Ames, X. Xu, J.W. Grizzle and P. Tabuada, “Control Barrier Function Based Quadratic Programs for Safety Critical Systems,” IEEE Trans. Automatic Control, vol. 62, no. 8, pp. 3861–3876, 2017. doi: 10.1109/TAC.2016.2638961
    [24]
    H. Wang, J. Peng, F. Zhang and Y. Wang, “A Composite Control Framework of Safety Satisfaction and Uncertainties Compensation for Constrained Time-Varying Nonlinear MIMO Systems,” IEEE Trans. Systems, Man, and Cybernetics: Systems, vol. 53, no. 12, pp. 7864–7875, 2023. doi: 10.1109/TSMC.2023.3301881
    [25]
    B. Li, S. Wen, Z. Yan, G. Wen and T. Huang, “A Survey on the Control Lyapunov Function and Control Barrier Function for Nonlinear-Affine Control Systems,” IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 3, pp. 584–602, 2023. doi: 10.1109/JAS.2023.123075
    [26]
    A.D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath and P. Tabuada, “Control Barrier Functions: Theory and Applications,” 18th European Control Conf. (ECC), Naples, Italy, pp. 3420-3431, 2019.
    [27]
    X. Xu, P. Tabuada, J.W. Grizzle, and A. D. Ames, “Robustness of Control Barrier Functions for Safety Critical Control,” Proc. IFAC Conf. on Analysis and Design of Hybrid Systems, vol. 48, no. 27, pp. 54–61, 2015.
    [28]
    X. Tan, W.S. Cortez and D. V. Dimarogonas, “High-Order Barrier Functions: Robustness, Safety, and Performance-Critical Control,” IEEE Trans. Automatic Control, vol. 67, no. 6, pp. 3021–3028, 2022. doi: 10.1109/TAC.2021.3089639
    [29]
    A. Singletary, S. Kolathaya and A.D. Ames, “Safety-Critical Kinematic Control of Robotic Systems,” IEEE Control Systems Letters, vol. 6, pp. 139–144, 2022. doi: 10.1109/LCSYS.2021.3050609
    [30]
    C.T. Landi, F. Ferraguti, S. Costi, M. Bonfe, and C. Secchi, “Safety Barrier Functions for Human-Robot Interaction With Industrial Manipulators,” 18th European Control Conf. (ECC), pp. 2565-2570, 2019.
    [31]
    T.G. Molnar, R.K. Cosner, A.W. Singletary, W. Ubellacker, and A. D. Ames, “Model-Free Safety-Critical Control for Robotic Systems,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 944–951, 2022. doi: 10.1109/LRA.2021.3135569
    [32]
    L. Wang, A.D. Ames and M. Egerstedt, “Safety Barrier Certificates for Collisions-Free Multirobot Systems,” IEEE Trans. Robotics, vol. 33, no. 3, pp. 661–674, 2017. doi: 10.1109/TRO.2017.2659727
    [33]
    X. Xu, “Constrained Control of Input-Output Linearizable Systems Using Control Sharing Barrier Function,” Automatica, vol. 87, pp. 195–201, 2018. doi: 10.1016/j.automatica.2017.10.005
    [34]
    W. Xiao, C. Belta and C.G. Cassandras, “Adaptive Control Barrier Functions,” IEEE Trans. Automatic Control, vol. 67, no. 5, pp. 2267–2281, 2022. doi: 10.1109/TAC.2021.3074895
    [35]
    H. Wang, J. Peng, J. Xu, F. Zhang and Y. Wang, “High-Order Control Barrier Functions-Based Optimization Control for Time-Varying Nonlinear Systems With Full-State Constraints: A Dynamic Sub-Safe Set Approach,” Int. Journal of Robust and Nonlinear Control, vol. 33, no. 8, pp. 4490–4503, 2023. doi: 10.1002/rnc.6624
    [36]
    H. Wang, J. Peng, F. Zhang, H. Zhang and Y. Wang, “High-Order Control Barrier Functions-Based Impedance Control of a Robotic Manipulator With Time-Varying Output Constraints,” ISA Transac tions, vol. 129, pp. 361–369, 2022. doi: 10.1016/j.isatra.2022.02.013
    [37]
    B. Park and J. Parkm, “Heel-Strike and Toe-Off Walking of Humanoid Robot Using Quadratic Programming Considering the Foot Contact States,” Robotics and Autonomous Systems, vol. 163, p. 104396, 2023. doi: 10.1016/j.robot.2023.104396
    [38]
    J. Lin, D.-H. Zhai, Y. Xiong and Y. Xia, “Safety Control for UR-Type Robotic Manipulators via High-Order Control Barrier Functions and Analytical Inverse Kinematics,” IEEE Trans. Industrial Electronics, 2023. doi: 10.1109/TIE.2023.3296810
    [39]
    J. Christoph and S. Hirche, “Augmented Invariance Control for Impedance-Controlled Robots With Safety Margins,” IFAC-Papers OnLine, vol. 50, no. 1, pp. 12053–12058, 2017. doi: 10.1016/j.ifacol.2017.08.2121
    [40]
    H.K. Khalil, “Nonlinear Systems,” 3rd ed. Prentice Hall, 2002.
    [41]
    C.I. Byrnes and A. Isidori, “Global Feedback Stabilization of Nonlinear Systems,” 24th IEEE Conf. on Decision and Control (CDC), Fort Lauderdale, FL, USA, pp. 1031–1037, 1985.
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(13)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (67) PDF downloads(26) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return