A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 2
Mar.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Ameer Hamza Khan, Zili Shao, Shuai Li, Qixin Wang and Nan Guan, "Which is the Best PID Variant for Pneumatic Soft Robots? An Experimental Study," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 451-460, Mar. 2020. doi: 10.1109/JAS.2020.1003045
Citation: Ameer Hamza Khan, Zili Shao, Shuai Li, Qixin Wang and Nan Guan, "Which is the Best PID Variant for Pneumatic Soft Robots? An Experimental Study," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 451-460, Mar. 2020. doi: 10.1109/JAS.2020.1003045

Which is the Best PID Variant for Pneumatic Soft Robots? An Experimental Study

doi: 10.1109/JAS.2020.1003045
More Information
  • This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots. Fabricated using soft materials, soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction. However, due to structural flexibility and compliance, mathematical models for these soft robots are nonlinear with an infinite degree of freedom (DOF). Therefore, accurate position (or orientation) control and optimization of their dynamic response remains a challenging task. Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID. However, to the best of our knowledge, there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response, i.e., reduce overshoot and settling time. In this paper, we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results. Additionally, most of the existing work on model-free control in pneumatic soft-robotic literature use manually tuned parameters, which is a time-consuming, labor-intensive task. We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically. We presented results for both manual tuning and automatic tuning using the Ziegler–Nichols method and proposed algorithm, respectively. We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy. The experiment results show that for soft robots, the PID-controller essentially reduces to the PI controller. This behavior was observed in both manual and automatic tuning experiments; we also discussed a rationale for removing the derivative term.


  • loading
  • [1]
    V. Vikas, P. Grover, and B. A. Trimmer, “Model-free control framework for multi-limb soft robots,” in Proc. Intelligent Robots and Systems (IROS), IEEE/RSJ Int. Conf., pp. 1111–1116, IEEE, 2015.
    H.-T. Lin, G. G. Leisk, and B. Trimmer, “Goqbot: a caterpillar-inspired soft-bodied rolling robot,” Bioinspiration and Biomimetics, vol. 6, no. 2, pp. 026007, 2011.
    M. Calisti, M. Giorelli, G. Levy, B. Mazzolai, B. Hochner, C. Laschi, and P. Dario, “An octopus-bioinspired solution to movement and manipulation for soft robots,” Bioinspiration and Biomimetics, vol. 6, no. 3, pp. 036002, 2011.
    A. D. Marchese, C. D. Onal, and D. Rus, “Autonomous soft robotic fish capable of escape maneuvers using fluidic elastomer actuators,” Soft Robotics, vol. 1, no. 1, pp. 75–87, 2014. doi: 10.1089/soro.2013.0009
    P. Polygerinos, S. Lyne, Z. Wang, L. F. Nicolini, B. Mosadegh, G. M. Whitesides, and C. J. Walsh, “Towards a soft pneumatic glove for hand rehabilitation,” in Proc. Intelligent Robots and Systems (IROS), IEEE/RSJ Int. Conf., pp. 1512–1517, IEEE, 2013.
    F. Largilliere, V. Verona, E. Coevoet, M. Sanz-Lopez, J. Dequidt, and C. Duriez, “Real-time control of soft-robots using asynchronous finite element modeling,” in Proc. Robotics and Autom. (ICRA), IEEE Int. Conf., pp. 2550–2555, IEEE, 2015.
    F. Faure, C. Duriez, H. Delingette, J. Allard, B. Gilles, S. Marchesseau, H. Talbot, H. Courtecuisse, G. Bousquet, I. Peterlik, and S. Cotin, “Sofa: a multi-model framework for interactive physical simulation,” in Proc. Soft Tissue Biomechanical Modeling for Computer Assisted Surgery, pp. 283–321, Springer, 2012.
    C. Duriez, “Control of elastic soft robots based on real-time finite element method,” in Proc. Robotics and Autom. (ICRA), IEEE Int. Conf., pp. 3982–3987, IEEE, 2013.
    A. D. Marchese, R. Tedrake, and D. Rus, “Dynamics and trajectory optimization for a soft spatial fluidic elastomer manipulator,” The Int. J. Robotics Research, vol. 35, no. 8, pp. 1000–1019, 2016. doi: 10.1177/0278364915587926
    F. Renda, M. Giorelli, M. Calisti, M. Cianchetti, and C. Laschi, “Dynamic model of a multibending soft robot arm driven by cables,” IEEE Trans. Robotics, vol. 30, no. 5, pp. 1109–1122, 2014. doi: 10.1109/TRO.2014.2325992
    I. A. Gravagne, C. D. Rahn, and I. D. Walker, “Large deflection dynamics and control for planar continuum robots,” IEEE/ASME Trans. Mechatronics, vol. 8, no. 2, pp. 299–307, 2003. doi: 10.1109/TMECH.2003.812829
    F. Ni, A. Henning, K. Tang, and L. L. Cai, “Soft damper for quick stabilization of soft robotic actuator,” in Proc. Real-time Computing and Robotics (RCAR), IEEE Int. Conf., pp. 466–471, IEEE, 2016.
    Y. Wei, Y. H. Chen, T. Ren, Q. Chen, C. X. Yan, Y. Yang, and Y. T. Li, “A novel, variable stiffness robotic gripper based on integrated soft actuating and particle jamming,” Soft Robotics, vol. 3, no. 3, pp. 134–143, 2016. doi: 10.1089/soro.2016.0027
    Y. Q. Li, Y. H. Chen, T. Ren, and Y. Hu, “Passive and active particle damping in soft robotic actuators,” in Proc. Robotics and Autom. (ICRA), IEEE Int. Conf., pp. 1547–1552, IEEE, 2018.
    M. Luo, E. H. Skorina, W. J. Tao, F. C. Chen, S. Ozel, Y. N. Sun, and C. D. Onal, “Toward modular soft robotics: proprioceptive curvature sensing and sliding-mode control of soft bidirectional bending modules,” Soft Robotics, vol. 4, no. 2, pp. 117–125, 2017. doi: 10.1089/soro.2016.0041
    S. Terryn, J. Brancart, D. Lefeber, G. Van Assche, and B. Vanderborght, “Self-healing soft pneumatic robots,” Sci. Robot., vol. 2, pp. eaan4268, 2017. doi: 10.1126/scirobotics.aan4268
    G. Gerboni, A. Diodato, G. Ciuti, M. Cianchetti, and A. Menciassi, “Feedback control of soft robot actuators via commercial flex bend sensors,” IEEE/ASME Trans. Mechatronics, vol. 22, no. 4, pp. 1881–1888, Aug. 2017.
    K. H. Ang, G. Chong, and Y. Li, “Pid control system analysis, design, and technology,” IEEE Trans. Control Systems Technology, vol. 13, no. 4, pp. 559–576, 2005. doi: 10.1109/TCST.2005.847331
    K. J. Åström and T. Hägglund, PID Controllers: Theory, Design, and Tuning, vol. 2. Instrument Society of America Research Triangle Park, NC, 1995.
    K.-S. Tang, K. F. Man, G. R. Chen, and S. Kwong, “An optimal fuzzy pid controller,” IEEE Trans. Industrial Electronics, vol. 48, no. 4, pp. 757–765, 2001. doi: 10.1109/41.937407
    X. Luo, M. C. Zhou, S. Li, Y. N. Xia, Z.-H. You, Q. S. Zhu, and H. Leung, “Incorporation of efficient second-order solvers into latent factor models for accurate prediction of missing qos data,” IEEE Trans. Cybernetics, vol. 48, no. 4, pp. 1216–1228, 2017.
    X. Luo, M. C. Zhou, S. Li, and M. S. Shang, “An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications,” IEEE Trans. Industrial Informatics, vol. 14, no. 5, pp. 2011–2022, 2017.
    B. Tondu, “Modelling of the mckibben artificial muscle: a review,” J. Intelligent Material Systems and Structures, vol. 23, pp. 225–253, 2012. doi: 10.1177/1045389X11435435
    M. Doumit, A. T. Fahim, and M. Munro, “Analytical modeling and experimental validation of the braided pneumatic muscle,” IEEE Trans. Robotics, vol. 25, no. 6, pp. 1282–1291, 2009. doi: 10.1109/TRO.2009.2032959
    B. Mosadegh, P. Polygerinos, C. Keplinger, S. Wennstedt, R. F. Shepherd, U. Gupta, J. Shim, K. Bertoldi, C. J. Walsh, and G. M. Whitesides, “Pneumatic networks for soft robotics that actuate rapidly,” Adv. Funct. Mate., vol. 24, pp. 2163–2170, 2014.
    K. C. Galloway, P. Polygerinos, C. J. Walsh, and R. J. Wood, “Mechanically programmable bend radius for fiber-reinforced soft actuators,” in Proc. 16th Int. Conf. Adv. Robotics, pp. 1–6, IEEE, 2013.
    Y. Yang, Y. H. Chen, Y. T. Li, M. Z. Q. Chen, and Y. Wei, “Bioinspired robotic fingers based on pneumatic actuator and 3d printing of smart material,” Soft Robotics, vol. 4, no. 2, pp. 147–162, 2017. doi: 10.1089/soro.2016.0034
    X. Luo, J. P. Sun, Z. D. Wang, S. Li, and M. S. Shang, “Symmetric and nonnegative latent factor models for undirected, high-dimensional, and sparse networks in industrial applications,” IEEE Trans. Industrial Informatics, vol. 13, no. 6, pp. 3098–3107, 2017. doi: 10.1109/TII.2017.2724769
    X. Luo, M. C. Zhou, S. Li, Z. H. You, Y. N. Xia, and Q. S. Zhu, “A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method,” IEEE Trans. Neural Networks and Learning Systems, vol. 27, no. 3, pp. 579–592, 2015.
    R. V. Martinez, J. L. Branch, C. R. Fish, L. H. Jin, R. F. Shepherd, R. M. D. Nunes, Z. G. Suo, and G. M. Whitesides, “Robotic tentacles with three-dimensional mobility based on flexible elastomers,” Advanced Materials, vol. 25, no. 2, pp. 205–212, 2013. doi: 10.1002/adma.201203002
    C. D. Santina, R. K. Katzschmann, A. Bicchi, and D. Rus, “Dynamic control of soft robots interacting with the environment,” in Proc. 1st IEEE-RAS Int. Conf. Soft Robotics, At, Livomo, Italy, IEEE, Apr. 2018.
    E. H. Skorina, M. Luo, W. J. Tao, F. C. Chen, J. Fu, and C. D. Onal, “Adapting to flexibility: Model reference adaptive control of soft bending actuators,” IEEE Robotics and Autom. Letters, vol. 2, no. 2, pp. 964–970, 2017. doi: 10.1109/LRA.2017.2655572
    C. Keplinger, T. F. Li, R. Baumgartner, Z. G. Suo, and S. Bauer, “Harnessing snap-through instability in soft dielectrics to achieve giant voltage-triggered deformation,” Soft Matter, vol. 8, no. 2, pp. 285–288, 2012. doi: 10.1039/C1SM06736B
    I. A. Anderson, T. A. Gisby, T. G. McKay, B. M. O’Brien, and E. P. Calius, “Multi-functional dielectric elastomer artificial muscles for soft and smart machines,” J. Applied Physics, vol. 112, no. 4, pp. 041101, 2012. doi: 10.1063/1.4740023
    J. T. B. Overvelde, T. Kloek, J. J. A. D’haen, and K. Bertoldi, “Amplifying the response of soft actuators by harnessing snap-through instabilities,” Proc. National Academy of Sciences, vol. 112, no. 35, pp. 10863–10868, 2015. doi: 10.1073/pnas.1504947112
    M. Loepfe, C. M. Schumacher, U. B. Lustenberger, and W. J. Stark, “An untethered, jumping roly-poly soft robot driven by combustion,” Soft Robotics, vol. 2, no. 1, pp. 33–41, 2015. doi: 10.1089/soro.2014.0021
    W. Felt, K. Y. Chin, and C. D. Remy, “Contraction sensing with smart braid mckibben muscles,” IEEE/ASME Trans. Mechatronics, vol. 21, no. 3, pp. 1201–1209, 2016. doi: 10.1109/TMECH.2015.2493782
    Y.-L. Park, B.-R. Chen, C. Majidi, R. J. Wood, R. Nagpal, and E. C. Goldfield, “Active modular elastomer sleeve for soft wearable assistance robots,” in Proc. Intelligent Robots and Systems (IROS), IEEE/RSJ Int. Conf., pp. 1595–1602, IEEE, 2012.
    Y.-L. Park, C. Majidi, R. Kramer, P. Bérard, and R. J. Wood, “Hyperelastic pressure sensing with a liquid-embedded elastomer,” J. Micromechanics and Microengineering, vol. 20, no. 12, pp. 125029–125034, 2010. doi: 10.1088/0960-1317/20/12/125029
    A. J. Veale, I. A. Anderson, and S. Q. Xie, “The smart peano fluidic muscle: a low profile flexible orthosis actuator that feels pain,” in Proc. SPIE Smart Structures and Materials, International Society for Optics and Photonics, 2015.
    H. Lin, F. Guo, F. F. Wang, and Y.-B. Jia, “Picking up a soft 3d object by “feeling” the grip,” The Int. J. Robotics Research, vol. 34, no. 11, pp. 1361–1384, 2015. doi: 10.1177/0278364914564232
    X. Luo, H. Wu, H. Q. Yuan, and M. C. Zhou, “Temporal pattern-aware qos prediction via biased non-negative latent factorization of tensors,” IEEE Trans. Cybernetics, Apr. 2019. doi: 10.1109/TCYB.2019.2903736
    I. Galiana, F. L. Hammond, R. D. Howe, and M. B. Popovic, “Wearable soft robotic device for post-stroke shoulder rehabilitation: Identifying misalignments,” in Proc. Intelligent Robots and Systems (IROS), IEEE/RSJ Int. Conf., pp. 317–322, IEEE, 2012.
    M. Z. Zhu, W. L. Xu, and L. K. Cheng, “Esophageal peristaltic control of a soft-bodied swallowing robot by the central pattern generator,” IEEE/ASME Trans. Mechatronics, vol. 22, no. 1, pp. 91–98, 2017. doi: 10.1109/TMECH.2016.2609465
    H. In, U. Jeong, H. Lee, and K.-J. Cho, “A novel slack-enabling tendon drive that improves efficiency, size, and safety in soft wearable robots,” IEEE/ASME Trans. Mechatronics, vol. 22, no. 1, pp. 59–70, 2017. doi: 10.1109/TMECH.2016.2606574
    X. Luo, M. C. Zhou, Y. N. Xia, Q. S. Zhu, A. C. Ammari, and A. Alabdulwahab, “Generating highly accurate predictions for missing qos data via aggregating nonnegative latent factor models,” IEEE Trans. Neural Networks and Learning Systems, vol. 27, no. 3, pp. 524–537, 2015.
    X. Luo, M. C. Zhou, Y. N. Xia, and Q. S. Zhu, “An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems,” IEEE Trans. Industrial Informatics, vol. 10, no. 2, pp. 1273–1284, 2014. doi: 10.1109/TII.2014.2308433
    Y. F. Hao, Z. Y. Gong, Z. X. Xie, S. Y. Guan, X. B. Yang, Z. Y. Ren, T. M. Wang, and L. Wen, “Universal soft pneumatic robotic gripper with variable effective length,” in Proc. 35th Chinese Control Conf. (CCC), pp. 6109–6114, IEEE, 2016.
    Z. Bingul and O. Karahan, “Comparison of pid and fopid controllers tuned by pso and abc algorithms for unstable and integrating systems with time delay,” Optimal Control Applications and Methods, vol. 39, no. 4, pp. 1431–1450, 2018. doi: 10.1002/oca.2419
    Z. Bingul and O. Karahan, “A novel performance criterion approach to optimum design of pid controller using cuckoo search algorithm for avr system,” J. Franklin Institute, vol. 355, no. 13, pp. 5534–5559, 2018. doi: 10.1016/j.jfranklin.2018.05.056
    J. T. Agee, Z. Bingul, and S. Kizir, “Tip trajectory control of a flexible-link manipulator using an intelligent proportional integral (ipi) controller,” Trans. Institute of Measurement and Control, vol. 36, no. 5, pp. 673–682, 2014. doi: 10.1177/0142331213518577
    S. J. Wright, “Coordinate descent algorithms,” Mathematical Programming, vol. 151, no. 1, pp. 3–34, 2015. doi: 10.1007/s10107-015-0892-3
    D. P. Holland, E. J. Park, P. Polygerinos, G. J. Bennett, and C. J. Walsh, “The soft robotics toolkit: shared resources for research and design,” Soft Robotics, vol. 1, no. 3, pp. 224–230, 2014. doi: 10.1089/soro.2014.0010
    “Smooth-on inc.” https://www.smooth-on.com/tb/files/DRAGON_SKIN_SERIES_TB.pdf. Accessed: Aug. 28, 2018.


    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(10)  / Tables(3)

    Article Metrics

    Article views (2263) PDF downloads(129) Cited by()


    • Comprehensive experimental study on the performance of PID for soft robots.
    • Comparison between manual and automatic PID parameter tuning algorithms.
    • Identifying the peculiarity of PID for soft robots as compared to rigid robots.
    • Discussion on optimal strategy to tune PID parameters.


    DownLoad:  Full-Size Img  PowerPoint