Abstract
Compliant interaction control is a key technology for robots performing contact-rich manipulation tasks. The design of the compliant controller needs to consider the robot hardware because complex control algorithms may not be compatible with the hardware performance, especially for some industrial robots with low bandwidth sensors. This paper focuses on effective and easy-to-use compliant control algorithms for position/velocity-controlled robots. Inspired by human arm stiffness adaptation behavior, a novel variable target stiffness (NVTS) admittance control strategy is proposed for adaptive force tracking, in which a proportional integral derivative (PID) variable stiffness law is designed to update the stiffness coefficient of the admittance function by the force and position feedback. Meanwhile, its stability and force-tracking capability are theoretically proven. In addition, an impact compensator (Impc) is integrated into the NVTS controller to enhance its disturbance-suppression capability when the robot is subjected to strong vibration disturbances in complicated surface polishing tasks. The proposed controllers are validated through four groups of experimental tests using different robots and the corresponding results demonstrate that they have high-accuracy tracking capability and strong adaptability in unknown environments.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Villani L, De Schutter J. Force control. In: Springer Handbook of Robotics. Cham: Springer, 2016. 195–220
Bernier C. Polishing Robots: Automating the Finishing Process. 2022. https://howtorobot.com/expert-insight/polishing-robots
Chen H, Liu Y. Robotic assembly automation using robust compliant control. Robotics Comput-Integrated Manuf, 2013, 29: 293–300
Schumacher M, Wojtusch J, Beckerle P, et al. An introductory review of active compliant control. Robotics Autonomous Syst, 2019, 119: 185–200
Whitney D E. Force feedback control of manipulator fine motions. J Dyn Syst Measure Control, 1997, 99: 91–97
Raibert M H, Craig J J. Hybrid position/force control of manipulators. J Dyn Syst Measure Control, 1981, 103: 126–133
Chiaverini S, Sciavicco L. The parallel approach to force/position control of robotic manipulators. IEEE Trans Robot Automat, 1993, 9: 361–373
Karayiannidis Y, Rovithakis G, Doulgeri Z. Force/position tracking for a robotic manipulator in compliant contact with a surface using neuroadaptive control. Automatica, 2007, 43: 1281–1288
Hogan N. Impedance control: An approach to manipulation: Part I-Theory. J Dyn Syst Measure Control, 1985, 107: 1–7
Ott C, Mukherjee R, Nakamura Y. Unified impedance and admittance control. In: Proceedings of the IEEE International Conference on Robotics and Automation. Anchorage, 2010. 554–561
Seul Jung, Hsia T C. Neural network impedance force control of robot manipulator. IEEE Trans Ind Electron, 1998, 45: 451–461
Valency T, Zacksenhouse M. Accuracy/robustness dilemma in impedance control. J Dyn Syst Measure Control, 2003, 125: 310–319
Natale C. Interaction Control of Robot Manipulators: Six Degrees-of Freedom Tasks. Vol. 3. Berlin, Heidelberg: Springer-Verlag, 2003
Duan J, Gan Y, Chen M, et al. Adaptive variable impedance control for dynamic contact force tracking in uncertain environment. Robotics Autonomous Syst, 2018, 102: 54–65
Jung S, Hsia T C, Bonitz R G. Force tracking impedance control for robot manipulators with an unknown environment: theory, simulation, and experiment. Int J Robotics Res, 2001, 20: 765–774
Zhang X, Liu J, Gao Q, et al. Adaptive robust decoupling control of multi-arm space robots using time-delay estimation technique. Nonlinear Dyn, 2020, 100: 2449–2467
Lee C H, Wang W C. Robust adaptive position and force controller design of robot manipulator using fuzzy neural networks. Nonlinear Dyn, 2016, 85: 343–354
Roveda L, Pedrocchi N, Tosatti L M. Exploiting impedance shaping approaches to overcome force overshoots in delicate interaction tasks. Int J Adv Robot Syst, 2016, 13: 1–11
Seraji H. Adaptive admittance control: An approach to explicit force control in compliant motion. In: Proceedings of the IEEE International Conference On Robotics And Automation. San Diego, 1994. 2705–2712
Lee K, Buss M. Force tracking impedance control with variable target stiffness. IFAC Proc Volumes, 2008, 41: 6751–6756
Roveda L, Castaman N, Franceschi P, et al. A control framework definition to overcome position/interaction dynamics uncertainties in force-controlled tasks. In: Proceedings of the IEEE International Conference on Robotics and Automation. Paris, 2020. 6819–6825
Balatti P, Kanoulas D, Tsagarakis N, et al. A method for autonomous robotic manipulation through exploratory interactions with uncertain environments. Auton Robot, 2020, 44: 1395–1410
Ferraguti F, Secchi C, Fantuzzi C. A tank-based approach to impedance control with variable stiffness. In: Proceedings of the IEEE International Conference On Robotics and Automation. Karlsruhe, 2013. 4948–4953
Ding L, Xing H, Gao H, et al. VDC-based admittance control of multi-DOF manipulators considering joint flexibility via hierarchical control framework. Control Eng Pract, 2022, 124: 105186
Peng G, Yang C, He W, et al. Force sensorless admittance control with neural learning for robots with actuator saturation. IEEE Trans Ind Electron, 2019, 67: 3138–3148
He W, Xue C, Yu X, et al. Admittance-based controller design for physical human-robot interaction in the constrained task space. IEEE Trans Automat Sci Eng, 2020, 17: 1937–1949
Li Y, Ge S S. Impedance learning for robots interacting with unknown environments. IEEE Trans Contr Syst Technol, 2013, 22: 1422–1432
Hamedani M H, Sadeghian H, Zekri M, et al. Intelligent impedance control using wavelet neural network for dynamic contact force tracking in unknown varying environments. Control Eng Pract, 2021, 113: 104840
He W, Dong Y. Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Trans Neural Netw Learn Syst, 2017, 29: 1174–1186
Roveda L, Riva D, Bucca G, et al. Sensorless optimal switching impact/force controller. IEEE Access, 2021, 9: 158167
Roveda L, Veerappan P, Maccarini M, et al. A human-centric framework for robotic task learning and optimization. J Manuf Syst, 2023, 67: 68–79
Ajoudani A, Tsagarakis N, Bicchi A. Tele-impedance: Teleoperation with impedance regulation using a body-machine interface. Int J Robotics Res, 2012, 31: 1642–1656
Yang C, Ganesh G, Haddadin S, et al. Human-like adaptation of force and impedance in stable and unstable interactions. IEEE Trans Robot, 2011, 27: 918–930
Yang C, Zeng C, Fang C, et al. A DMPs-based framework for robot learning and generalization of humanlike variable impedance skills. IEEE ASME Trans Mechatron, 2018, 23: 1193–1203
Wu Y, Zhao F, Tao T, et al. A framework for autonomous impedance regulation of robots based on imitation learning and optimal control. IEEE Robot Autom Lett, 2020, 6: 127–134
Ott C. Cartesian Impedance Control of Redundant and Flexible-Joint Robots. Berlin, Heidelberg: Springer, 2008
Zhang X, Liu J, Tong Y, et al. Attitude decoupling control of semifloating space robots using time-delay estimation and supertwisting control. IEEE Trans Aerosp Electron Syst, 2021, 57: 4280–4295
Swevers J, Verdonck W, De Schutter J. Dynamic model identification for industrial robots. IEEE Control Syst Mag, 2007, 27: 58–71
Åström K J, Hägglund T. The future of PID control. Control Eng Pract, 2001, 9: 1163–1175
Katsuhiko O. Modern Control Engineering. Vol. 5. Upper Saddle River: Prentice Hall, 2010
Winkler A, Suchy J. Identification and controller design for the inverted pendulum actuated by a position controlled robot. In: Proceedings of the International Conference on Methods & Models in Automation & Robotics. Miedzyzdroje, 2013. 285–293
Kang G, Oh H S, Seo J K, et al. Variable admittance control of robot manipulators based on human intention. IEEE ASME Trans Mechatron, 2019, 24: 1023–1032
Roveda L, Iannacci N, Vicentini F, et al. Optimal impedance force-tracking control design with impact formulation for interaction tasks. IEEE Robot Autom Lett, 2015, 1: 130–136
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62103407, 52075530, and 52175272), and the State Key Laboratory of Robotics Foundation (Grant No. Y91Z0303).
Rights and permissions
About this article
Cite this article
Zhang, X., Zhou, H., Liu, J. et al. A practical PID variable stiffness control and its enhancement for compliant force-tracking interactions with unknown environments. Sci. China Technol. Sci. 66, 2882–2896 (2023). https://doi.org/10.1007/s11431-022-2436-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11431-022-2436-y