SCIENCE CHINA Information Sciences, Volume 63 , Issue 9 : 192205(2020) https://doi.org/10.1007/s11432-019-2639-6

Learning impedance control of robots with enhanced transient and steady-state control performances

More info
  • ReceivedMar 30, 2019
  • AcceptedAug 5, 2019
  • PublishedJul 24, 2020



This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61720106012, 61873268, 61633016), Beijing Natural Science Foundation (Grant No. L182060), Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB32040000), and China Postdoctoral Science Foundation (Grant No. 2019T120405).


[1] Yu J P, Zhao L, Yu H S. Barrier Lyapunov functions-based command filtered output feedback control for full-state constrained nonlinear systems. Automatica, 2019, 105: 71-79 CrossRef Google Scholar

[2] Abdelatti M, Yuan C Z, Zeng W. Cooperative deterministic learning control for a group of homogeneous nonlinear uncertain robot manipulators. Sci China Inf Sci, 2018, 61: 112201 CrossRef Google Scholar

[3] He W, Yin Z, Sun C Y. Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function.. IEEE Trans Cybern, 2017, 47: 1641-1651 CrossRef PubMed Google Scholar

[4] Yu J P, Shi P, Zhao L. Finite-time command filtered backstepping control for a class of nonlinear systems. Automatica, 2018, 92: 173-180 CrossRef Google Scholar

[5] Hogan N. Impedance Control: An Approach to Manipulation: Part I-Theory. J Dynamic Syst Measurement Control, 1985, 107: 1-7 CrossRef Google Scholar

[6] Cui J, Lai M, Chu Z Y. Experiment on impedance adaptation of under-actuated gripper using tactile array under unknown environment. Sci China Inf Sci, 2018, 61: 122202 CrossRef Google Scholar

[7] Chu Z Y, Yan S B, Hu J. Impedance Identification Using Tactile Sensing and Its Adaptation for an Underactuated Gripper Manipulation. Int J Control Autom Syst, 2018, 16: 875-886 CrossRef Google Scholar

[8] Sun T R, Peng L, Cheng L. Stability-Guaranteed Variable Impedance Control of Robots Based on Approximate Dynamic Inversion. IEEE Trans Syst Man Cybern Syst, 2019, : 1-8 CrossRef Google Scholar

[9] Zhang F, Hou Z G, Cheng L. IEEE Trans Human-Mach Syst, 2016, 46: 761-768 CrossRef Google Scholar

[10] Saglia J A, Tsagarakis N G, Dai J S. Control Strategies for Patient-Assisted Training Using the Ankle Rehabilitation Robot (ARBOT). IEEE/ASME Trans Mechatron, 2013, 18: 1799-1808 CrossRef Google Scholar

[11] Li Z J, Zhao S, Duan J. Human Cooperative Wheelchair With Brain-Machine Interaction Based on Shared Control Strategy. IEEE/ASME Trans Mechatron, 2017, 22: 185-195 CrossRef Google Scholar

[12] Wojtara T, Uchihara M, Murayama H. Human-robot collaboration in precise positioning of a three-dimensional object. Automatica, 2009, 45: 333-342 CrossRef Google Scholar

[13] Vukobratovic M, Surdilovic, Ekalo Y, et al. Dynamics and Robust Control of Robot-Environment Interaction. Singapore: World Scientific, 2009. Google Scholar

[14] Jung S, Hsia T C. Neural network impedance force control of robot manipulator. IEEE Trans Ind Electron, 1998, 45: 451-461 CrossRef Google Scholar

[15] Jamwal P K, Hussain S, Ghayesh M H. Impedance Control of an Intrinsically Compliant Parallel Ankle Rehabilitation Robot. IEEE Trans Ind Electron, 2016, 63: 3638-3647 CrossRef Google Scholar

[16] He W, Dong Y, Sun C Y. Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation. IEEE Trans Syst Man Cybern Syst, 2016, 46: 334-344 CrossRef Google Scholar

[17] Sharifi M, Behzadipour S, Salarieh H. Cooperative modalities in robotic tele-rehabilitation using nonlinear bilateral impedance control. Control Eng Practice, 2017, 67: 52-63 CrossRef Google Scholar

[18] Sharifi M, Behzadipour S, Vossoughi G. Nonlinear model reference adaptive impedance control for human-robot interactions. Control Eng Practice, 2014, 32: 9-27 CrossRef Google Scholar

[19] Chan S P, Yao B, Gao W B, et al. Robust impedance control of robot manipulators. Int J Robot Autom, 1991, 6: 220--227. Google Scholar

[20] Mohammadi H, Richter H. Robust tracking/impedance control: application to prosthetics. In: Proceedings of American Control Conference, 2015. 2673--2678. Google Scholar

[21] Cheah C C, Wang D W. Learning impedance control for robotic manipulators. IEEE Trans Robot Automat, 1998, 14: 452-465 CrossRef Google Scholar

[22] Li X, Liu Y H, Yu H Y. Iterative learning impedance control for rehabilitation robots driven by series elastic actuators. Automatica, 2018, 90: 1-7 CrossRef Google Scholar

[23] Liang X Q, Zhao H, Li X F. Force tracking impedance control with unknown environment via an iterative learning algorithm. Sci China Inf Sci, 2019, 62: 050215 CrossRef Google Scholar

[24] Li Y N, Ge S S. Human-Robot Collaboration Based on Motion Intention Estimation. IEEE/ASME Trans Mechatron, 2014, 19: 1007-1014 CrossRef Google Scholar

[25] Li Z J, Huang Z C, He W. Adaptive Impedance Control for an Upper Limb Robotic Exoskeleton Using Biological Signals. IEEE Trans Ind Electron, 2017, 64: 1664-1674 CrossRef Google Scholar

[26] Sun T, Peng L, Cheng L. Composite Learning Enhanced Robot Impedance Control.. IEEE Trans Neural Netw Learning Syst, 2019, : 1-8 CrossRef PubMed Google Scholar

[27] He W, Dong Y. Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning.. IEEE Trans Neural Netw Learning Syst, 2018, 29: 1174-1186 CrossRef PubMed Google Scholar

[28] Li X, Pan Y P, Chen G. Multi-modal control scheme for rehabilitation robotic exoskeletons. Int J Robot Res, 2017, 36: 759-777 CrossRef Google Scholar

[29] Pan Y P, Yu H Y. Composite Learning From Adaptive Dynamic Surface Control. IEEE Trans Automat Contr, 2016, 61: 2603-2609 CrossRef Google Scholar

[30] Pan Y P, Yu H Y. Composite learning robot control with guaranteed parameter convergence. Automatica, 2018, 89: 398-406 CrossRef Google Scholar

[31] Wang C, Peng L, Luo L C, et al. Genetic algorithm based dynamics modeling and control of a parallel rehabilitation robot. In: Proceedings of 2018 IEEE Congress on Evolutionary Computation, 2018. 1--7. Google Scholar

[32] Spong M W, Vidyasagar M. Robot Dynamics and Control. New York: John Wiley & Sons, 2008. Google Scholar