Stability-Guaranteed Variable Impedance Control of Robots Based on Approximate Dynamic Inversion

Variable impedance control has been considered as one of the most important compliant control approaches for its abilities in improving compliance, safety, and efficiency in robot-environment interaction. However, existing variable impedance controllers have deficits in stability guarantee. This article proposes a stability-guaranteed variable impedance control approach for robots with modeling uncertainties based on approximate dynamic inversion (ADI). Novel constraints on variable impedance profiles are given to guarantee the exponential stability of the desired variable impedance dynamics. An ADI-based impedance control law is designed to achieve the desired variable impedance dynamics through the convergence of a variable impedance error. Based on the extended Tikhonovs theorem, it is proven that the closed-loop control system has semiglobal practical exponential stability. The proposed impedance controller can be implemented in a PID form and is appealing for its simple structure, easy implementation, and control stability guarantee. The effectiveness of the proposed variable impedance controller is illustrated by an illustrative example taken on a five-bar parallel robot.

[1]  Mergen H. Ghayesh,et al.  Impedance Control of an Intrinsically Compliant Parallel Ankle Rehabilitation Robot , 2016, IEEE Transactions on Industrial Electronics.

[2]  Aude Billard,et al.  Stability Considerations for Variable Impedance Control , 2016, IEEE Transactions on Robotics.

[3]  Haoyong Yu,et al.  Efficient PID Tracking Control of Robotic Manipulators Driven by Compliant Actuators , 2019, IEEE Transactions on Control Systems Technology.

[4]  Mojtaba Sharifi,et al.  Cooperative modalities in robotic tele-rehabilitation using nonlinear bilateral impedance control , 2017 .

[5]  Aude Billard,et al.  Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction , 2014, IEEE Transactions on Haptics.

[6]  Antonio Bicchi,et al.  Coordination Control of a Dual-Arm Exoskeleton Robot Using Human Impedance Transfer Skills , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[7]  Seul Jung,et al.  Neural network impedance force control of robot manipulator , 1998, IEEE Trans. Ind. Electron..

[8]  Zhicong Huang,et al.  Adaptive Impedance Control for an Upper Limb Robotic Exoskeleton Using Biological Signals , 2017, IEEE Transactions on Industrial Electronics.

[9]  Bruno Siciliano,et al.  Variable Impedance Control of Redundant Manipulators for Intuitive Human–Robot Physical Interaction , 2015, IEEE Transactions on Robotics.

[10]  Shuzhi Sam Ge,et al.  An Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control for Robust Fault Tolerant Control of Robot Manipulators , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[11]  Chao Zeng,et al.  A Learning Framework of Adaptive Manipulative Skills From Human to Robot , 2019, IEEE Transactions on Industrial Informatics.

[12]  Neville Hogan,et al.  Impedance Control: An Approach to Manipulation , 1984, 1984 American Control Conference.

[13]  Jonathan P. How,et al.  Proportional-Integral Controllers for Minimum-Phase Nonaffine-in-Control Systems , 2010, IEEE Transactions on Automatic Control.

[14]  Danwei Wang,et al.  Learning impedance control for robotic manipulators , 1998, IEEE Trans. Robotics Autom..

[15]  Changyin Sun,et al.  Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[16]  Nikolaos G. Tsagarakis,et al.  Tele-impedance: Teleoperation with impedance regulation using a body–machine interface , 2012, Int. J. Robotics Res..

[17]  Hou Zeng-guang,et al.  Synchronous Active Interaction Control and Its Implementation for a Rehabilitation Robot , 2015 .

[18]  Sethu Vijayakumar,et al.  Transferring Human Impedance Behavior to Heterogeneous Variable Impedance Actuators , 2013, IEEE Transactions on Robotics.

[19]  Long Cheng,et al.  Composite Learning Enhanced Robot Impedance Control , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[20]  Bin Zhou,et al.  On asymptotic stability of linear time-varying systems , 2016, Autom..

[21]  Chun-Yi Su,et al.  Human-Inspired Control of Dual-Arm Exoskeleton Robots With Force and Impedance Adaptation , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[22]  Shuzhi Sam Ge,et al.  Human–Robot Collaboration Based on Motion Intention Estimation , 2014, IEEE/ASME Transactions on Mechatronics.

[23]  Changyin Sun,et al.  Adaptive Neural Network Control of Biped Robots , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[24]  Stefan Schaal,et al.  Learning variable impedance control , 2011, Int. J. Robotics Res..

[25]  Chen Xiangjie,et al.  Design for TCSC fuzzy immunity PID impedance controller , 2010, 2010 The 2nd Conference on Environmental Science and Information Application Technology.

[26]  Xiang Li,et al.  Iterative learning impedance control for rehabilitation robots driven by series elastic actuators , 2018, Autom..

[27]  Jonathan P. How,et al.  On Approximate Dynamic Inversion and Proportional-Integral control , 2009, 2009 American Control Conference.

[28]  Yahui Gan,et al.  Adaptive variable impedance control for dynamic contact force tracking in uncertain environment , 2018, Robotics Auton. Syst..

[29]  Beibei Ren,et al.  UDE-Based Variable Impedance Control of Uncertain Robot Systems , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.