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Contact force cancelation in robot impedance control by target impedance modification

Published online by Cambridge University Press:  06 February 2023

An-Chyau Huang*
Affiliation:
Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC
Kun-Ju Lee
Affiliation:
Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC
Wei-Lin Du
Affiliation:
Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC
*
*Corresponding author. E-mail: achuang@mail.ntust.edu.tw

Abstract

A force sensorless impedance controller is proposed in this paper for robot manipulators without using force estimators. From the observation of the impedance control law, the force feedback term can be canceled if the inertia matrix in the target impedance is the same as the robot inertia matrix. However, the inertia matrix in the target impedance is almost always a constant matrix, while the robot inertia matrix is a function of the robot configuration, and hence, they may not be identical in general. A modification of the coefficient matrix for the contact force term in the target impedance is suggested in this paper to enable cancelation of the force feedback term in the impedance control law so that a force sensorless impedance controller without using force estimators can be obtained. The tracking performance in the free space phase and the motion trajectory in the compliant motion phase of the new design are almost the same as those in the traditional impedance control. Modification of the inertia matrix in the target impedance will result in small variations of the contact force which is acceptable in practical applications. For robot manipulators containing uncertainties, an adaptive version of the new controller is also developed in this paper to give satisfactory performance without the need for force sensors. Rigorous mathematical justification in closed-loop stability is given in detail, and computer simulations are performed to verify the efficacy of the proposed design.

Type
Research Article
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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References

Raibert, M. H. and Craig, J. J., “Hybrid position/force control of manipulators,” ASME J. Dyn. Syst. Meas. Control 103(2), 126133 (1981).CrossRefGoogle Scholar
Craig, J. J.. Introduction to Robotics: Mechanics and Control (3rd edition, Pearson Prentice Hall, Upper Saddle River, NJ, 2005).Google Scholar
Huang, A. C. and Chien, M. C.. Adaptive Control of Robot Manipulators – A Unified Regressor-Free Approach (World Scientific, 2010).CrossRefGoogle Scholar
Hogan, N., “Impedance control: An approach to manipulation: Part1—theory, Part2—implementation, Part3—an approach to manipulation,” ASME J. Dyn. Syst. Meas. Control 107(1), 124 (1985).CrossRefGoogle Scholar
Spong, M. W., Hutchinson, S. and Vidyasagar, M.. Robot Modeling and Control (John Wiley & Sons, Hoboken, NJ, 2006).Google Scholar
Song, P., Yu, Y. and Zhang, X., “A tutorial survey and comparison of impedance control on robotic manipulation,” Robotica 37(5), 801836 (2019).CrossRefGoogle Scholar
Al-Shuka, H. F. N., Leonhardt, S., Zhu, W. H., Song, R., Ding, C. and Li, Y., “Active impedance control of bioinspired motion robotic manipulators: An overview,” Appl. Bionics Biomech. 2018, 8203054 (2018).CrossRefGoogle ScholarPubMed
Murakami, T., Yu, F. and Ohnishi, K., “Torque sensorless control in multi-degree-of-freedom manipulator,” IEEE Trans. Ind. Electron. 40(2), 259265 (1993).CrossRefGoogle Scholar
Eom, K. S., Suh, I. H., Chung, W. K. and Oh, S. R., “Disturbance observer-based force control of robot manipulator without force sensor,” IEEE Int. Conf. Robot. Autom. 4, 30123017 (1998).CrossRefGoogle Scholar
Alcocer, A., Robertsson, A., Valera, A. and Johansson, R., “Force estimation and control in robot manipulators,” IFAC Proc. 36(17), 5560 (2003).CrossRefGoogle Scholar
Tungpataratanawong, S., Ohishi, K. and Miyazaki, T., “Force sensorless workspace impedance control considering resonant vibration of industrial robot,” 31st Annu. Proc. IEEE Conf. Ind. Electron. Soc. 18781883 (Raleigh, NC, 2005).Google Scholar
Erden, M. S. and Tomiyama, T., “Human-intent detection and physically interactive control of a robot without force sensors,” IEEE Trans. Robot. 26(2), 370382 (2010).CrossRefGoogle Scholar
Van Damme, M., Beyl, P., Vanderborght, B., Grosu, V., Van Ham, R., Vanderniepen, I., Matthys, A. and Lefeber, D., “Estimating robot end-effector force from noisy actuator torque measurements,” 2011 IEEE Int. Conf. Robot. Autom., 11081113 (Shanghai, 2011).CrossRefGoogle Scholar
Tachi, S., Sakaki, T., Arai, H., Nishizawa, S. and Pelaez-Polo, J. F., “Impedance control of a direct-drive manipulator without using force sensors,” Adv. Robot. 5(2), 183205 (2012).CrossRefGoogle Scholar
Wahrburg, A., Morara, E., Cesari, G., Matthias, B. and Ding, H., “Cartesian contact force estimation for robotic manipulators using Kalman filters and the generalized momentum,” 2015 IEEE Int. Conf. Autom. Sci. Eng., 12301235 (Gothenburg, 2015).CrossRefGoogle Scholar
Ragaglia, M., Zanchettin, A. M., Bascetta, L. and Rocco, P., “Accurate sensorless lead-through programming for lightweight robots in structured environments,” Robot. Comput. Integr. Manuf. 39, 921 (2016).CrossRefGoogle Scholar
Choi, J. H., Kwak, J. H., An, J. and Oh, S., “Force sensorless multi-functional impedance control of rehabilitation robot,” IFAC-PapersOnLine 50(1), 1207712082 (2017).CrossRefGoogle Scholar
Yuan, F., Qian, Y., Gao, L., Yuan, Z. and Wan, W., “Position-based impedance force controller with sensorless force estimation,” Assembly Autom. 39(3), 489496 (2019).CrossRefGoogle Scholar
Han, L., Xu, W., Li, B. and Kang, P., “Collision detection and coordinated compliance control for a dual-arm robot without force/torque sensing based on momentum observer,” IEEE/ASME Trans. Mechatron. 24(5), 22612272 (2019).CrossRefGoogle Scholar
Zeng, F., Xiao, J. and Liu, H., “Force/torque sensorless compliant control strategy for assembly tasks using a 6-DOF collaborative robot,” IEEE Access 7, 108795108805 (2019).CrossRefGoogle Scholar
Dong, A., Du, Z. and Yan, Z., “A sensor interaction forces estimator for bilateral teleoperation system based on online sparse Gaussian process regression,” Mech. Mach. Theory 143, 103620 (2020).CrossRefGoogle Scholar
Peng, G., Yang, C., He, W. and Chen, C. L. P., “Force sensorless admittance control with neural learning for robots with actuator saturation,” IEEE Trans. Ind. Electron. 67(4), 31383148 (2020).CrossRefGoogle Scholar
Peng, G., Chen, C. L. P., He, W. and Yang, C., “Neural learning based force sensorless admittance control for robots with input deadzone,” IEEE Trans. Ind. Electron. 68(6), 51845196 (2021).CrossRefGoogle Scholar
Yang, C., Peng, G., Cheng, L., Na, J. and Li, Z., “Force sensorless admittance control for teleoperation of uncertain robot manipulator using neural networks,” IEEE Trans. Syst. Man Cybern. Syst. 51(5), 32823292 (2021).CrossRefGoogle Scholar
Roveda, L., Shahid, A. A., Iannacci, N. and Piga, D., “Sensorless optimal interaction control exploiting environment stiffness estimation,” IEEE Trans. Control Syst. Technol. 30(1), 218233 (2022).CrossRefGoogle Scholar
Chien, M. C. and Huang, A. C., “Adaptive impedance control of robot manipulators based on function approximation technique,” Robotica 22(4), 395403 (2004).CrossRefGoogle Scholar