Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-25T05:14:04.142Z Has data issue: false hasContentIssue false

Joint friction estimation for walking bipeds

Published online by Cambridge University Press:  05 November 2014

Iyad Hashlamon*
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey E-mail: [email protected]
Kemalettin Erbatur*
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul, Turkey E-mail: [email protected]
*
*Corresponding authors. E-mail: [email protected], [email protected]
*Corresponding authors. E-mail: [email protected], [email protected]

Summary

This paper proposed a new approach for the joint friction estimation of non-slipping walking biped robots. The proposed approach is based on the combination of a measurement-based strategy and a model-based method. The former is used to estimate the joint friction online when the foot is in contact with the ground, while the latter adopts a friction model to represent the joint friction when the leg is swinging. The measurement-based strategy utilizes the measured ground reaction forces (GRF) and the readings of an inertial measurement unit (IMU) located at the robot body. Based on these measurements, the joint angular accelerations and the body attitude and velocity are estimated. The aforementioned measurements and estimates are used in a reduced dynamical model of the biped. However, when the leg is swinging, this strategy is inapplicable. Therefore, a friction model is adopted. Its parameters are identified adaptively using the estimated online friction whenever the foot is in contact. The estimated joint friction is used in the feedback torque control signal. The proposed approach is validated using the full-dynamics of 12-DOF biped model. By using this approach, the robot center of mass (CoM) position error is reduced by 10% which demonstrates the effectiveness of this approach.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Hirukawa, H., “Walking biped humanoids that perform manual labour,” Phil. Trans. A Math. Phys. Eng. Sci. 365, 6577 (Jan. 15, 2007).Google Scholar
2.Nishiyama, T., Hoshino, H., Sawada, K., Baba, A., Sekine, T., Yamada, W., Terasawa, A., Nakajima, R., Tokunaga, Y. and Yoneda, M., “Communication Agent Embedded in Humanoid Robot,” SICE Annual Conference (2003), vol. 2, pp. 15141519.Google Scholar
3.Raibert, M. H. and Tello, E. R., “Legged robots that balance,” IEEE Expert 1, 8989 (1986).Google Scholar
4.Koeda, M., Uda, Y., Sugiyama, S. and Yoshikawa, T., “Shuffle Turn and Translation of Humanoid Robots,” IEEE International Conference on Robotics and Automation (ICRA), (2011) pp. 593–598.Google Scholar
5.Miura, K., Nakaoka, S., Morisawa, M., Kanehiro, F., Harada, K. and Kajita, S., “Analysis on a Friction based “Twirl” for Biped Robots,” IEEE International Conference on Robotics and Automation (ICRA), (2010) pp. 4249–4255.Google Scholar
6.Kajita, S., Morisawa, M., Miura, K., Nakaoka, S., Harada, K., Kaneko, K., Kanehiro, F. and Yokoi, K., “Biped Walking Stabilization based on Linear Inverted Pendulum Tracking,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2010) pp. 4489–4496.Google Scholar
7.Liu, T., Inoue, Y. and Shibata, K., “A wearable ground reaction force sensor system and its application to the measurement of extrinsic gait variability,” Sensors 10, 1024010255 (2010).Google Scholar
8.Seven, U., Akbas, T., Fidan, K. and Erbatur, K., “Bipedal robot walking control on inclined planes by fuzzy reference trajectory modification,” Soft Computing, 16 (11), 19591976 (2012).Google Scholar
9.Yi, S.-J., Zhang, B.-T., Hong, D. and Lee, D. D., “Practical Bipedal Walking Control on Uneven Terrain using Surface Learning and Push Recovery,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2011), pp. 3963–3968.Google Scholar
10.Yeoun-Jae, K., Joon-Yong, L. and Ju-Jang, L., “A Balance Control Strategy of a Walking Biped Robot in An Externally Applied Force,” International Conference on Information and Automation (ICIA), (2012), pp. 572–577.Google Scholar
11.Ferreira, J. P., Crisostomo, M. M. and Coimbra, A. P., “Human gait acquisition and characterization,” IEEE Trans. Instrum. Meas. 58, 29792988 (2009).Google Scholar
12.Siciliano, B. and Khatib, O., Springer Handbook of Robotics (Springer-Verlag New York, Inc., 2007).Google Scholar
13.Hensen, R. H., Van de Molengraft, M. and Steinbuch, M., “Friction induced hunting limit cycles: A comparison between the LuGre and switch friction model,” Automatica 39, 21312137 (2003).CrossRefGoogle Scholar
14.Hensen, R. H. and van de Molengraft, M. J., “Friction Induced Hunting Limit Cycles: An Event Mapping Approach,” Proceedings of the American Control Conference, (2002) pp. 2267–2272.Google Scholar
15.Olsson, H. and Astrom, K. J., “Friction generated limit cycles,” IEEE Trans. Control Syst. Technol. 9, 629636 (2001).Google Scholar
16.Bona, B. and Indri, M., “Friction Compensation in Robotics: An Overview,” IEEE European Control Conference on Decision and Control, (2005) pp. 4360–4367.Google Scholar
17.Jatta, F., Adamini, R., Visioli, A. and Legnani, G., “Hybrid Force/Velocity Robot Contour Tracking: An Experimental Analysis of Friction Compensation Strategies,” IEEE International Conference on Robotics and Automation, (2002), vol. 2, pp. 1723–1728.Google Scholar
18.Armstrong-Hélouvry, B., Dupont, P. and De Wit, C. C., “A survey of models, analysis tools and compensation methods for the control of machines with friction,” Automatica 30, 10831138 (1994).Google Scholar
19.Hellsen, R. H. A., Angelis, G. Z., van de Molengraft, M. J. G., de Jager, A. G. and Kok, J. J., “Grey-box modeling of friction: An experimental case-study,” Eur. J. Control 6, 258267 (2000).Google Scholar
20.Lampaert, V., Swevers, J. and Al-Bender, F., “Modification of the leuven integrated friction model structure,” IEEE Trans. Autom. Control 47, 683687 (2002).Google Scholar
21.Makkar, C., Dixon, W. E., Sawyer, W. G. and Hu, G., “A New Continuously Differentiable Friction Model for Control Systems Design,” Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, (2005) pp. 600–605.Google Scholar
22.Joohyung, K., Hoseong, K., Heekuk, L., Keehong, S., Bokman, L., Minhyung, L., Jusuk, L. and Kyungsik, R., “Balancing Control of a Biped Robot,” IEEE International Conference on Systems, Man, and Cybernetics (SMC), (2012) pp. 2756–2761.Google Scholar
23.Shang, W., Cong, S. and Zhang, Y., “Nonlinear friction compensation of a 2-DOF planar parallel manipulator,” Mechatronics 18, 340346 (2008).Google Scholar
24.Hensen, R. H. A., Van de Molengraft, M. J. G. and Steinbuch, M., “Frequency domain identification of dynamic friction model parameters,” IEEE Trans. Control Syst. Technol. 10, 191196 (2002).Google Scholar
25.Moreno, J. and Kelly, R., “Manipulator Velocity Field Control with Dynamic Friction Compensation,” IEEE Conference on Decision and Control, (2003), vol. 4, pp. 3834–3839.Google Scholar
26.Moreno, J., Kelly, R. and Campa, R., “Manipulator velocity control using friction compensation,” IEE Proc.-Control Theory Appl. 150, 119126 (2003).Google Scholar
27.Kostic, D., de Jager, B., Steinbuch, M. and Hensen, R., “Modeling and identification for high-performance robot control: An RRR-robotic arm case study,” IEEE Trans. Control Syst. Technol. 12, 904919 (2004).Google Scholar
28.Morel, G., Iagnemma, K. and Dubowsky, S., “The precise control of manipulators with high joint-friction using base force/torque sensing,” Automatica 36, 931941 (2000).Google Scholar
29.Grami, S. and Gharbia, Y., “GMS Friction Compensation in Robot Manipulator,” Conference of the IEEE on Industrial Electronics Society (IECON), (2013) pp. 3555–3560.Google Scholar
30.Dongdong, Z., Jing, N., Xuemei, R., Herrmann, G. and Longo, S., “Adaptive Control of Robotic Servo System with Friction Compensation,” IEEE Conference on Robotics, Automation and Mechatronics (RAM), (2011) pp. 285–290.Google Scholar
31.Ray, L. R., Ramasubramanian, A. and Townsend, J., “Adaptive friction compensation using extended Kalman–Bucy filter friction estimation,” Control Eng. Pract. 9, 169179 (2001).Google Scholar
32.Vedagarbha, P., Dawson, D. M. and Feemster, M., “Tracking control of mechanical systems in the presence of nonlinear dynamic friction effects,” IEEE Trans. Control Syst. Technol. 7, 446456 (1999).CrossRefGoogle Scholar
33.Friedland, B. and Park, Y. J., “On adaptive friction compensation,” IEEE Trans. Autom. Control 37, 16091612 (1992).Google Scholar
34.Vischer, D. and Khatib, O., “Design and development of high-performance torque-controlled joints,” IEEE Trans. Robot. Autom. 11, 537544 (1995).Google Scholar
35.Pfeffer, L. E., Khatib, O. and Hake, J., “Joint torque sensory feedback in the control of a PUMA manipulator,” IEEE Trans. Robot. Autom. 5, 418425 (1989).Google Scholar
36.Mohammadi, A., Tavakoli, M., Marquez, H. J. and Hashemzadeh, F., “Nonlinear disturbance observer design for robotic manipulators,” Control Eng. Pract. 21, 253267 (2013).CrossRefGoogle Scholar
37.Wen-Hua, C., Ballance, D. J., Gawthrop, P. J. and O'Reilly, J., “A nonlinear disturbance observer for robotic manipulators,” IEEE Trans. Ind. Electron. 47, 932938 (2000).Google Scholar
38.Xing, D., Su, J., Liu, Y. and Zhong, J., “Robust approach for humanoid joint control based on a disturbance observer,” Control Theory & Appl. IET 5, 16301636 (2011).Google Scholar
39.Ryu, J.-H., Song, J. and Kwon, D.-S., “A nonlinear friction compensation method using adaptive control and its practical application to an in-parallel actuated 6-DOF manipulator,” Control Eng. Pract. 9, 159167 (2001).Google Scholar
40.Armstrong, B., Neevel, D. and Kusik, T., “New results in NPID control: Tracking, integral control, friction compensation and experimental results,” IEEE Trans. Control Syst. Technol. 9, 399406 (2001).Google Scholar
41.Lampaert, V., Swevers, J. and Al-Bender, F., “Comparison of Model and Non-Model based Friction Compensation Techniques in the Neighbourhood of Pre-Sliding Friction,” Proceedings of the American Control Conference, (2004), vol. 2, pp. 1121–1126.Google Scholar
42.Huang, S. N., Tan, K. K. and Lee, T. H., “Adaptive friction compensation using neural network approximations,” IEEE Trans. Syst. Man Cybern. C 30, 551557 (2000).Google Scholar
43.Vitiello, V. and Tornambe, A., “Adaptive Compensation of Modeled Friction using a RBF Neural Network Approximation,” IEEE Conference on Decision and Control, (2007) pp. 4699–4704.Google Scholar
44.Kim, Y. H. and Lewis, F. L., “Reinforcement Adaptive Learning Neural Network based Friction Compensation for High Speed and Precision,” Proceedings of the IEEE Conference on Decision and Control, (1998), vol. 1, pp. 1064–1069.Google Scholar
45.Wang, G. L., Li, Y. F. and Bi, D. X., “Support vector networks in adaptive friction compensation,” IEEE Trans. Neural Netw. 18, 12091219 (2007).Google Scholar
46.Wang, Y. F., Wang, D. H. and Chai, T. Y., “Modeling and control compensation of nonlinear friction using adaptive fuzzy systems,” Mech. Syst. Signal Process. 23, 24452457 (2009).Google Scholar
47.Mostefai, L., Denai, M., Oh, S. and Hori, Y., “Optimal control design for robust fuzzy friction compensation in a robot joint,” IEEE Trans. Ind. Electron. 56, 38323839 (2009).Google Scholar
48.Yongfu, W., Dianhui, W. and Tianyou, C., “Extraction and adaptation of fuzzy rules for friction modeling and control compensation,” IEEE Trans. Fuzzy Syst. 19, 682693 (2011).Google Scholar
49.Velez-Diaz, D. and Yu, T., “Adaptive robust fuzzy control of nonlinear systems,” IEEE Trans. Syst. Man Cybern. B 34, 15961601 (2004).Google Scholar
50.Llama, M. A., Kelly, R. and Santibanez, V., “Stable computed-torque control of robot manipulators via fuzzy self-tuning,” IEEE Trans. Syst. Man Cybern. B 30, 143150 (2000).CrossRefGoogle ScholarPubMed
51.Shiev, K., Shakev, N., Topalov, A. V. and Ahmed, S., “Trajectory Control of Manipulators using Type-2 Fuzzy Neural Friction and Disturbance Compensator,” IEEE International Conference Intelligent Systems (IS), (2012) pp. 324–329.Google Scholar
52.Patton, R. J., Putra, D. and Klinkhieo, S., “Friction compensation as a fault-tolerant control problem,” Int. J. Syst. Sci. 41, 9871001 (2010).Google Scholar
53.Yan, H., Vanderborght, B., R. Van Ham, Qining, W., M. Van Damme, X. Guangming and Lefeber, D., “Step length and velocity control of a dynamic bipedal walking robot with adaptable compliant joints,” IEEE/ASME Trans. Mechatronics 18, 598611 (2013).Google Scholar
54.van Zutven, P., Kostic, D. and Nijmeijer, H., “Foot Placement for Planar Bipeds with Point Feet,” IEEE International Conference on Robotics and Automation (ICRA), (2012) pp. 983–988.Google Scholar
55.Hase, T., Qingjiu, H. and Xuedong, C., “Performance Analysis of Biped Walking Robot with Circular Feet using Optimal Trajectory Planning Method,” IEEE International Conference on Robotics and Biomimetics, (2009), pp. 143–148.Google Scholar
56.Miyahara, K., Harada, Y., Nenchev, D. N. and Sato, D., “Three-Dimensional Limit Cycle Walking with Joint Actuation,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2009) pp. 4445–4450.Google Scholar
57.Yoshikawa, T. and Khatib, O., “Compliant Humanoid Robot Control by the Torque Transformer,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2009) pp. 3011–3018.Google Scholar
58.Han, S. I. and Lee, K. S., “Robust friction state observer and recurrent fuzzy neural network design for dynamic friction compensation with backstepping control,” Mechatronics 20, 384401 (2010).Google Scholar
59.Kajita, S. and Tani, K., “Study of Dynamic Biped Locomotion on Rugged Terrain-Derivation and Application of the Linear Inverted Pendulum Mode,” Proceedings of IEEE International Conference on Robotics and Automation, (1991), vol. 2, pp. 1405–1411.Google Scholar
60.Erbatur, K., Seven, U., Taskiran, E., Koca, O., Yilmaz, M., Unel, M., Kiziltas, G., A. Sabanovic and Onat, A., “SURALP: A New Full-Body Humanoid Robot Platform,” IEEE-Rsj International Conference on Intelligent Robots and Systems, (New York, 2009) pp. 4949–4954.CrossRefGoogle Scholar
61.Hashlamon, I. and Erbatur, K., “Experimental Verification of an Orientation Estimation Technique for Autonomous Robotic Platforms.” Master degree Thesis (Istanbul: Sabanci University, 2010).Google Scholar
62.Erbatur, K. and Seven, U., “Humanoid Gait Synthesis with Moving Single Support ZMP Trajectories,” Proceedings of the 13th Iasted International Conference on Robotics and Applications/Proceedings of the Iasted International Conference on Telematics, (Schilling, K., ed.) (Anaheim: Acta Press Anaheim, 2007) pp. 95–100.Google Scholar
63.Hirukawa, H., Hattori, S., Harada, K., Kajita, S., Kaneko, K., Kanehiro, F., K. Fujiwara and Morisawa, M., “A Universal Stability Criterion of the Foot Contact of Legged Robots - Adios ZMP,” Proceedings of the IEEE International Conference on Robotics and Automation, (2006) pp. 1976–1983.Google Scholar
64.Erbatur, K. and Kurt, O., “Natural ZMP trajectories for biped robot reference generation,” IEEE Trans. Ind. Electron. 56, 835845 (Mar. 2009).Google Scholar
65.Hur, I., Matsuki, Y., Tomokuni, N., Huang, J. and Yabuta, T., “Standing Stability of Surfing Robot without Force Sensor,” 25th Annual Conference of the Robotics Society of Japan, 3H11, (2007) pp. 15–18.Google Scholar
66.Kajita, S., Kanehiro, F., Kaneko, K., Fujiwara, K., Harada, K., Yokoi, K. and Hirukawa, H., “Biped Walking Pattern Generation by using Preview Control of Zero-Moment Point,” Proceedings of IEEE International Conference on Robotics and Automation ICRA, (2003), vol. 2, pp. 1620–1626.Google Scholar
67.Stephens, B. J., “State Estimation for Force-Controlled Humanoid Balance using Simple Models in the Presence of Modeling Error,” IEEE International Conference on Robotics and Automation, (2011) pp. 3994–3999.Google Scholar
68.Hashlamon, I. and Erbatur, K., “Center of Mass States and Disturbance Estimation for a Walking Biped,” IEEE International Conference on Mechatronics, ICM 2013, Vicenza (ITALY). (2013) pp. 248–253.Google Scholar
69.Hashlamon, I. and Erbatur, K., “An improved real-time adaptive Kalman filter with recursive noise covariance updating rules,” Turk. J. Electr. Eng. Comput. Sci., Accepted, (2013) doi: 10.3906/elk-1309-60.Google Scholar
70.Swevers, J., Ganseman, C., Tukel, D. B., De Schutter, J. and Van Brussel, H., “Optimal robot excitation and identification,” IEEE Trans. Robot. Autom. 13, 730740 (1997).CrossRefGoogle Scholar
71.Hsu, P., Bodson, M., S. Sastry and Paden, B., “Adaptive Identification and Control for Manipulators without Using Joint Accelerations,” Proceedings IEEE International Conference on Robotics and Automation, (1987) pp. 1210–1215.Google Scholar
72.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,” IEEE International Conference on Robotics and Automation (ICRA), (2011) pp. 1108–1113.Google Scholar
73.Erbatur, K. and Kawamura, A., “A New Penalty based Contact Modeling and Dynamics Simulation Method as Applied to Biped Walking Robots,” Proceedings of the FIRA World Congress, Vienna, Austria, (2003).Google Scholar