Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-05T16:53:43.553Z Has data issue: false hasContentIssue false

Human-adaptive control of series elastic actuators

Published online by Cambridge University Press:  07 July 2014

Andrea Calanca*
Affiliation:
Department of Computer Science, University of Verona, Verona, Italy
Paolo Fiorini
Affiliation:
Department of Computer Science, University of Verona, Verona, Italy
*
*Corresponding author. E-mail: [email protected]

Summary

Force-controlled series elastic actuators (SEAs) are the widely used components of novel physical human–robot interaction applications such as assistive and rehabilitation robotics. These systems are characterized by the presence of the “human in the loop” so that control response and stability depend on uncertain human dynamics. A common approach to guarantee stability is to use a passivity-based controller. Unfortunately, existing passivity-based controllers for SEAs do not define the performance of the force/torque loop. We propose a method to obtain predictable force/torque dynamics based on adaptive control and oversimplified human models. We propose a class of stable human-adaptive algorithms and experimentally show advantages of the proposed 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.Buerger, S. P. and Hogan, N., “Complementary stability and loop shaping for improved human-robot interaction,” IEEE Trans. Robot. 23 (2), 232244 (2007).Google Scholar
2.Buerger, S. and Hogan, N., “Relaxing Passivity for Human-Robot Interaction,” Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (Oct. 2006) pp. 4570–4575.Google Scholar
3.Cai, L. L., Fong, A. J., Otoshi, C. K., Liang, Y., Burdick, J. W., Roy, R. R. and Edgerton, V. R., “Implications of assist-as-needed robotic step training after a complete spinal cord injury on intrinsic strategies of motor learning,” J. Neurosci. 26 (41), 1056410568 (Oct. 2006).Google Scholar
4.Calanca, A., Capisani, L. M., Ferrara, A. and Magnani, L., “MIMO closed loop identification of an industrial robot,” IEEE Trans. Control Syst. Technol. 19 (5), 12141224 (2011).Google Scholar
5.Calanca, A., Piazza, S. and Fiorini, P., “Force Control System for Pneumatic Actuators of an Active Gait Orthosis,” Proceedings of the 2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Tokyo, Japan (Sep. 26–29, 2010) pp. 6469.Google Scholar
6.Calanca, A., Piazza, S. and Fiorini, P., “A motor learning oriented, compliant and mobile gait orthosis,” Appl. Bionics Biomech. 9 (1), 1527 (2012).Google Scholar
7.Duschau-Wicke, A., von Zitzewitz, J., Caprez, A., Lunenburger, L. and Riener, R., “Path control: A method for patient-cooperative robot-aided gait rehabilitation,” IEEE Trans. Neural. Syst. Rehabil. Eng. 18 (1), 3848 (Feb. 2010).Google Scholar
8.Erdmann, W. S., “Geometry and inertia of the human body-review of research,” Acta Bioeng. Biomech. 1 (1), 2335 (1999).Google Scholar
9.Ferraro, M., Palazzolo, J. J., Krol, J., Krebs, H. I., Hogan, N. and Volpe, B. T., “Robotaided sensorimotor arm training improves outcome in patients with chronic stroke,” Neurology 61 (11), 16041607 (2003).Google Scholar
10.Kearney, R. E., Stein, R. B. and Parameswaran, L., “Identification of intrinsic and reflex contributions to human ankle stiffness dynamics,” IEEE Trans. Biomed. Eng. 44 (6), 493504 (Jun. 1997).Google Scholar
11.Kong, K., Member, S. and Bae, J., “Control of rotary series elastic actuator for ideal force-mode actuation in human-robot interaction applications,” IEEE/ASME Int. Conf. Mechatronics 14 (1), 105118 (2009).Google Scholar
12.Krebs, H. I., Dipietro, L., Volpe, B. T. and Hogan, N., “Rehabilitation robotics: Performance-based progressive robot-assisted therapy,” Auton. Robots 15, 720 (2003).Google Scholar
13.Kyoungchul, K., Hyosang, M., Doyoung, J. and Masayoshi, T., “Control of an exoskeleton for realization of aquatic therapy effects,” IEEE/ASME Trans. Mechatronics 15, 191200 (2010).Google Scholar
14.Narendra, K. S. and Annaswamy, A. M., “Robust Adaptive Control,” In: Proceedings of the 1984 American Control Conference (ACC '84), Boston, MA (TFRT-1035) (Springer, New York, NY, 1985) 848 pp.Google Scholar
15.Palazzolo, J. J., “Robotic Technology to Aid and Assess Recovery and Learning in Stroke Patients,” Ph.D. Thesis (Massachusetts Institute of Technology, 2005).Google Scholar
16.Pratt, G. A. and Williamson, M. M., “Series Elastic Actuators,” In: IEEE International Conference on Intelligent Robots and Systems, vol. 1 (1995) pp. 399–406.Google Scholar
17.Pratt, G. A., Willisson, P. and Bolton, C., “Late Motor Processing in Low-Impedance Robots: Impedance Control of Series-Elastic Actuators,” American Control Conference (2004) pp. 3245–3251.Google Scholar
18.Pratt, J., Chew, C.-M., Torres, A., Dilworth, P. and Pratt, G., “Virtual model control: An intuitive approach for bipedal locomotion,” Int. J. Robot. Res. 20 (2), 129143 (2001).Google Scholar
19.Slotine, J.-J. E. and Li, W., Applied Nonlinear Control, vol. 62 (Prentice Hall, Upper Saddle River, NJ, 1991).Google Scholar
20.Stein, R. B., Zehr, E. P., Lebiedowska, M. K., Popović, D. B., Scheiner, A. and Chizeck, H. J., “Estimating mechanical parameters of leg segments in individuals with and without physical disabilities,” IEEE Trans. Rehabil. Eng. 4 (3), 201211 (Sep. 1996).Google Scholar
21.Stroeve, S., “Impedance characteristics of a neuromusculoskeletal model of the human arm I. Posture control,” Biol. Cybern. 81 (5–6), 475494 (Nov. 1999).Google Scholar
22.Tee, K. P., Burdet, E., Chew, C. M. and Milner, T. E., “A model of force and impedance in human arm movements,” Biol. Cybern. 90 (5), 368375 (May 2004).Google Scholar
23.Vallery, H., “Stable and User-Controlled Assistance of Human Motor Function.” Ph.D. Thesis (University of Munchen, Munich, Germany, 2009).Google Scholar
24.Vallery, H., Ekkelenkamp, R., van der Kooij, H. and Buss, M., “Passive and Accurate Torque Control of Series Elastic Actuators,” Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (Oct. 2007) pp. 3534–3538.Google Scholar
25.Vallery, H., van Asseldonk, E. H. F., Buss, M. and van der Kooij, H., “Reference trajectory generation for rehabilitation robots: Complementary limb motion estimation,” IEEE Trans. Neural Syst. Rehabil. Eng. 17 (1), 2330 (Feb. 2009).Google Scholar
26.Xu, Y. and Hollerbach, J. M., “A robust ensemble data method for identification of human joint mechanical properties during movement,” IEEE Trans. Biomed. Eng. 46 (4), 409419 (Apr. 1999).Google Scholar
27.Zatsiorsky, V. M., Kinetics of Human Motion (Human Kinetics, Champaign, IL, 2002).Google Scholar