Document Type : Full Research Paper


1 PhD Candidate, Bioelectric Group, Bioengineering School, Amirkabir University of Technology

2 Associated Professor, Bioelectric Group, Bioengineering School, Amirkabir University of Technology



Based on previous studies, human motor control system may apply two control strategies, impedance control and model based control, for learning motor skills and counteracting environmental instabilities. Since interaction among these controllers is not fully studied, the investigation of impedance and model based controllers function during learning period seems desirable. In this study a supervisory controller was used to coordinate the model based and impedance controllers. Coordinating model based controller and impedance controller by using supervisory unit will result in simultaneously adjustment of forward motor command and joint stiffness. In order to evaluate performance of the suggested model, it was applied to arm reaching movements in the presence of external force fields. Results showed that both suitable impedance values and a proper internal model are required to fulfill movements similar to those of humans under different circumstances. Research has shown that central nervous system is able to purposefully modulate arm impedance to counteract environmental disturbances. This study showed that beside this modulation, the maximum motor learning may occur in direction with the least impedance and the most kinematic error. It also concluded that confronting abrupt changes in disturbance, the system managed to decrease error without learning the new dynamic using previous knowledge by supervisory system. A part of this compensation is due to stiffness variations and another part is due to decreasing the influence of model based controller.


Main Subjects

[1]     Hogan N. (1985b) Impedance Control: An Approach to Manipulation: Part I-Theory, Journal of Dynamic Systems, Measurement, and Control. 107:1-7.
[2]     Towhidkhah F., Gander R.E., Wood H.C., Model Predictive Impedance Control: A Model for Joint Movement, J. Motor Behavior, 1997, 29:209-222.
[3]     Franklin D.W., Osu R., Burdet E., Kawato M. & Milner T.E., Adaptation to Stable and Unstable Dynamics Achieved by Combined Impedance Control and Inverse Dynamics Model, J. Neurophysiol., 2003, 90:3270-3282.
[4]     Franklin D.W., Liaw G., Milner T.E., Osu R., Burdet E., Kawato M., Endpoint Stiffness of the Arm Is Directionally Tuned to Instability in the Environment, J. Neuroscience, 2007, 27:7705- 7716.
[5]     Burdet E., Tee K.P., Mareels I., Milner T.E., Chew C.M., Franklin D.W., Osu R., Kawato M., Stability and motor adaptation in human arm movements, Biol. Cybern., 2006, 94:20-32.
[6]     Milner T.E., Franklin D.W., Imamizo H., Kawato M., Central Control of Grasp: Manipulation of Objects with Complex and Simple Dynamics, NeuroImage, 2007, 36:388 – 395.
[7]     Darainy M., Ostry D.J., Muscle Co-contraction Following Dynamics Learning, Exp Brain Res., 2008, 190: 153-163.
[8]     Kawato M., Internal Models for Motor Control and Trajectory Planning, Curr. Opin. Neurobiol., 1999, 9: 718-27.
[9]     Darainy M., Evaluation and Modeling of Learning Effects on Control of Skilled Movements through Impedance Regulation and Model Predictive Control, In Persian, Ph.D. Thesis, 2005, Amirkabir university of technology,Tehran, Iran.
[10] Milner T.E. & Franklin D.W., Impedance Control and Internal Model Use During the Initial Stage of Adaptation to Novel Dynamics in Humans, J. physiol., 2005, 567.2: 651-664.
[11] Franklin D.W., Burdet E., Tee K.P., Osu R., Chew C.M., Milner T.E., Kawato M., CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm. J. Neuroscience, 2008, 28: 11165-11173.
[12] Thoroughman K.A. & Shadmehr R., Electromyographic Correlates of Learning an Internal Model of Reaching Movements, J. Neurosci., 1999 19:8574–8588.
[13] Selen L.P.J., Franklin D.W., Wolpert D.M., Impedance Control Reduce Instability that Arises from Motor Noise. J. Neurosci., 2009, 29:12606-12616.
[14] Shadmehr R., Mussa-Ivaldi F.A., Adaptive Representation of Dynamics during Learning of a Motor task, J Neurosci., 1994, 14:3208-3224.
[15] Mehta B., Schaal S., Forward Models in Visuomotor Control, J. Neurophysiol., 2002, 88:942-953.
[16] Imamizu H., Sugimoto N., Osu R., Tsutsui K., Sugiyama K., Wada Y., Kawato M., Explicit Contextual Information Selectively Contributes to Predictive Switching of Internal Models, Exp. Brain Res., 2007, 181: 395-408.
[17] Emadi Andali M., Bahrami F., Jabehdar Maralani P., MODEM: a Multi-agent Hierarchical Structure to Model the Human Motor Control System, Biol. Cybern., 2009, 101: 361-377.
[18] Wagner M.J., Smith M.A., Shared Internal Models for Feedforward and Feedback Control, J. Neuroscience, 2008, 42: 10663-10673.
[19] Mitrovic D., Klanke S., Osu R., Kawato M., Vijayakumar S., Impedance Control as an Emergent Mechanism from Minimising Uncertainty, 2009, report, The University of Edinburgh, EDI-INF-RR-1317.
[20] Burdet E., Osu R., Franklin D.W., Milner T.E., Kawato M., The central nervous system stabilizes unstable dynamics by learning optimal impedance, Nature, 2001, 414: 446- 449.
[21] Milner T.E., Hinder M.R., Position Information but not Force Information is Used in Adapting to Changes in Environmental Dynamics, J. Neurophysiol., 2006, 96.2, 526-534.
[22] Darainy M., Mattar A.G., Ostry D.J., Effects of Human Arm Impedance on Dynamics Learning and Generalization, J. neurophysiol., 2009, 101: 3158–3168.
[23] Shadmehr R., Krakauer J.W., A Computational Neuroanatomy for Motor Control, Exp Brain Res., 2008, 185: 359-381.
[24] Cerminara N.L., Apps R., Marple-Horvat E., An Internal Model of Moving Visual Target in the Lateral Cerebellum, J. Physiol., 2009, 587.2: 429-442.
[25] Wang L., Model Predictive Control System Design and Implementation using MATLAb, Springer-Verlog London Limited, 2009.
[26] Wang L.X., A Course in Fuzzy Systems and Control, Prentice-Hall inc, 1997.
[27] Shadmehr R., Brashers-Krug T., Functional Stages in the Formation of Human Long-term Motor Memory, J Neurosci., 1997, 17:409-419.
[28] Tee K.P., Burdet E., Chew C.M., Milner T.E., A Model of Force and Impedance in Human Arm Movements, J. Biol. Cybern., 2004, 90:368-375.
[29] Mussa-Ivaldi F.A., Hogan N., Bizzi E., Neural, Mechanical and Geometric Factors Subserving Arm Posture in Human, J. Neurosci., 1985, 5:2732-2743.
[30] Osu R., Burdet E., Franklin D.W., Milner T.E., Kawato M., Different Mechanism Involved in Adaptation to Stable and Unstable Dynamics, J. Neurophysiol., 2003, 90:3255-3269.
[31] Wainscott K.W., Donchin O., Shadmehr R., Internal Models and Contextual Cues: Encoding Serial Order and Direction of Movement, J. Neurophysiol, 2005, 93: 786-800.
[32] Izawa J., Rane T., Dochin O., Shadmehr R., Motor Adaptation as a Process of Reoptimization, J. Neuroscience, 2008, 28: 2883-2891.
[33] Lonini L., Dipietro L., Zollo L., Guglielmelli E., Krebs H.I., An Internal Model for Acquisition and Retention of Motor Learning During Arm Reaching, Neural Computation, 2009, 21:2009-2027.
[34] Della-Maggiore V., Malfait N., Ostry D.J., Paus T., Stimulation of the Posterior Parietal Cortex Interferes with Arm Trajectory Adjustments during the Learning of new Dynamics, J. Neurosci, 2004, 24: 9971–9976.
[35] Emken J.L., Benitez R., Sideris A., Bobrow J.E., Reinkensmeyer D.J., Motor Adaptation as a Greedy Optimization of Error and Effort, J Neurophysiol.,