and Applied Mechanics
56, 4, pp. 1179-1191, Warsaw 2018
DOI: 10.15632/jtam-pl.56.4.1179
Computational optimization and implementation of control system for mechatronic treadmill with body weight support system
which consists of a body weight support system (BWS system) and a treadmill. This publication
covers mainly issues related to the design and optimization process of a control
algorithm dedicated for the unloading system. The proposed control system is based on
a fuzzy logic controller coupled with a PID regulator. The optimization of parameters for
regulators has been conducted based on numerical simulations in which a hybrid optimization
method combining a genetic algorithm with a gradient algorithm has been used. The
developed control system has been tested experimentally.
References
Cao J., Xie S.Q., Das R., Zhu G.L., 2014, Control strategies for effective robot assisted gait
rehabilitation: the state of art and future prospects, Medical Engineering and Physics, 36, 12, 1555-1566
Chen G., Chan C.K., Guo Z., Yu H., 2013, A review of lower extremity assistive robotic
exoskeletons in rehabilitation therapy, Critical Reviews in Biomedical Engineering, 41, 4-5, 343-363
Dragunas A.C., Gordon K.E., 2016, Body weight support impacts lateral stability during
treadmill walking, Journal of Biomechanics, 49, 13, 2662-2668
Duda S., Gąsiorek D., Gembalczyk G., Kciuk S., Mężyk A., 2016, Mechatronic device for
locomotor training, Acta Mechanica et Automatica, 10, 4, 310-315
Duda S., Gemalczyk G., 2016, Experimental study on the fuzzy-PID hybrid control algorithm
for unloading system in mechatronic device for gait re-education, Proceedings of VII European
Congress on Computational Methods in Applied Sciences and Engineering, 6567-6573
Duda S., Gembalczyk G., Switonski E., 2017, Design study and development of mechatronic
treadmill for gait reeducation, Engineering Dynamics and Life Sciences. Proceedings of 14th
International Conference Dynamical Systems Theory and Applications 2017, 183-191
Frey M., Colombo G., Vaglio M., Bucher R., J¨org M., Riener R., 2006, A novel mechatronic
body weight support system, Neural Systems and Rehabilitation Engineering, 14, 3, 311-321
Gniłka J., Mężyk A., 2017, Experimental identification and selection of dynamic properties of
a high-speed tracked vehicle suspension system, Eksploatacja i Niezawodność – Maintenance and
Reliability, 19, 1, 108-113
Hidler J., Brennan D., Black I., Nichols D., Brady K., 2011, ZeroG: Overground gait and
balance training system, Journal of Rehabilitation Research and Development, 48, 4, 287-298
Jurkojć J., Wodarski P., Bieniek A., Gzik M., Michnik R., 2017, Influence of changing
frequency and various sceneries on stabilometric parameters and on the effect of adaptation in an
immersive 3D virtual environment, Acta of Bioengineering and Biomechanics, 19, 3, 129-137
Koceska N., Koceski S., 2013, Review: robot devices for gait rehabilitation, International Journal
of Computer Applications, 62, 13, 1-8
Koenig A., Omlin X., Bergmann J., Zimmerli L., Bolliger M., M¨uller F., 2011, Controlling
patient participation during robot assisted gait training, Journal of Neuroengineering and
Rehabilitation, 8, 14-25
Kot A., Nawrocka A., 2012, Balance platform system dynamic properties, Journal of Vibroengineering, 14, 1, 178-182
Lünenburger L., Colombo G., Riener R., 2007, Biofeedback for robotic gait rehabilitation,
Journal of Neuroengineering and Rehabilitation, 4, 1
Mehrholz J., Pohl M., Elsner B., 2014, Treadmill training and body weight support for
walking after stroke, [In:] Cochrane Database of Systematic Reviews, 1, John Wiley & Sons, Ltd
Mężyk A., Klein W., Fice M., Pawlak M., Basiura K., 2016, Mechatronic model of continuous
miner cutting drum driveline, Mechatronics, 37, 12-20
Miądlicki K., Pajor M., 2015, Real-time gesture control of a CNC machine tool with the use
Microsoft Kinect sensor, International Journal of Scientific and Engineering Research, 6, 538-543
Mignardot J.B., Le Goff C.G., van Den Brand R., 2017, A multidirectional gravity-assist
algorithm that enhances locomotor control in patients with stroke or spinal cord injury, Science
Translational Medicine, 9
Pajor M., Herbin P., 2015, Exoskeleton of upper limb – model using real movement parameters
(in Polish), Modelowanie Inżynierskie, 26, 57, 40-46
Pratt G.A., Williamson M.M., 1995, Series elastic actuators, IEEE International Conference
on Intelligent Robots and Systems, 399-406
Querry R.G., Pacheco F., Annaswamy T., Goetz L., Winchester P.K., Tansey K.E., 2008, Synchronous stimulation and monitoring of soleus H reflex during robotic body weightsupported
ambulation in subjects with spinal cord injury, Journal of Rehabilitation Research and
Development, 45, 1, 175-186
Raczka W., Sibielak M., Kowal J., Konieczny J., 2013, Application of an SMA spring for
vibration screen control, Journal of Low Frequency Noise, Vibration and Active Control, 32, 1-2, 117-131
Reinkensmeyer D.J., Dietz V., 2016, Neurorehabilitation Technology, Springer, International
Publishing
Riener R., L¨unenburger L., Maier I.C., Colombo G., Dietz V., 2010, Locomotor training
in subjects with sensori-motor deficits: an overview of the robotic gait orthosis lokomat, Journal
of Healthcare Engineering, 1, 2, 197-216
Robinson D.W., Pratt J.E., Paluska D.J., Pratt G.A., 1999, Series elastic actuator development
for a biomimetic walking robot, Proceedings of IEEE/ASME International Conference on
Advanced Intelligent Mechatronics, 561-568.
Sapiński B., Rosół M., Węgrzynowski M., 2016, Investigation of an energy harvesting MR
damper in a vibration control system, Smart Materials and Structures, 25, 12, 125017, 1-15
Snamina J., Kowal J., Orkisz P., 2013, Active suspension based on low dynamic stiffness, Acta
Physica Polonica A, 123, 6, 1118-1122
Xu W.J., 2012, Permanent magnet synchronous motor with linear quadratic speed controller,
Energy Procedia, 14, 364-369
Zhao C.S., Zhu S.J., He Q.W., 2007, Fuzzy-PID control method for two-stage vibration isolation
system, Journal of Theoretical and Applied Mechanics, 45, 1, 171-177