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Data-driven model-free adaptive sliding mode control for the multi degree-of-freedom robotic exoskeleton. (English) Zbl 1386.93071
Summary: In this paper, a data-driven Model-Free Adaptive Sliding Mode Control (MFASMC) approach is proposed based on a novel transformation and linearization of the robotic exoskeleton dynamics and a discrete time sliding mode with exponential reaching law. The main feature of the approach is that the dynamics of the multi Degree-Of-Freedom (DOF) robotic exoskeleton are transformed and linearized properly for MFASMC, the controller designing depends only on the measured input torque and output velocity of each joint of the exoskeleton and the sliding mode reaching law guarantees the convergence of MFASMC schemes. The proposed control strategy can maneuver the robotic exoskeleton tracking on its desired velocity tightly even when the dynamic parameter of the exoskeleton is time-varying irregularly and uncertainly. Extensive simulation experiments are conducted by a SimMechanics model of the robotic exoskeleton to illustrate the effectiveness of the proposed approaches.

MSC:
93B12 Variable structure systems
93C40 Adaptive control/observation systems
93C85 Automated systems (robots, etc.) in control theory
68T40 Artificial intelligence for robotics
Software:
SimMechanics
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[1] Anam, K.; Al-Jumaily, A. A., Active exoskeleton control systems: state of the art, Procedia Eng., 41, 988-994, (2012)
[2] Carignan, C.; Tang, J.; Roderick, S., Development of an exoskeleton haptic interface for virtual task training, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, IROS 2009, 3697-3702, (2009), IEEE
[3] Frisoli, A.; Sotgiu, E.; Procopio, C.; Bergamasco, M.; Rossi, B.; Chisari, C., Design and implementation of a training strategy in chronic stroke with an arm robotic exoskeleton, 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), 1-8, (2011), IEEE
[4] Gao, W.; Wang, Y.; Homaifa, A., Discrete-time variable structure control systems, IEEE Trans. Ind. Electron., 42, 117-122, (1995)
[5] Ge, S.; Li, Z.; Yang, H., Data driven adaptive predictive control for holonomic constrained under-actuated biped robots, IEEE Trans. Control Syst. Technol., 20, 787-795, (2012)
[6] Głowiński, S.; Krzyżyński, T.; Pecolt, S.; Maciejewski, I., Design of motion trajectory of an arm exoskeleton, Arch. Appl. Mech., 85, 75-87, (2015)
[7] Gopura, R. A.R. C.; Kiguchi, K.; Li, Y., Sueful-7: a 7DOF upper-limb exoskeleton robot with muscle-model-oriented EMG-based control, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, IROS 2009, 1126-1131, (2009), IEEE
[8] Hou, Z.; Jin, S., Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems, IEEE Trans. Neural Networks, 22, 2173-2188, (2011)
[9] Hou, Z.; Jin, S., A novel data-driven control approach for a class of discrete-time nonlinear systems, IEEE Trans. Control Syst. Technol., 19, 1549-1558, (2011)
[10] Hou, Z.; Jin, S., Model Free Adaptive Control: Theory and Applications, (2013), CRC Press
[11] Hou, Z.-S.; Wang, Z., From model-based control to data-driven control: survey, classification and perspective, Inf. Sci., 235, 3-35, (2013) · Zbl 1284.93010
[12] Hou, Z.-S.; Xu, J.-X., On data-driven control theory: the state of the art and perspective, Acta Autom. Sin., 35, 650-667, (2009)
[13] Kang, H.-B.; Wang, J.-H., Adaptive robust control of 5 DOF upper-limb exoskeleton robot, Int. J. Control Autom. Syst., 13, 733-741, (2015)
[14] Kikuuwe, R.; Yasukouchi, S.; Fujimoto, H.; Yamamoto, M., Proxy-based sliding mode control: a safer extension of PID position control, IEEE Trans. Rob., 26, 670-683, (2010)
[15] Li, Q.; Wang, D.; Du, Z.; Sun, L., A novel rehabilitation system for upper limbs, 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005, IEEE-EMBS 2005, 6840-6843, (2006), IEEE
[16] Li, Z.; Su, C.; Li, G.; Su, H., Fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs, IEEE Trans. Fuzzy Syst., 23, 555-566, (2015)
[17] Li, Z.; Wang, B.; Sun, F.; Yang, C.; Xie, Q.; Zhang, W., Semg-based joint force control for an upper-limb power-assist exoskeleton robot, IEEE J. Biomed. Health Inf., 18, 1043-1050, (2014)
[18] Lo, H. S.; Xie, S. Q., Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects, Med. Eng. Phys., 34, 261-268, (2012)
[19] Nef, T.; Guidali, M.; Riener, R., Armin III - arm therapy exoskeleton with an ergonomic shoulder actuation, Appl. Bionics Biomech., 6, 127-142, (2009)
[20] Nguyen-Tuong, D.; Peters, J., Online kernel-based learning for task-space tracking robot control, IEEE Trans. Neural Networks Learn. Syst., 23, 1417-1425, (2012)
[21] Norouzi-Gheidari, N., Effects of robot-assisted therapy on stroke rehabilitation in upper limbs: systematic review and meta-analysis of the literature, J. Rehabil. Res. Dev., 49, 479-495, (2012)
[22] Rahman, M. H.; Ochoa-Luna, C.; Rahman, M. J.; Saad, M.; Archambault, P., Force-position control of a robotic exoskeleton to provide upper extremity movement assistance, Int. J. Model. Ident. Control, 21, 390-400, (2014)
[23] Rahman, M. H.; Saad, M.; Kenne, J.-P.; Archambault, P. S., Modeling and control of a 7DOF exoskeleton robot for arm movements, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), 245-250, (2009), IEEE
[24] Renquan, L.; Li, Z.; Su, C.; Xue, A., Development and learning control of a human limb with a rehabilitation exoskeleton, IEEE Trans. Ind. Electron., 61, 3776-3785, (2014)
[25] Slotine, J.-J. E.; Li, W., On the adaptive control of robot manipulators, Int. J. Rob. Res., 6, 49-59, (1987)
[26] Stienen, A. H.; Hekman, E. E.; Van der Helm, F. C.; Prange, G. B.; Jannink, M. J.; Aalsma, A. M.; Van der Kooij, H., Dampace: dynamic force-coordination trainer for the upper extremities, Rehabilitation Robotics, 2007. ICORR 2007. IEEE 10th International Conference on, 820-826, (2007), IEEE
[27] Vertechy, R.; Frisoli, A.; Dettori, A.; Solazzi, M.; Bergamasco, M., Development of a new exoskeleton for upper limb rehabilitation, IEEE International Conference on Rehabilitation Robotics, 2009, ICORR 2009, 188-193, (2009), IEEE
[28] Wang, Z.; Liu, D., A data-based state feedback control method for a class of nonlinear systems, IEEE Trans. Ind. Inf., 9, 2284-2292, (2013)
[29] Jun Yang, Q. B., Human-machine interaction force control: using a model-referenced adaptive impedance device to control an index finger exoskeleton, J. Zhejiang Univ. Sci. C: Comput. Electron., 15, 275-283, (2014)
[30] Yin, S.; Ding, S.; Xie, X.; Luo, H., A review on basic data-driven approaches for industrial process monitoring, IEEE Trans. Ind. Electron., 61, 6418-6428, (2014)
[31] Yin, S.; Huang, Z., Performance monitoring for vehicle suspension system via fuzzy positivistic c-means clustering based on accelerometer measurements, IEEE/ASME Trans. Mechatron., PP, 1-8, (2014)
[32] Yin, S.; Li, X.; Gao, H.; Kaynak, O., Data-based techniques focused on modern industry: an overview, IEEE Trans. Ind. Electron., 62, 657-667, (2015)
[33] Yin, S.; Zhu, X.; Kaynak, O., Improved PLS focused on key-performance-indicator-related fault diagnosis, IEEE Trans. Ind. Electron., 62, 1651-1658, (2015)
[34] Zhang, L.; Cong, D.-C.; Jiang, H.-Z.; Li, H.-R., Method of generating virtual prototype based on simmechanics CAD translator, J. Syst. Simul., 19, 4073-4699, (2007)
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