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Myoelectric Model Reference Adaptive Control with Adaptive Kalman Filter for a soft elbow exoskeleton

dc.contributor.authorToro-Ossaba A
dc.contributor.authorTejada J.C
dc.contributor.authorRúa S
dc.contributor.authorNúñez J.D
dc.contributor.authorPeña A.
dc.date.accessioned2024-07-31T21:07:01Z
dc.date.available2024-07-31T21:07:01Z
dc.date.created2024
dc.identifier.issn9670661
dc.identifier.urihttp://hdl.handle.net/11407/8449
dc.descriptionRehabilitation and assistance exoskeletons have been widely studied because they allow to provide more effective, intensive, and adaptive therapies; in addition, they can be used to augment the user's capabilities in order to provide movement assistance. In particular, soft robotic exoskeletons have been researched during the past decade because they allow the device to adapt to the body contours, increasing the user's comfort. One of the main challenges in the development of soft robotic exoskeletons is the design of controllers that allow intuitive and active control of the device. This work addresses the development of a myoelectric Model Reference Adaptive Controller (MRAC) with an Adaptive Kalman Filter for controlling a cable driven soft elbow exoskeleton. The proposed MRAC controller proved to be suitable for both passive and active control of the soft elbow exoskeleton. The controlled system achieved a Mean Absolute Error (MAE) of approximately 4.5° in passive mode and 10° during active mode. Additionally, the active control mode allowed an average reduction of 34 % to 40 % in the joint torque RMS when performing dynamic Flexion–Extension movements. The active control mode is based on a surface electromyography (sEMG) joint torque estimation algorithm that achieved an approximate MAE of 1 N m. The proposed MRAC controller proved to be robust enough to adapt to the exoskeleton uncertainties and external disturbances; additionally, the adaptive scheme allowed the system to operate with only two sEMG channels and the measurement of the joint angle, which is estimated by using two Inertial Measurement Units. © 2023 Elsevier Ltd
dc.language.isoeng
dc.publisherElsevier Ltd
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85175534643&doi=10.1016%2fj.conengprac.2023.105774&partnerID=40&md5=4dacc81e6ad2dd18e586b6958cf4af2e
dc.sourceControl engineering Practice
dc.sourceControl eng. Pract.
dc.sourceScopus
dc.subjectAdaptive Kalman Filtereng
dc.subjectElbow exoskeletoneng
dc.subjectModel Reference Adaptive Control (MRAC)eng
dc.subjectMyoelectric (EMG) controleng
dc.subjectSoft roboticseng
dc.subjectAdaptive filteringeng
dc.subjectAdaptive filterseng
dc.subjectControllerseng
dc.subjectExoskeleton (Robotics)eng
dc.subjectKalman filterseng
dc.subjectPassive filterseng
dc.subjectUncertainty analysiseng
dc.subjectActive controleng
dc.subjectAdaptive kalman filtereng
dc.subjectElbow exoskeletoneng
dc.subjectEMG controleng
dc.subjectMean absolute erroreng
dc.subjectModel reference adaptive controleng
dc.subjectModel-reference adaptive controlseng
dc.subjectMyoelectric (EMG) controleng
dc.subjectRobotic exoskeletonseng
dc.subjectSoft roboticseng
dc.subjectModel reference adaptive controleng
dc.titleMyoelectric Model Reference Adaptive Control with Adaptive Kalman Filter for a soft elbow exoskeletoneng
dc.typearticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Telecomunicacionesspa
dc.type.spaArtículo
dc.identifier.doi10.1016/j.conengprac.2023.105774
dc.relation.citationvolume142
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationToro-Ossaba, A., Research Group in Computational Intelligence and Automation (GIICA), Universidad EIA, Antioquia, Envigado, 055428, Colombia
dc.affiliationTejada, J.C., Research Group in Computational Intelligence and Automation (GIICA), Universidad EIA, Antioquia, Envigado, 055428, Colombia
dc.affiliationRúa, S., Electronics and Telecommunications engineering Department, Universidad De Medellín, Antioquia, Medellín, 050026, Colombia
dc.affiliationNúñez, J.D., Research Group in Computational Intelligence and Automation (GIICA), Universidad EIA, Antioquia, Envigado, 055428, Colombia
dc.affiliationPeña, A., Grupo de Investigación en Información y Gestión, Escuela de Administración, Universidad EAFIT, Antioquia, Medellín, 050021, Colombia
dc.relation.referencesAbu-Dakka, F.J., Saveriano, M., Variable impedance control and learning—A review (2020) Frontiers in Robotics and AI, 7
dc.relation.referencesAdewuyi, A.A., Hargrove, L.J., Kuiken, T.A., Evaluating EMG feature and classifier selection for application to partial-hand prosthesis control (2016) Frontiers in Neurorobotics, 10, p. 15
dc.relation.referencesAl-Shuka, H.F., Leonhardt, S., Zhu, W.H., Song, R., Ding, C., Li, Y., Active impedance control of bioinspired motion robotic manipulators: An overview (2018) Applied Bionics and Biomechanics, 2018
dc.relation.referencesAsogbon, M.G., Samuel, O.W., Jiang, Y., Wang, L., Geng, Y., Sangaiah, A.K., Appropriate feature set and window parameters selection for efficient motion intent characterization towards intelligently smart EMG-PR System (2020) Symmetry, 12 (10), p. 1710
dc.relation.referencesBai, E.W., Identification of linear systems with hard input nonlinearities of known structure (2010) Lecture Notes in Control and Information Sciences, 404, pp. 259-270
dc.relation.referencesBardi, E., Gandolla, M., Braghin, F., Resta, F., Pedrocchi, A.L., Ambrosini, E., Upper limb soft robotic wearable devices: A systematic review (2022) Journal of Neuroengineering and Rehabilitation, 19 (1), pp. 1-17
dc.relation.referencesBeckerle, P., Salvietti, G., Unal, R., Prattichizzo, D., Rossi, S., Castellini, C., A human–robot interaction perspective on assistive and rehabilitation robotics (2017) Frontiers in Neurorobotics, 11 (MAY), p. 24
dc.relation.referencesBi, L., Feleke, A., Guan, C., A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration (2019) Biomedical Signal Processing and Control, 51, pp. 113-127
dc.relation.referencesBuongiorno, D., Barsotti, M., Barone, F., Bevilacqua, V., Frisoli, A., A linear approach to optimize an EMG-Driven neuromusculoskeletal model for movement intention detection in myo-control: A case study on shoulder and elbow joints (2018) Frontiers in Neurorobotics, 12 (November), p. 74
dc.relation.referencesCamardella, C., Barsotti, M., Buongiorno, D., Frisoli, A., Bevilacqua, V., Towards online myoelectric control based on muscle synergies-to-force mapping for robotic applications (2021) Neurocomputing, 452, pp. 768-778
dc.relation.referencesCavallaro, E.E., Rosen, J., Perry, J.C., Burns, S., Real-time myoprocessors for a neural controlled powered exoskeleton arm (2006) IEEE Transactions on Biomedical engineering, 53 (11), pp. 2387-2396
dc.relation.referencesChiaradia, D., Tiseni, L., Xiloyannis, M., Solazzi, M., Masia, L., Frisoli, A., An assistive soft wrist exosuit for flexion movements with an ergonomic reinforced glove (2021) Frontiers in Robotics and AI, 7, p. 182
dc.relation.referencesChu, C.Y., Patterson, R.M., Soft robotic devices for hand rehabilitation and assistance: A narrative review (2018) Journal of Neuroengineering and Rehabilitation, 15 (1), pp. 1-14
dc.relation.referencesCopaci, D., Serrano, D., Moreno, L., Blanco, D., A high-level control algorithm based on sEMG signalling for an elbow joint SMA exoskeleton (2018) Sensors, 18 (8), p. 2522
dc.relation.referencesCriswell, E., (2011) CRAM's introduction to surface electromyography, pp. 1-412. , Criswell E. 2nd ed. Jones and Bartlett
dc.relation.referencesda Silva, L.D.L., Pereira, T.F., Leithardt, V.R.Q., Seman, L.O., Zeferino, C.A., Hybrid impedance-admittance control for upper limb exoskeleton using electromyography (2020) Applied Sciences, 10 (20), p. 7146
dc.relation.referencesDe Leva, P., Adjustments to Zatsiorsky-Seluyanov's segment inertia parameters (1996) Journal of Biomechanics, 29 (9), pp. 1223-1230
dc.relation.referencesDella Santina, C., Katzschmann, R.K., Bicchi, A., Rus, D., Dynamic control of soft robots interacting with the environment (2018) 2018 IEEE International Conference on Soft Robotics, RoboSoft 2018, pp. 46-53. , Institute of Electrical and Electronics engineers Inc
dc.relation.referencesDenève, A., Moughamir, S., Afilal, L., Zaytoon, J., Control system design of a 3-DOF upper limbs rehabilitation robot (2008) Computer Methods and Programs in Biomedicine, 89 (2), pp. 202-214
dc.relation.referencesdu Plessis, T., Djouani, K., Oosthuizen, C., A review of active hand exoskeletons for rehabilitation and assistance (2021) Robotics, 10 (1), p. 40
dc.relation.referencesDurandau, G., Farina, D., Asín-Prieto, G., Dimbwadyo-Terrer, I., Lerma-Lara, S., Pons, J.L., Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling (2019) Journal of Neuroengineering and Rehabilitation, 16 (1), pp. 1-18
dc.relation.referencesDurandau, G., Farina, D., Sartori, M., Robust real-time musculoskeletal modeling driven by electromyograms (2017) IEEE Transactions on Biomedical engineering, 65 (3), pp. 556-564
dc.relation.referencesDurandau, G., Sartori, M., Bortole, M., Moreno, J.C., Pons, J.L., Farina, D., EMG-driven models of human-machine interaction in individuals wearing the H2 exoskeleton (2016) IFAC-PapersOnLine, 49 (32), pp. 200-203
dc.relation.referencesFuentes-Alvarez, R., Hernandez, J.H., Matehuala-Moran, I., Alfaro-Ponce, M., Lopez-Gutierrez, R., Salazar, S., Assistive robotic exoskeleton using recurrent neural networks for decision taking for the robust trajectory tracking (2022) Expert Systems with Applications, 193
dc.relation.referencesGrewal, M.S., Andrews, A.P., Kalman filtering: Theory and practice using MATLAB (2008), pp. 1-575. , 3rd ed. John Wiley and Sons
dc.relation.referencesGui, K., Liu, H., Zhang, D., A practical and adaptive method to achieve EMG-Based torque estimation for a robotic exoskeleton (2019) IEEE/ASME Transactions on Mechatronics, 24 (2), pp. 483-494
dc.relation.referencesGull, M.A., Bai, S., Bak, T., A review on design of upper limb exoskeletons (2020) Robotics, 9 (1), p. 16
dc.relation.referencesHahne, J.M., Bießmann, F., Jiang, N., Rehbaum, H., Farina, D., Meinecke, F.C., Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control (2014) IEEE Transactions on Neural Systems and Rehabilitation engineering, 22 (2), pp. 269-279
dc.relation.referencesHao, Y., Visell, Y., Beyond soft hands: Efficient grasping with non-anthropomorphic soft grippers (2021) Frontiers in Robotics and AI, p. 210
dc.relation.referencesHayashibe, M., Guiraud, D., Voluntary EMG-to-force estimation with a multi-scale physiological muscle model (2013) BioMedical engineering Online, 12 (1), pp. 1-18
dc.relation.referencesHu, Y., Li, Z., Li, G., Yuan, P., Yang, C., Song, R., Development of sensory-motor fusion-based manipulation and grasping control for a robotic hand-eye system (2017) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (7), pp. 1169-1180
dc.relation.referencesIgual, C., Pardo, L.A., Hahne, J.M., Igual, J., Myoelectric control for upper limb prostheses (2019) Electronics, 8 (11), p. 1244
dc.relation.referencesIoannou, P.A., Sun, J., Robust adaptive control (2012), Dover Dover Publications Mineola
dc.relation.referencesIson, M., Artemiadis, P., The role of muscle synergies in myoelectric control: Trends and challenges for simultaneous multifunction control (2014) Journal of Neural engineering, 11 (5)
dc.relation.referencesJabbari Asl, H., Katagiri, K., Narikiyo, T., Yamashita, M., Kawanishi, M., Augmenting human power by assistive robots: Application of adaptive neural networks (2021) Control engineering Practice, 110
dc.relation.referencesJaber, H.A., Rashid, M.T., Fortuna, L., Online myoelectric pattern recognition based on hybrid spatial features (2021) Biomedical Signal Processing and Control, 66
dc.relation.referencesJiang, Y., Gao, W., Na, J., Zhang, D., Hämäläinen, T.T., Stojanovic, V., Value iteration and adaptive optimal output regulation with assured convergence rate (2022) Control engineering Practice, 121
dc.relation.referencesJung, M.K., Muceli, S., Rodrigues, C., Megia-Garcia, A., Pascual Valdunciel, A., Del-Ama, A.J., Intramuscular EMG-driven musculoskeletal modelling: Towards implanted muscle interfacing in spinal cord injury patients (2021) IEEE Transactions on Biomedical engineering
dc.relation.referencesKhalil, H., Nonlinear systems (2001), 3rd ed. Pearson
dc.relation.referencesKhan, R., Malik, F.M., Raza, A., Mazhar, N., Ullah, H., Umair, M., Robust nonlinear control design and disturbance estimation for ball and beam system (2020) 2020 3rd international conference on computing, mathematics and engineering technologies: Idea to innovation for building the knowledge economy, , Institute of Electrical and Electronics engineers Inc
dc.relation.referencesKiguchi, K., Hayashi, Y., An EMG-based control for an upper-limb power-assist exoskeleton robot (2012) IEEE Transactions on Systems, Man and Cybernetics, Part B, 42 (4), pp. 1064-1071
dc.relation.referencesKopke, J.V., Hargrove, L.J., Ellis, M.D., Applying LDA-based pattern recognition to predict isometric shoulder and elbow torque generation in individuals with chronic stroke with moderate to severe motor impairment (2019) Journal of Neuroengineering and Rehabilitation, 16 (1), pp. 1-11
dc.relation.referencesLenzi, T., De Rossi, S.M.M., Vitiello, N., Carrozza, M.C., Intention-based EMG control for powered exoskeletons (2012) IEEE Transactions on Biomedical engineering, 59 (8), pp. 2180-2190
dc.relation.referencesLi, Z., Huang, Z., He, W., Su, C.Y., Adaptive impedance control for an upper limb robotic exoskeleton using biological signals (2017) IEEE Transactions on Industrial Electronics, 64 (2), pp. 1664-1674
dc.relation.referencesLi, Z., Xu, C., Wei, Q., Shi, C., Su, C.Y., Human-inspired control of dual-arm exoskeleton robots with force and impedance adaptation (2020) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50 (12), pp. 5296-5305
dc.relation.referencesLiu, J., Li, X., Li, G., Zhou, P., EMG feature assessment for myoelectric pattern recognition and channel selection: A study with incomplete spinal cord injury (2014) Medical engineering & Physics, 36 (7), pp. 975-980
dc.relation.referencesLiu, G., Sun, N., Liang, D., Chen, Y., Yang, T., Fang, Y., Neural network-based adaptive command filtering control for pneumatic artificial muscle robots with input uncertainties (2022) Control engineering Practice, 118
dc.relation.referencesLiu, G., Sun, N., Yang, T., Liu, Z., Fang, Y., Equivalent-input-disturbance rejection-based adaptive motion control for pneumatic artificial muscle arms via hysteresis compensation models (2023) Control engineering Practice, 138
dc.relation.referencesLloyd, D.G., Besier, T.F., An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo (2003) Journal of Biomechanics, 36 (6), pp. 765-776
dc.relation.referencesLorrain, T., Jiang, N., Farina, D., Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses (2011) Journal of Neuroengineering and Rehabilitation, 8 (1), pp. 1-9
dc.relation.referencesLu, L., Wu, Q., Chen, X., Shao, Z., Chen, B., Wu, H., Development of a sEMG-based torque estimation control strategy for a soft elbow exoskeleton (2019) Robotics and Autonomous Systems, 111, pp. 88-98
dc.relation.referencesLujan-Moreno, G.A., Howard, P.R., Rojas, O.G., Montgomery, D.C., Design of experiments and response surface methodology to tune machine learning hyperparameters, with a random forest case-study (2018) Expert Systems with Applications, 109, pp. 195-205
dc.relation.referencesLv, X., Han, J., Yang, C., Cong, D., Model reference adaptive impedance control in lower limbs rehabilitation robot (2017) 2017 IEEE International Conference on Information and Automation, ICIA 2017, pp. 254-259. , Institute of Electrical and Electronics engineers Inc
dc.relation.referencesMadgwick, S., An efficient orientation filter for inertial and inertial/magnetic sensor arrays: Tech. rep. (2010), p. 32. , https://x-io.co.uk/open-source-imu-and-ahrs-algorithms/, University of Bristol URL
dc.relation.referencesMasia, L., Xiloyannis, M., Khanh, D.B., Wilson, A.C., Contu, S., Yongtae, K.G., Actuation for robot-aided rehabilitation: Design and control strategies (2018) Rehabilitation Robotics, pp. 47-61
dc.relation.referencesMendes Souza, G.C., Moreno, R.L., Netlab MLP - Performance evaluation for pattern recognition in myoletric signal (2018) Procedia computer science, 130, pp. 932-938. , Elsevier B.V
dc.relation.referencesMerletti, R., Farina, D., (2016) Surface electromyography: Physiology, engineering and applications, pp. 1-570. , Merletti R. Farina D. Wiley-IEEE Press
dc.relation.referencesNam, C., Rong, W., Li, W., Cheung, C., Ngai, W., Cheung, T., An exoneuromusculoskeleton for self-help upper limb rehabilitation after stroke (2020) Soft Robotics
dc.relation.referencesNguyen, N.T., Model-Reference adaptive control: A primer (2018) Advanced textbooks in control and signal processing, p. 444. , 1st ed. Springer Cham
dc.relation.referencesNguyen, D.H., Lowenberg, M.H., Neild, S.A., Identifying limits of linear control design validity in nonlinear systems: A continuation-based approach (2021) Nonlinear Dynamics, 104 (2), pp. 901-921
dc.relation.referencesPark, J.H., Park, G., Kim, H.Y., Lee, J.Y., Ham, Y., Hwang, D., A comparison of the effects and usability of two exoskeletal robots with and without robotic actuation for upper extremity rehabilitation among patients with stroke: A single-blinded randomised controlled pilot study (2020) Journal of Neuroengineering and Rehabilitation, 17 (1), pp. 1-12
dc.relation.referencesPau, J.W., Xie, S.S., Pullan, A.J., Neuromuscular interfacing: Establishing an EMG-driven model for the human elbow joint (2012) IEEE Transactions on Biomedical engineering, 59 (9), pp. 2586-2593
dc.relation.referencesPeternel, L., Noda, T., Petrič, T., Ude, A., Morimoto, J., Babič, J., Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation (2016) PLoS One, 11 (2)
dc.relation.referencesPlagenhoef, S., Gaynor Evans, F., Abdelnour, T., Anatomical data for analyzing human motion (1983) Research Quarterly for Exercise and Sport, 54 (2), pp. 169-178
dc.relation.referencesPopescu, F., Hidler, J.M., Rymer, W.Z., Elbow impedance during goal-directed movements (2003) Experimental Brain Research, 152 (1), pp. 17-28
dc.relation.referencesRiani, A., Madani, T., Benallegue, A., Djouani, K., Adaptive integral terminal sliding mode control for upper-limb rehabilitation exoskeleton (2018) Control engineering Practice, 75, pp. 108-117
dc.relation.referencesSartori, M., Maculan, M., Pizzolato, C., Reggiani, M., Farina, D., Modeling and simulating the neuromuscular mechanisms regulating ankle and knee joint stiffness during human locomotion (2015) Journal of Neurophysiology, 114 (4), pp. 2509-2527
dc.relation.referencesSartori, M., Reggiani, M., Pagello, E., Lloyd, D.G., Modeling the human knee for assistive technologies (2012) IEEE Transactions on Biomedical engineering, 59 (9), pp. 2642-2649
dc.relation.referencesSharifi, M., Behzadipour, S., Vossoughi, G.R., Model reference adaptive impedance control of rehabilitation robots in operational space (2012) Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 1698-1703
dc.relation.referencesSimao, M., Mendes, N., Gibaru, O., Neto, P., A review on electromyography decoding and pattern recognition for human-machine interaction (2019) IEEE Access, 7, pp. 39564-39582
dc.relation.referencesSimon, D., Optimal state estimation: Kalman, H∞, and nonlinear approaches (2006), pp. 1-526. , 1st ed. John Wiley & Sons, Inc Hoboken, New Jersey
dc.relation.referencesSimon, A.M., Hargrove, L.J., Lock, B.A., Kuiken, T.A., A decision-based velocity ramp for minimizing the effect of misclassifications during real-time pattern recognition control (2011) IEEE Transactions on Biomedical engineering, 58 (8), pp. 2360-2368
dc.relation.referencesSingh, R.M., Chatterji, S., Trends and challenges in EMG based control scheme of exoskeleton robots-A review (2012) International Journal of Scientific & engineering Research, 3 (8)
dc.relation.referencesSmith, L.H., Hargrove, L.J., Lock, B.A., Kuiken, T.A., Determining the optimal window length for pattern recognition-based myoelectric control: Balancing the competing effects of classification error and controller delay (2011) IEEE Transactions on Neural Systems and Rehabilitation engineering, 19 (2), pp. 186-192
dc.relation.referencesSmith, L.H., Kuiken, T.A., Hargrove, L.J., Real-time simultaneous and proportional myoelectric control using intramuscular EMG (2014) Journal of Neural engineering, 11 (6)
dc.relation.referencesSong, X., Sun, P., Song, S., Stojanovic, V., Finite-time adaptive neural resilient DSC for fractional-order nonlinear large-scale systems against sensor-actuator faults (2023) Nonlinear Dynamics, 111 (13), pp. 12181-12196
dc.relation.referencesSong, X., Wu, C., Stojanovic, V., Song, S., 1 bit encoding–decoding-based event-triggered fixed-time adaptive control for unmanned surface vehicle with guaranteed tracking performance (2023) Control engineering Practice, 135
dc.relation.referencesSouza, R.S., de Castro Martins, T., Furtado, G.P., Forner-Cordero, A., Model reference adaptive impedance controller design for modular exoskeleton (2018) IFAC-PapersOnLine, 51 (27), pp. 345-349
dc.relation.referencesSu, H., Li, Z., Li, G., Yang, C., EMG-based neural network control of an upper-limb power-assist exoskeleton robot (2013) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7952 LNCS, pp. 204-211
dc.relation.referencesThuruthel, T.G., Ansari, Y., Falotico, E., Laschi, C., Control strategies for soft robotic manipulators: A survey (2018) Soft Robotics, 5 (2), pp. 149-163
dc.relation.referencesTrivedi, D., Rahn, C.D., Kier, W.M., Walker, I.D., Soft robotics: Biological inspiration, state of the art, and future research (2008) Applied Bionics and Biomechanics, 5 (3), pp. 99-117
dc.relation.referencesTrumic, M., Della Santina, C., Jovanovic, K., Fagiolini, A., Adaptive control of soft robots based on an enhanced 3D augmented rigid robot matching (2021) Proceedings of the American Control Conference, vol. 2021-May, pp. 4991-4996. , Institute of Electrical and Electronics engineers Inc
dc.relation.referencesWei, Q., Li, Z., Zhao, K., Kang, Y., Su, C.Y., Synergy-based control of assistive lower-limb exoskeletons by skill transfer (2020) IEEE/ASME Transactions on Mechatronics, 25 (2), pp. 705-715
dc.relation.referencesXu, W., Chu, B., Rogers, E., Iterative learning control for robotic-assisted upper limb stroke rehabilitation in the presence of muscle fatigue (2014) Control engineering Practice, 31, pp. 63-72
dc.relation.referencesYang, C., Zeng, C., Fang, C., He, W., Li, Z., A DMPs-based framework for robot learning and generalization of humanlike variable impedance skills (2018) IEEE/ASME Transactions on Mechatronics, 23 (3), pp. 1193-1203
dc.relation.referencesYao, S., Zhuang, Y., Li, Z., Song, R., Adaptive admittance control for an ankle exoskeleton using an EMG-driven musculoskeletal model (2018) Frontiers in Neurorobotics, 12 (APR), p. 16
dc.relation.referencesYe, W., Li, Z., Su, C.Y., Development and human-like control of an upper limb rehabilitation exoskeleton using sEMG bio-feedback (2012) 2012 IEEE International Conference on Mechatronics and Automation, pp. 2077-2082
dc.relation.referencesYe, W., Li, Z., Yang, C., Chen, F., Su, C.Y., Motion detection enhanced control of an upper limb exoskeleton robot for rehabilitation training (2017) International Journal of Humanoid Robotics, 14 (1)
dc.relation.referencesYu, H., Choi, I.S., Han, K.L., Choi, J.Y., Chung, G., Suh, J., Development of a upper-limb exoskeleton robot for refractory construction (2018) Control engineering Practice, 72, pp. 104-113
dc.relation.referencesZhao, C., Control design of upper limb rehabilitation exoskeleton robot based on long and short-term memory network (2021) Journal of Physics: Conference Series, 1986 (1)
dc.relation.referencesZhu, Y., Wu, Q., Chen, B., Zhao, Z., Design and voluntary control of variable stiffness exoskeleton based on sEMG driven model (2022) IEEE Robotics and Automation Letters, 7 (2), pp. 5787-5794
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