{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T01:50:40Z","timestamp":1779933040228,"version":"3.53.1"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T00:00:00Z","timestamp":1773446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["52175014"],"award-info":[{"award-number":["52175014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Key Research and Development Program of Jiangsu Province","award":["BE2023813"],"award-info":[{"award-number":["BE2023813"]}]},{"name":"the Open Research Fund of State Key Laboratory of Digital Medical Engineering","award":["2024-M09"],"award-info":[{"award-number":["2024-M09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Upper limb motor dysfunction resulting from stroke requires effective rehabilitation solutions; however, current exoskeletons are limited by single-input control, inadequate adaptation to various rehabilitation stages, and restriction to one limb. This study presents the development of a three-degree-of-freedom upper limb rehabilitation exoskeleton with three core innovations: (1) a neuro-fuzzy adaptive admittance control architecture that integrates human\u2013robot interaction force and joint angular velocity as dual inputs for real-time damping adjustment, enabling accurate capture of dynamic movement intentions; (2) a Brunnstrom stage-specific fuzzy rule base that directly links clinical rehabilitation needs to adaptive control parameters; (3) a bilateral adaptable mechanical structure, allowing dual-upper limb training to enhance practical application. By combining radial basis function (RBF) neural network-based adaptive proportional\u2013integral\u2013derivative (PID) control with fuzzy variable-parameter admittance control, the system achieves a maximum trajectory tracking error of less than 1.2\u00b0 and a root mean square (RMS) error of \u22640.13\u00b0. Trajectory tracing experiments confirm an RMS error of 2.99 mm for a circular trajectory at Bd = 2. The proposed strategy, validated through position tracking, admittance interaction, and trajectory tracing experiments, effectively balances tracking accuracy and human\u2013machine compliance, providing valuable technical support for robot-assisted upper limb rehabilitation.<\/jats:p>","DOI":"10.3390\/s26061838","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T08:41:26Z","timestamp":1773650486000},"page":"1838","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development of a Neural-Fuzzy-Based Variable Admittance Control Strategy for an Upper Limb Rehabilitation Exoskeleton"],"prefix":"10.3390","volume":"26","author":[{"given":"Yixing","family":"Shi","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keyi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yehong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7774-6092","authenticated-orcid":false,"given":"Qingcong","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1016\/S1474-4422(24)00369-7","article-title":"Global, regional, and national burden of stroke and its risk factors, 1990\u20132021: A systematic analysis for the Global Burden of Disease Study 2021","volume":"23","author":"Feigin","year":"2024","journal-title":"Lancet Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.cogr.2024.09.001","article-title":"Emerging trends in human upper extremity rehabilitation robot","volume":"4","author":"Hasan","year":"2024","journal-title":"Cogn. Robot."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hou, K., Khan, M.M.R., and Rahman, M.H. (2025). An Accessible AI-Assisted Rehabilitation System for Guided Upper Limb Therapy. Sensors, 25.","DOI":"10.3390\/s25196239"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, T., Tao, H., Liu, F., Hu, B., Wu, M., and Yu, H. (2024). Research on adaptive impedance control technology of upper limb rehabilitation robot based on impedance parameter prediction. Front. Bioeng. Biotechnol., 11.","DOI":"10.3389\/fbioe.2023.1332689"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Qassim, H.M., and Wan Hasan, W.Z. (2020). A Review on Upper Limb Rehabilitation Robots. Appl. Sci., 10.","DOI":"10.3390\/app10196976"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, C., Peng, L., Hou, Z.-G., Wang, W., and Su, T. (2019, January 6\u20139). A Novel Assist-As-Needed Controller Based on Fuzzy-Logic Inference and Human Impedance Identification for Upper-Limb Rehabilitation. Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China.","DOI":"10.1109\/SSCI44817.2019.9002868"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"pzab010","DOI":"10.1093\/ptj\/pzab010","article-title":"Robot-Assisted Therapy for Upper Extremity Motor Impairment After Stroke: A Systematic Review and Meta-Analysis","volume":"101","author":"Wu","year":"2021","journal-title":"Phys. Ther."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113314","DOI":"10.1016\/j.ymssp.2025.113314","article-title":"Development and DMP-learning-based predictive control of a shoulder-hip assistive exoskeleton for muscular augmentation","volume":"239","author":"Wu","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1007\/s11465-021-0651-5","article-title":"Optimum design and preliminary experiments of a novel parallel end traction apparatus for upper-limb rehabilitation","volume":"16","author":"Zuo","year":"2021","journal-title":"Front. Mech. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zou, Y., Wu, X., Zhang, B., Zhang, Q., Zhang, A., and Qin, T. (2022). Stiffness Analysis of Parallel Cable-Driven Upper Limb Rehabilitation Robot. Micromachines, 13.","DOI":"10.3390\/mi13020253"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bououden, S., Brahmi, B., Iqbal, N., Fareh, R., and Rahman, M.H. (2025). Disturbance-Resilient Flatness-Based Control for End-Effector Rehabilitation Robotics. Actuators, 14.","DOI":"10.20944\/preprints202505.2123.v1"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1108\/IR-12-2023-0312","article-title":"Fuzzy logic system-based force tracking control of robot in highly dynamic environments","volume":"51","author":"Liu","year":"2024","journal-title":"Ind. Robot Int. J. Robot. Res. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5219","DOI":"10.1007\/s10072-021-05185-3","article-title":"Robotic rehabilitation for end-effector device and botulinum toxin in upper limb rehabilitation in chronic post-stroke patients: An integrated rehabilitative approach","volume":"42","author":"Paolucci","year":"2021","journal-title":"Neurol. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Song, T., Zhang, K., Yan, Z., Li, Y., Guo, S., and Li, X. (2025). Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMG. Sensors, 25.","DOI":"10.3390\/s25041057"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chuchnowska, I., Mikulska, J., Burkacki, M., Chmura, M., Chrzan, M., Kalinowski, J., Sucho\u0144, S., Ples, M., Sobiech, M., and Szaflik, P. (2025). Comparative Analysis of Passive Movement During Robot-Assisted and Therapist-Led Rehabilitation Exercises. Sensors, 25.","DOI":"10.3390\/s25175334"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1109\/TRO.2019.2930915","article-title":"An Elbow Exoskeleton for Upper Limb Rehabilitation with Series Elastic Actuator and Cable-Driven Differential","volume":"35","author":"Chen","year":"2019","journal-title":"IEEE Trans. Robot."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cai, S.Q., Wu, W.F., and Xie, L.H. (2020, January 20\u201323). Dual-Arm Upper Limb Rehabilitation Robot: Mechanism Design and Preliminary Experiments. Proceedings of the 6th International Conference on Control, Automation and Robotics (ICCAR), Singapore.","DOI":"10.1109\/ICCAR49639.2020.9108019"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tucan, P., Vaida, C., Ulinici, I., Banica, A., Burz, A., Pop, N., Birlescu, I., Gherman, B., Plitea, N., and Antal, T. (2021). Optimization of the ASPIRE Spherical Parallel Rehabilitation Robot Based on Its Clinical Evaluation. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18063281"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.1109\/TMECH.2022.3224423","article-title":"CURER: A Lightweight Cable-Driven Compliant Upper Limb Rehabilitation Exoskeleton Robot","volume":"28","author":"Qian","year":"2023","journal-title":"IEEE Asme Trans. Mechatron."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6696","DOI":"10.1109\/LRA.2025.3570892","article-title":"A sEMG-based Muscle-Compensation-Error Elimination Module for the Home-Based Bilateral Rehabilitation System","volume":"10","author":"Wang","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Feng, G., Zhang, J., Zuo, G., Li, M., Jiang, D., and Yang, L. (2022). Dual-Modal Hybrid Control for an Upper-Limb Rehabilitation Robot. Machines, 10.","DOI":"10.3390\/machines10050324"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3437","DOI":"10.1109\/TRO.2023.3286073","article-title":"Human\u2013Robot Interaction Evaluation-Based AAN Control for Upper Limb Rehabilitation Robots Driven by Series Elastic Actuators","volume":"39","author":"Han","year":"2023","journal-title":"IEEE Trans. Robot."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, J., Zuo, G., Feng, G., and Zhang, X. (2022). Assist-As-Needed Control Strategy of Bilateral Upper Limb Rehabilitation Robot Based on GMM. Machines, 10.","DOI":"10.3390\/machines10020076"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2100","DOI":"10.1109\/TMECH.2020.2992090","article-title":"Field-Based Assist-as-Needed Control Schemes for Rehabilitation Robots","volume":"25","author":"Asl","year":"2020","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1177\/01423312241245764","article-title":"Adaptive backstepping sliding mode subject-cooperative control for a pediatric lower-limb exoskeleton robot","volume":"47","author":"Narayan","year":"2024","journal-title":"Trans. Inst. Meas. Control"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5903","DOI":"10.1109\/LRA.2024.3398565","article-title":"A Variable-Admittance Assist-As-Needed Controller for Upper-Limb Rehabilitation Exoskeletons","volume":"9","author":"Zarrin","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1109\/TASE.2019.2954769","article-title":"Impedance-Controlled Variable Stiffness Actuator for Lower Limb Robot Applications","volume":"17","author":"Liu","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"23329","DOI":"10.1007\/s00521-021-06037-z","article-title":"Iterative learning-based path control for robot-assisted upper-limb rehabilitation","volume":"35","author":"Maqsood","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"123807","DOI":"10.1109\/ACCESS.2019.2938566","article-title":"Adaptive Admittance Control of an Upper Extremity Rehabilitation Robot With Neural-Network-Based Disturbance Observer","volume":"7","author":"Wu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, R., Zhou, J., and Song, R. (2021, January 3\u20135). Adaptive Admittance Control Based on Linear Quadratic Regulation Optimization Technique for a Lower Limb Rehabilitation Robot. Proceedings of the 2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM), Chongqing, China.","DOI":"10.1109\/ICARM52023.2021.9536058"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111104","DOI":"10.1016\/j.jbiomech.2022.111104","article-title":"EMG-driven fatigue-based self-adapting admittance control of a hand rehabilitation robot","volume":"138","author":"Mashayekhi","year":"2022","journal-title":"J. Biomech."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TMECH.2024.3392604","article-title":"Barrier Lyapunov Function-Based Fuzzy Adaptive Admittance Control of an Upper Limb Exoskeleton Using RBFNN Compensation","volume":"30","author":"Wu","year":"2025","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Abdullahi, A.M., Haruna, A., and Chaichaowarat, R. (2024). Hybrid Adaptive Impedance and Admittance Control Based on the Sensorless Estimation of Interaction Joint Torque for Exoskeletons: A Case Study of an Upper Limb Rehabilitation Robot. J. Sens. Actuator Netw., 13.","DOI":"10.3390\/jsan13020024"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhu, C., Zhang, Y., Zhou, J., and Shan, Z. (2025, January 28\u201330). Variable Admittance Compliance Control of Upper Limb Training and Rehabilitation Robot Based on Genetic Algorithm Optimization. Proceedings of the 2025 International Conference on Health Big Data 2025, Kunming, China.","DOI":"10.1145\/3733006.3733036"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1109\/TMRB.2024.3503899","article-title":"Design and EMG-EEG Fusion-Based Admittance Control of a Hand Exoskeleton With Series Elastic Actuators","volume":"7","author":"Zou","year":"2025","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102935","DOI":"10.1016\/j.mechatronics.2022.102935","article-title":"Variable admittance time-delay control of an upper limb rehabilitation robot based on human stiffness estimation","volume":"90","author":"Wu","year":"2023","journal-title":"Mechatronics"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.isatra.2023.10.028","article-title":"Finite-time adaptive super-twisting sliding mode control for autonomous robotic manipulators with actuator faults","volume":"144","author":"Hu","year":"2024","journal-title":"ISA Trans."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shimizu, M., Hanafusa, M., and Ishikawa, J. (2022, January 18\u201320). Operability Improvement of Human-Robot Collaboration by Human-Adaptive Impedance Control Based on Human Arm Stiffness Estimation. Proceedings of the 2022 IEEE 17th International Conference on Advanced Motion Control (AMC), Padova, Italy.","DOI":"10.1109\/AMC51637.2022.9729296"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, X., Hu, D., Zhang, R., Pan, Z., Chen, Y., Xie, L., Luo, J., and Zhu, Y. (2022). Interpretable evaluation for the Brunnstrom recovery stage of the lower limb based on wearable sensors. Front. Neuroinform., 16.","DOI":"10.3389\/fninf.2022.1006494"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4163","DOI":"10.1109\/TFUZZ.2024.3387045","article-title":"Investigating Formation and Containment Problem for Nonlinear Multiagent Systems by Interval Type-2 Fuzzy Sliding Mode Tracking Approach","volume":"32","author":"Chang","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"33042","DOI":"10.1038\/s41598-025-18242-0","article-title":"Adaptive fuzzy-recurrent neural network tuned fractional-order distributed control for robust frequency regulation in multi-microgrid systems","volume":"15","author":"Kandasamy","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xie, F., Guan, X., Peng, X., Zeng, Y., Wang, Z., and Qin, T. (2024). Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System. Sustainability, 16.","DOI":"10.3390\/su16093823"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"16878132221109994","DOI":"10.1177\/16878132221109994","article-title":"Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control","volume":"14","author":"Gao","year":"2022","journal-title":"Adv. Mech. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6739","DOI":"10.1007\/s11071-024-10449-6","article-title":"State filtered disturbance rejection control","volume":"113","author":"Yang","year":"2024","journal-title":"Nonlinear Dyn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1109\/TASE.2025.3639494","article-title":"Multilayer Neuroadaptive Reinforcement Learning of Disturbed Nonlinear Systems via Actor-Critic Mechanism","volume":"23","author":"Yang","year":"2026","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Salahuddin, H., Imdad, K., Chaudhry, M.U., Nazarenko, D., Bolshev, V., and Yasir, M. (2022). Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller. Appl. Sci., 12.","DOI":"10.3390\/app12094647"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/6\/1838\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T05:16:53Z","timestamp":1773811013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/6\/1838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":46,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["s26061838"],"URL":"https:\/\/doi.org\/10.3390\/s26061838","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}