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The dependence of the control algorithm on the prior knowledge of the robotic exoskeleton dynamics model is reduced, and the influence of the system uncertainties are compensated by using the model-free adaptive sliding mode controller based on data-driven methodology and neural network estimator, which improves the robustness of the system. Finally, real-time experimental results show that the control scheme proposed in this paper achieves better control performances with good robustness with respect to system uncertainties and external wind disturbances compared with the model-free adaptive control scheme and model-free sliding mode adaptive control scheme.<\/jats:p>","DOI":"10.1155\/2020\/8327456","type":"journal-article","created":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T19:32:01Z","timestamp":1584473521000},"page":"1-10","source":"Crossref","is-referenced-by-count":4,"title":["Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2012-3142","authenticated-orcid":true,"given":"Xiangjian","family":"Chen","sequence":"first","affiliation":[{"name":"Jiangsu University of Science and Technology, School of Computer, Zhenjiang, 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