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      Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm

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          Abstract

          The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through an algorithm simulation test and two-link simulation experiment, the algorithm improves the prediction accuracy by 14.23 ± 0.5%, and the sensor data is smooth. Input the predicted signal into the two-link model, and use the calculated torque method to verify the prediction accuracy data and smoothness. The simulation results showed that the algorithm can predict the joint angle of the human body and can be used for the follow-up control of the swinging legs of the exoskeleton.

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          Particle swarm optimization

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            Dynamical movement primitives: learning attractor models for motor behaviors.

            Nonlinear dynamical systems have been used in many disciplines to model complex behaviors, including biological motor control, robotics, perception, economics, traffic prediction, and neuroscience. While often the unexpected emergent behavior of nonlinear systems is the focus of investigations, it is of equal importance to create goal-directed behavior (e.g., stable locomotion from a system of coupled oscillators under perceptual guidance). Modeling goal-directed behavior with nonlinear systems is, however, rather difficult due to the parameter sensitivity of these systems, their complex phase transitions in response to subtle parameter changes, and the difficulty of analyzing and predicting their long-term behavior; intuition and time-consuming parameter tuning play a major role. This letter presents and reviews dynamical movement primitives, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques. The essence of our approach is to start with a simple dynamical system, such as a set of linear differential equations, and transform those into a weakly nonlinear system with prescribed attractor dynamics by means of a learnable autonomous forcing term. Both point attractors and limit cycle attractors of almost arbitrary complexity can be generated. We explain the design principle of our approach and evaluate its properties in several example applications in motor control and robotics.
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              Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art

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                Author and article information

                Contributors
                Journal
                Appl Bionics Biomech
                Appl Bionics Biomech
                ABB
                Applied Bionics and Biomechanics
                Hindawi
                1176-2322
                1754-2103
                2021
                21 January 2021
                : 2021
                : 8850348
                Affiliations
                1College of Optoelectronics Science and Engineering, Soochow University, Suzhou Jiangsu Province, 215000, China
                2Micro-Nano Automation Institute, Jiangsu Industrial Technology Research Institute, Suzhou, Jiangsu Province 215131, China
                3Shanghai Huangpu District Fire Rescue Detachment, Shanghai 200001, China
                Author notes

                Academic Editor: Jose Merodio

                Author information
                https://orcid.org/0000-0003-1356-7295
                https://orcid.org/0000-0003-4994-1330
                https://orcid.org/0000-0001-7984-174X
                https://orcid.org/0000-0002-1365-4869
                https://orcid.org/0000-0003-0286-3064
                Article
                10.1155/2021/8850348
                7843196
                dda8798e-4483-4905-a3b2-ed593f7fe8d5
                Copyright © 2021 Shijia Zha et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 May 2020
                : 15 October 2020
                : 8 January 2021
                Funding
                Funded by: Natural Science Research General Program of Higher Education of Jiangsu Province
                Award ID: 16KJB510040
                Categories
                Research Article

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