Abstract
The paper proposes an upper limb exoskeleton interaction force control method based on active motion intention recognition to address the problems of lack of patient active participation willingness and poor flexibility of the rehabilitation training control method during the middle and late stages of rehabilitation training. Initially, this paper establishes the human-machine interaction force model by analyzing the human-machine interaction dynamics and statics of the upper limb rehabilitation exoskeleton. Moreover, a method for active intention recognition based on interaction force is proposed using a neural network with a radial basis. During the active training process, the adaptive impedance control method based on the interaction force is proposed to increase the range of motion during rehabilitation. The bounded matrix parameter adaptive matrix control method is compared, and experimental results demonstrate that the method proposed in this paper can effectively reduce the human-machine interaction force, improve the flexibility of the exoskeleton robotic arm, and increase the patient’s willingness to participate actively.
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Acknowledgment
This work was supported by the Science and technology plan project of Xi’an city (Grant no. 21XJZZ0079), the Natural Science Foundation of Shaanxi Province (Grant No. 2020JM-131 and 2020KW-058).
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Zhao, C., Cao, Y., Liu, X., Wang, W. (2023). Research on Interactive Force Control Method of Upper Limb Exoskeleton Based on Active Intention Recognition. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14268. Springer, Singapore. https://doi.org/10.1007/978-981-99-6486-4_31
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DOI: https://doi.org/10.1007/978-981-99-6486-4_31
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