Adaptive control of a robotic exoskeleton for enhanced knee rehabilitation
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DOI:
https://doi.org/10.15625/0866-7136/23798Keywords:
rehabilitation, machine learning, variable stiffness actuator, spring designAbstract
Variable stiffness actuators (VSAs) offer significant potential for diverse applications due to their ability to adjust stiffness in response to operational requirements. The proposed VSA comprises 12 pairs of outer pulleys and six pairs of inner pulleys, uniformly arranged around a central axis and connected in series to a spring via a cable. Rotation of the inner pulleys around the center extends the spring, generating an elastic force that is transmitted through the cable to the output link, producing output torque and stiffness. The output stiffness varies with the angle of rotation, posing challenges for control system design. This study proposes machine learning-based approaches to achieve accurate and low-latency control. Specifically, neural network models are developed to estimate the spring preload at various actuator positions, enabling precise control of the desired stiffness. The results show that the Feedforward Neural Network enables the actuator to achieve a stiffness with a root-mean-square error of 0.0073 Ncm/degree.
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Quỹ Đổi mới sáng tạo Vingroup
Grant numbers VINIF.2025.DA065
