Abstract
This paper proposed an adaptive control strategy based on RBF (radial basis function) neural network and PD Computed-Torque algorithm for precise tracking of a predefined trajectory. This control strategy can not only give a small tracking error, but also have a good robustness to the modeling errors of the robot dynamics equation and also to the system friction. With this control algorithm, the robot can work in assist-as-needed mode by detecting the human active joint torque. At last, a simulation result using matlab simulink is given to illustrate the effectiveness of our control strategy.
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Zhang, F. et al. (2010). An Adaptive RBF Neural Network Control Strategy for Lower Limb Rehabilitation Robot. In: Liu, H., Ding, H., Xiong, Z., Zhu, X. (eds) Intelligent Robotics and Applications. ICIRA 2010. Lecture Notes in Computer Science(), vol 6425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16587-0_39
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DOI: https://doi.org/10.1007/978-3-642-16587-0_39
Publisher Name: Springer, Berlin, Heidelberg
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