An adaptive RBF neural network control strategy for lower limb rehabilitation robot
Intelligent Robotics and Applications: Third International Conference, ICIRA …, 2010•Springer
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 …
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 …
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.
Springer
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