Position Control Based on Static Neural Networks of Anthropomorphic Robotic Fingers
A dynamic neurocontroller for positioning robot manipulators with a tendon-driven transmission system has been developed allowing to track desired trajectories and reject external disturbances. The controller is characterised as providing motor torques rather than joint torques. In this sense, the redundant problem regarded with the tendon-driven transmission systems is solved using neural networks that are able to learned the linear transformation that maps motor torques into joint torques. The neurocontroller not only learn the dynamics associated with the robot manipulator but also the parameters attached to the transmission system such as pulley radii. A theorem relying on the Lyapunov theory has been developed, guaranteeing the uniformly ultimately bounded stability of the whole system and providing both the control laws and weight updating laws.
KeywordsTracking Error Robot Manipulator Joint Torque Motor Torque Neural Network Control
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