The structural synthesis of Tendon-Driven manipulators having a pseudotriangular structure matrix
International Journal of Robotics Research
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators
Neural net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
Stochastic choice of basis functions in adaptive function approximation and the functional-link net
IEEE Transactions on Neural Networks
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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.