Neural Networks Models for Control and Identification of a Two Links Robotic Manipulator
نویسنده
چکیده
Abstract—several neural networks controllers for robotics manipulators have been developed during the last decades due to their capability to learn the dynamics properties and the improvements in the global stability of the system. In this paper, two control and identification schemes for a two links robotic manipulator implementing neural networks are presented. A multilayer feedforward neural network and a model reference adaptive controller are used to estimate the inverse dynamic of this mechanism. An online training algorithm based on the error dynamics is used on the adaptive neural network controller but the neural networks are trained offline with a backpropagation algorithm. The design and architecture of the neural networks are explained along with the identification procedure of the robotic system. Simulations and comparisons with a PD controller are done to test the performance of the neural network controller.
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