Abstract
AbstractThis paper proposes a concurrent learning algorithm for forward and inverse modeling. The algorithm is consisted of two phases. In the first phase, a feedback controller is used. The forward model is trained using the output values of the controller as the input values to the system and the inverse model is trained by the feedback error learning. In the second phase, the forward model and the inverse model are trained at the same time. By the simulation experiments in a two‐link manipulator, it is confirmed that our algorithm can converge faster than the ones already proposed.
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