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Neural Network model for a biped robot

In bipedal walking, stable balance and walking sequence are essential. In this work, a neural network to model the balance dynamic of a biped robot is proposed. The back propagation neural network capacities to generalize are used to agilely characterize the performance of a fuzzy PD incremental algorithm based on the ZMP criteria to balance a real biped robot structure. The effectiveness of the implemented neural model used as a system identificator is demonstrated by the comparison between its output (the predicted robot's ZMP), and the real robot's ZMP value.