{'fraction_non_alphanumeric': 0.015126050420168067, 'fraction_numerical': 0.0067226890756302525, 'mean_word_length': 5.144329896907217, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A neural network for the trajectory control of robotic manipulators with uncertainties

We propose a neural network to compensate for the structured and unstructured uncertainties in the robot model with the computed torque method. The neural network is used not to learn the inverse dynamic model but to compensate for the uncertainties of robotic manipulators. When training the neural network, we use the teaching signals present in the proposed control scheme, whose control structure is simpler than that proposed by Ishiguro et al. (1992), whose teaching signals come from the robot model.