{'fraction_non_alphanumeric': 0.033112582781456956, 'fraction_numerical': 0.006622516556291391, 'mean_word_length': 5.517241379310345, '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}}
Online adaptive control of robot manipulators using dynamic fuzzy neural networks

This paper presents a robust adaptive fuzzy neural controller suitable for motion control of a multi-link robot manipulator. The proposed controller has the following salient features: (1) the dynamic fuzzy neural networks structure, i.e. fuzzy control rules, can be generated or deleted automatically; (2) adaptive learning; (3) online learning of the robot dynamics; (4) fast learning speed; and (5) fast convergence of tracking error. The global stability of the system is established using the Lyapunov approach. Computer simulation studies of a two-link robot manipulator demonstrate that an excellent tracking performance can be achieved under external disturbances.