{'fraction_non_alphanumeric': 0.012195121951219513, 'fraction_numerical': 0.001524390243902439, 'mean_word_length': 5.636363636363637, '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}}
Sliding mode neural controller for nonlinear systems with higher-order and uncertainties

In this paper, we propose a new neural controller architecture which is derived from an adaptive sliding mode control framework for a SISO nonlinear system with higher-order and uncertainties. This neural controller can overcome some disadvantages inherent in sliding mode controllers such as the chattering problem, complex calculation of the equivalent control term and unavailable knowledge of the upper bounds of system uncertainties. Experimental results for a coupled drives CE8 system show that a real-time neural controller has been implemented successfully.