{'fraction_non_alphanumeric': 0.013888888888888888, 'fraction_numerical': 0.0, 'mean_word_length': 5.506944444444445, '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}}
Learning from stable adaptive NN output feedback control of uncertain ship dynamics

This paper studies the problem of learning from stable adaptive neural network (NN) output feedback control of ocean surface ship in uncertain dynamical environments. When only ship position and heading measurements are available for identification and control, stable adaptive output feedback NN tracking controller is proposed by employing a high-gain observer to estimate the other states of ship dynamics. Partial persistent excitation (PE) condition of some internal signals in the closed-loop system is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the uncertain ship dynamics and of storing the learned knowledge in memory. Simulation studies are performed to demonstrate the effectiveness of the proposed method.