{'fraction_non_alphanumeric': 0.01572617946345976, 'fraction_numerical': 0.0, 'mean_word_length': 5.597560975609756, '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}}
Model predictive control for autonomous underwater vehicle

Research on the autonomous underwater vehicles (AUVs) has been gaining more interest in the recent past. AUVs have been envisioned as a cost effective and safe solution for various underwater missions including but are not limited to underwater scientific test-bed, deep oceanic surveillance, environmental monitoring and underwater structures inspection. The control for such autonomous vehicles, however, continues to pose a serious challenge due to its complex dynamics and uncertain environment. As an alternative of direct tuning PID controllers which has several limitations, Model Predictive Control is proposed in this work. Characteristic of AUV where the dynamics is sluggish and linear around the region of operation makes the application of MPC amenable. In addition, the ability of the method to handle constraints inherent in the control inputs of AUV is appropriately exploited. Overall effectiveness of the new approach is evaluated based on the tracking performance, disturbance rejection and robustness.