{'fraction_non_alphanumeric': 0.029213483146067417, 'fraction_numerical': 0.0, 'mean_word_length': 5.5036496350364965, '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}}
Frequency-Domain System Identification of an Unmanned Helicopter Based on an Adaptive Genetic Algorithm

This paper presents a frequency-domain identification method for an unmanned helicopter (UH) based on an adaptive genetic algorithm (AGA). By using a homemade micro-guidance, navigation, and control system (MGNCS), data regarding the inputs (control signals of servos) and outputs (states of the UH) are recorded. After data preprocessing, the attitude model of the UH is identified by employing the AGA. The identified model is then analyzed in the time domain and the frequency domain in comparison with the least squares (LS) method. Control compensators are designed based on the identified model. Automatic hovering is successfully achieved based on the compensators. Simulation and experimental results demonstrate the effectiveness and superiority of this identification method.