{'fraction_non_alphanumeric': 0.04096638655462185, 'fraction_numerical': 0.0021008403361344537, 'mean_word_length': 4.919254658385094, '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}}
H∞ Control for Discrete-time Linear Systems by Integrating Off-policy Q-learning and Zero-sum Game

This paper focuses on solving H∞ control problem by proposing a novel off-policy Q-learning algorithm under the framework of zero-sum game for discrete-time (DT) linear systems, using only the measured data along the system trajectories. Firstly, H∞ control problem is formulated, followed by the standard on-policy Q-learning algorithm in order to learn the H∞ controller gain. Secondly, behavior control policy and behavior disturbance policy are introduced, and an off-policy Q-function based game Bellman equation (OPQ-GBE) is derived. Consequently, an off-policy Q-learning algorithm is developed for the first time for discrete-time linear systems subject to the external disturbance, and the convergence and no bias of solution of OPQ-BE are proven. Finally, a F-16 aircraft autopilot is given to verify the effectiveness of the proposed method.