{'fraction_non_alphanumeric': 0.025925925925925925, 'fraction_numerical': 0.0, 'mean_word_length': 5.402843601895735, '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}}
Game Theoretical Reinforcement Learning for Robust H∞ Tracking Control of Discrete-Time Linear Systems with Unknown Dynamics

Robust H∞ control has been widely studied to improve control performance for industrial process control systems against disturbances. However, most of existing robust H∞ control are model-based, and their deployment in some industrial facilities may greatly increase the installation and maintenance costs due to requiring system identification. Towards this end, a model-free robust H∞ tracking control scheme is developed based on game theoretical reinforcement learning (RL) for discrete-time linear systems with unknown dynamics. The normal robust H∞ tracking control problem is first modeled as a two-player zero-sum game with the controller and disturbance as the two players. A model-based solution by solving game discrete-time differential Riccati equation (GDARE) is introduced to show the solvability of the robust H∞ tracking control problem, and then a novel off-policy RL algorithm is developed to replace the GDARE method for model-free robust H∞ tracking control of the discrete-time linear systems with unknown dynamics. Stability of the learning algorithm is analyzed. Finally, a simulation study upon a de-oiling hydrocyclone system is conducted to demonstrate the effectiveness of the proposed algorithm.