{'fraction_non_alphanumeric': 0.024305555555555556, 'fraction_numerical': 0.0, 'mean_word_length': 5.407407407407407, '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}}
Higher-order Polynomial Signal Tracking Control of Unknown Systems using Off-policy Integral Reinforcement Learning

This paper aims at using an off-policy integral reinforcement learning (IRL) algorithm to solve the linear quadratic tracking (LQT) control problem of completely unknown continuous-time systems, such that an arbitrary higherorder polynomial signal can be followed via an optimal approach. Firstly, a linear continuous-time system with unknown model matrices is introduced with a target of tracking the reference signal with higher-order polynomials. Secondly, based on the knowledge of the on-policy IRL, an off-policy IRL algorithm is used to solve the derived iterative Bellman equation and update the control policy resulting in the optimal control policy. Finally, a simulation example is given to show the efficiency of the proposed approach.