@inproceedings{9002331a099d4bec9b0d638626c98d35,
title = "Parameter Optimization for a Quadrotor System with External Disturbance and Uncertainty via Reinforcement Learning",
abstract = "Parameter optimization is a crucial area within the field of control theory. This study introduces a novel framework based on reinforcement learning (RL) for controlling quadrotors. Initially, fast nonsingular terminal sliding mode control (FNTSMC) serves as the fundamental trajectory tracking controller for the quadrotor. Subsequently, fixed-time disturbance observers (FTDO) are employed to mitigate disturbances. Ultimately, an RL training framework is introduced to optimize the hyperparameters within the FNTSMCs. Extensive simulation and physical experiments are conducted to validate the efficacy and superiority of the proposed control framework.",
keywords = "Parameter optimization, Quadrotor control, Reinforcement learning, Sliding mode control",
author = "Yefeng Yang and Xiaojun Ban and Hongqian Lu and Tao Huang and Xianlin Huang",
note = "Publisher Copyright: {\textcopyright} 2024 Technical Committee on Control Theory, Chinese Association of Automation.; 43rd Chinese Control Conference, CCC 2024 ; Conference date: 28-07-2024 Through 31-07-2024",
year = "2024",
doi = "10.23919/CCC63176.2024.10661901",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "2480--2485",
editor = "Jing Na and Jian Sun",
booktitle = "Proceedings of the 43rd Chinese Control Conference, CCC 2024",
address = "美国",
}