Trajectory Tracking Control for Under-Actuated Hovercraft Using Differential Flatness and Reinforcement Learning-Based Active Disturbance Rejection Control
查看参考文献36篇
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文摘
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This paper proposes a scheme of trajectory tracking control for the hovercraft. Since the model of the hovercraft is under-actuated, nonlinear, and strongly coupled, it is a great challenge for the controller design. To solve this problem, the control scheme is divided into two parts. Firstly, we employ differential flatness method to find a set of flat outputs and consider part of the nonlinear terms as uncertainties. Consequently, we convert the under-actuated system into a full-actuated one. Secondly, a reinforcement learning-based active disturbance rejection controller (RL-ADRC) is designed. In this method, an extended state observer (ESO) is designed to estimate the uncertainties of the system, and an actorcritic-based reinforcement learning (RL) algorithm is used to approximate the optimal control strategy. Based on the output of the ESO, the RL-ADRC compensates for the total uncertainties in real-time, and simultaneously, generates the optimal control strategy by RL algorithm. Simulation results show that, compared with the traditional ADRC method, RL-ADRC does not need to manually tune the controller parameters, and the control strategy is more robust. |
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来源
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Journal of Systems Science and Complexity
,2022,35(2):502-521 【核心库】
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DOI
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10.1007/s11424-022-2037-0
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关键词
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Active disturbance rejection control
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differential flatness
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reinforcement learning
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trajectory tracking control
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under-actuated system
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地址
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School of Automation, Beijing Institute of Technology, Beijing, 100190
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语种
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英文 |
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文献类型
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研究性论文 |
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ISSN
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1009-6124 |
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学科
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自动化技术、计算机技术;水路运输 |
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基金
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国家自然科学基金
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文献收藏号
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CSCD:7238857
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参考文献 共
36
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