| 自主学习干扰观测器驱动的重复使用运载器再入段滑模控制 |
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| 引用本文： | 陈佳晔,穆荣军,白瑜亮,张新,崔乃刚.自主学习干扰观测器驱动的重复使用运载器再入段滑模控制[J].宇航学报,2019,40(6):694-702. |
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| 作者姓名： | 陈佳晔 穆荣军 白瑜亮 张新 崔乃刚 |
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| 作者单位： | 哈尔滨工业大学航天工程系，哈尔滨 150001 |
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| 摘 要： | 针对重复使用运载器(RLV)再入段的姿态控制问题，设计一种具有自主学习干扰观测器(SLDO)的滑模控制器。基于奇异摄动理论及时标分离原则，将RLV的姿态动力学方程划分为外环和内环子系统。根据RLV再入段模型不确定性和外部干扰均随时间变化、不可忽略且无法预知边界等特点，结合2型模糊神经结构、误差反馈学习架构以及滑模控制(SMC)理论，提出一种新型在线自主学习干扰观测器。设计基于SLDO驱动的多元超螺旋滑模控制器，完成对再入段姿态的跟踪。最后，针对6自由度RLV模型进行了仿真分析，仿真结果证明了控制方法的有效性以及鲁棒性。
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| 关 键 词： | 重复使用运载器(RLV) 自主学习干扰观测器(SLDO) 滑模控制(SMC) 再入段姿态跟踪 |
| 收稿时间： | 2018-07-09 |
Sliding Mode Control in Reentry Phase for a Reusable Launch Vehicle Driven by a Self Learning Disturbance Observer |
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| CHEN Jia ye,MU Rong jun,BAI Yu liang,ZHANG Xin,CUI Nai gang.Sliding Mode Control in Reentry Phase for a Reusable Launch Vehicle Driven by a Self Learning Disturbance Observer[J].Journal of Astronautics,2019,40(6):694-702. |
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| Authors: | CHEN Jia ye MU Rong jun BAI Yu liang ZHANG Xin CUI Nai gang |
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| Affiliation: | Department of Aerospace Engineering, Harbin Institute of Technology, Harbin 150001, China |
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| Abstract: | A sliding mode controller based on self-learning disturbance observer (SLDO) is designed for a reusable launch vehicle (RLV). According to the singular perturbation theory, a RLV dynamic model is divided into the outer-loop and inner-loop subsystems. Since the model uncertainties and the disturbances vary with time and have unknown boundaries, combining with the type-2 neuro-fuzzy structure, feedback-error learning scheme and sliding mode control (SMC) theory, a novel online SLDO is presented. The multivariable supertwisting sliding mode controller driven by a SLDO is designed to track the re-entry trajectory precisely and convergent rapidly. Finally, by the simulation and analysis of the 6-degree-of-freedom model of RLV in the reentry phase, the effectiveness and the robustness of the integrated control scheme are verified. |
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| Keywords: | Reusable launch vehicle (RLV) Self-learning disturbance observer (SLDO) Sliding mode control (SMC) Reentry attitude track |
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