| 基于非线性模型预测控制的火星大气进入智能制导方法 |
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| 引用本文： | 胥彪,李翔,李爽,张金鹏. 基于非线性模型预测控制的火星大气进入智能制导方法[J]. 系统工程与电子技术, 2021, 43(7): 1943-1953. DOI: 10.12305/j.issn.1001-506X.2021.07.26 |
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| 作者姓名： | 胥彪 李翔 李爽 张金鹏 |
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| 作者单位： | 1. 南京航空航天大学航天学院, 江苏 南京 2100162. 中国空空导弹研究院, 河南 洛阳 4710093. 航空制导武器航空科技重点实验室, 河南 洛阳 471009 |
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| 基金项目： | 国家自然科学基金(61603183);航空科学基金(30160152002);博士后科学基金(2018M630560) |
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| 摘 要： | 针对火星大气进入精确制导问题,提出了基于非线性模型预测控制(nonlinear model predictive con-trol,NMPC)的智能进入制导方法.首先,考虑了进入制导约束,采用NMPC方法设计制导算法.通过引入衰减记忆滤波器,提出了基于误差信息估计的预测模型修正方法,增强系统对模型误差的鲁棒性,并利用变...
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| 关 键 词： | 火星进入制导 非线性模型预测控制 衰减记忆滤波器 深度神经网络 智能制导 |
| 收稿时间： | 2020-09-02 |
Intelligent guidance method based on nonlinear model predictive control for Mars atmospheric entry |
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| Biao XU,Xiang LI,Shuang LI,Jinpeng ZHANG. Intelligent guidance method based on nonlinear model predictive control for Mars atmospheric entry[J]. System Engineering and Electronics, 2021, 43(7): 1943-1953. DOI: 10.12305/j.issn.1001-506X.2021.07.26 |
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| Authors: | Biao XU Xiang LI Shuang LI Jinpeng ZHANG |
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| Affiliation: | 1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China2. Luoyang Optoelectro Technology Development Center, Luoyang 471009, China3. Aviation Key Laboratory of Science and Technology on Airborne Guided Weapons, Luoyang 471009, China |
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| Abstract: | Aiming at the precision guidance problem of the Mars atmospheric entry guidance, an intelligent guidance method based on nonlinear model predictive control (NMPC) is proposed. First, considering guidance constraints, the guidance system is designed by using the NMPC method. By introducing the fading-memory filter, the prediction model correction method based on error information estimation is proposed to enhance the robustness of the system against model errors, and the system performance is improved by using the variable prediction time domain strategy. Then the NMPC guidance system is used as the guidance template to generate the sample data set under the actual entry conditions, which conduct the off-line training of the deep neural network. Finally, in the process of entry guidance, deep neural network is used to solve control variable online quickly instead of the process of solving complex optimization problem and integral prediction, and the intelligent guidance is realized by combining with lateral guidance. The simulation results show that the proposed guidance method can calculate command quickly and realize high-precision guidance. |
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| Keywords: | mars entry guidance nonlinear model predictive control fading-memory filter deep neural network intelligent guidance |
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