1. <button id="qm3rj"><thead id="qm3rj"></thead></button>
      <samp id="qm3rj"></samp>
      <source id="qm3rj"><menu id="qm3rj"><pre id="qm3rj"></pre></menu></source>

      <video id="qm3rj"><code id="qm3rj"></code></video>

        1. <tt id="qm3rj"><track id="qm3rj"></track></tt>
            1. 2.765

              2022影響因子

              (CJCR)

              • 中文核心
              • EI
              • 中國科技核心
              • Scopus
              • CSCD
              • 英國科學文摘

              留言板

              尊敬的讀者、作者、審稿人, 關于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復。謝謝您的支持!

              姓名
              郵箱
              手機號碼
              標題
              留言內容
              驗證碼

              可回收火箭大氣層內動力下降的多階段魯棒優化制導方法

              馮子鑫 薛文超 張冉 齊洪勝

              馮子鑫, 薛文超, 張冉, 齊洪勝. 可回收火箭大氣層內動力下降的多階段魯棒優化制導方法. 自動化學報, 2024, 50(3): 505?517 doi: 10.16383/j.aas.c230552
              引用本文: 馮子鑫, 薛文超, 張冉, 齊洪勝. 可回收火箭大氣層內動力下降的多階段魯棒優化制導方法. 自動化學報, 2024, 50(3): 505?517 doi: 10.16383/j.aas.c230552
              Feng Zi-Xin, Xue Wen-Chao, Zhang Ran, Qi Hong-Sheng. A multi-stage robust optimization guidance method for endoatmospheric powered descent of reusable rockets. Acta Automatica Sinica, 2024, 50(3): 505?517 doi: 10.16383/j.aas.c230552
              Citation: Feng Zi-Xin, Xue Wen-Chao, Zhang Ran, Qi Hong-Sheng. A multi-stage robust optimization guidance method for endoatmospheric powered descent of reusable rockets. Acta Automatica Sinica, 2024, 50(3): 505?517 doi: 10.16383/j.aas.c230552

              可回收火箭大氣層內動力下降的多階段魯棒優化制導方法

              doi: 10.16383/j.aas.c230552
              基金項目: 國家重點研發計劃(2018YFA0703800), 國家自然科學基金(62122083, 62103014), 中國科學院青年創新促進會(E129030401)資助
              詳細信息
                作者簡介:

                馮子鑫:中國科學院大學數學科學學院博士研究生. 2018年獲得鄭州大學學士學位, 2022年獲得中國科學院大學碩士學位. 主要研究方向為軌跡規劃, 最優控制和微分博弈. E-mail: fengzixin19@amss.ac.cn

                薛文超:中國科學院數學與系統科學研究院系統控制重點實驗室副研究員. 2007年獲得南開大學學士學位, 2012年獲得中國科學院大學博士學位. 主要研究方向為非線性不確定系統控制, 非線性不確定系統濾波和分布式濾波. 本文通信作者. E-mail: wenchaoxue@amss.ac.cn

                張冉:北京航空航天大學宇航學院副教授. 2013年獲得北京航空航天大學博士學位. 主要研究方向為高速飛行器的決策、軌跡規劃與制導理論. E-mail: zhangran@buaa.edu.cn

                齊洪勝:中國科學院數學與系統科學研究院研究員. 2008年獲得中國科學院大學博士學位. 主要研究方向為邏輯動態系統, 博弈與控制, 量子網絡和分布式優化. E-mail: qihongsh@amss.ac.cn

              A Multi-stage Robust Optimization Guidance Method for Endoatmospheric Powered Descent of Reusable Rockets

              Funds: Supported by National Key Research and Development Program of China (2018YFA0703800), National Natural Science Foundation of China (62122083, 62103014), and Youth Innovation Promotion Association of Chinese Academy of Sciences (E129030401)
              More Information
                Author Bio:

                FENG Zi-Xin Ph.D. candidate at the School of Mathematical Sciences, University of Chinese Academy of Sciences. He received his bachelor degree from Zhengzhou University in 2018 and received his master degree from University of Chinese Academy of Sciences in 2022. His research interest covers trajectory planning, optimal control, and differential games

                XUE Wen-Chao Associate researcher at the Key Laboratory of Systems and Control, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences. He received his bachelor degree from Nankai University in 2007 and received his Ph.D. degree from University of Chinese Academy of Sciences in 2012. His research interest covers nonlinear uncertain systems control, nonlinear uncertain systems filter, and distributed filter. Corresponding author of this paper

                ZHANG Ran Associate professor at the School of Astronautics, Beihang University. He received his Ph.D. degree from Beihang University in 2013. His research interest covers decision-making, trajectory design and guidance theory for high-speed flight vehicles

                QI Hong-Sheng Researcher at the Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences. He received his Ph.D. degree from University of Chinese Academy of Sciences in 2008. His research interest covers logical dynamic systems, games and control, quantum networks, and distributed optimization

              • 摘要: 針對大氣層內可回收火箭的動力下降問題, 提出一種多階段的魯棒優化(Robust optimization, RO)方法. 由于大氣層內存在未知風場, 如何在火箭下降段考慮這種不確定性具有十分重要的意義. 首先, 建立一個關于高度的不確定風場模型, 在該風場下給出火箭動力下降的魯棒最優控制問題. 為了求解該問題, 使用一種對不等式約束采取一階近似并將一階項作為安全裕量加入約束的魯棒優化方法, 得到一個可以求解的單階段魯棒優化算法. 其次, 定量給出安全裕量的上界, 基于該上界提出一種多階段魯棒優化算法, 避免單階段魯棒優化算法中安全裕量可能過大導致無法求解的問題. 最后, 通過仿真對比各個算法在多個實際風場下的性能, 結果表明所提出的多階段魯棒優化方法同時具有較高的落點精度和對于不同風場的魯棒性.
              • 圖  1  多階段RO算法流程圖

                Fig.  1  Flow chart of multi-stage RO algorithm

                圖  2  $ y$軸的六個實際風場和一個標稱風場

                Fig.  2  Six actual wind fields and one nominal wind field on the y-axis

                圖  3  實際風場參數與標稱風場參數的距離

                Fig.  3  Distances between parameters of actual wind fields and parameters of nominal wind field

                圖  4  表1的仿真參數條件下隨機生成的一組風場

                Fig.  4  A set of wind fields randomly generated under the simulation parameter conditions of Table 1

                圖  5  單階段RO算法和多階段RO算法在不同風場下的最終位置誤差

                Fig.  5  The terminal position errors of single-stage RO algorithm and multi-stage RO algorithm under different wind fields

                圖  6  單階段RO算法和多階段RO算法在不同風場下的最終速度誤差

                Fig.  6  The terminal velocity errors of single-stage RO algorithm and multi-stage RO algorithm under different wind fields

                圖  7  單階段RO算法和多階段RO算法在各個實際風場下的軌跡

                Fig.  7  The trajectories of single-stage RO algorithm and multi-stage RO algorithm under each actual wind field

                圖  8  單階段RO算法的控制–時間圖

                Fig.  8  Control-time diagram of single-stage RO algorithm

                圖  9  多階段RO算法的控制–時間圖

                Fig.  9  Control-time diagram of multi-stage RO algorithm

                圖  10  單階段RO算法在不同風場下的最終位置與約束范圍

                Fig.  10  The terminal position and its constraint range of single-stage RO algorithm under different wind fields

                圖  11  單階段RO算法在不同風場下的最終速度與約束范圍

                Fig.  11  The terminal velocity and its constraint range of single-stage RO algorithm under different wind fields

                圖  12  多階段RO算法在不同風場下的最終位置與約束范圍

                Fig.  12  The terminal position and its constraint range of multi-stage RO algorithm under different wind fields

                圖  13  多階段RO算法在不同風場下的最終速度與約束范圍

                Fig.  13  The terminal velocity and its constraint range of multi-stage RO algorithm under different wind fields

                圖  14  單階段RO算法與NO算法在不同風場下的最終位置誤差

                Fig.  14  The terminal position errors of single-stage RO algorithm and NO algorithm under different wind fields

                表  1  仿真參數值

                Table  1  Simulation parameter values

                參數取值
                初始狀態${\boldsymbol{r}}_0=\left[2~968~{\rm{m}},263~{\rm{m}},4~326~{\rm{m}}\right]^{\rm{T}} $, ${\boldsymbol{v}}_0 = \left[-295~{\rm{m/s}},27~{\rm{m/s}},-296~{\rm{m/s}}\right]^{\rm{T}}$, $m_0=48~185$ kg
                期望落點狀態${\boldsymbol{r}}_f =\left[0~{\rm{m}},0~{\rm{m}},0~{\rm{m}}\right]^{\rm{T}}$, $ {\boldsymbol{v}}_f =\left[0~{\rm{m/s}},0~{\rm{m/s}},0~{\rm{m/s}}\right]^{\rm{T}}$
                火箭燃料消耗常數$\kappa=2~975$
                火箭參考面積$S_{{{\rm{ref}}}}=8.814\ {\rm{m}}^2 $
                空氣動力學阻力系數$C_D=4.5$
                標稱風場參數$\hat{{\boldsymbol{s}}}=\left[-0.073,4.460,5.230\right]^{\rm{T}},\ \mu=2~234,\ \sigma=1~635$, $ k=2.16\times{10}^{-3}, b=0.35$
                ${\boldsymbol{S}}_\tau $中的參數$\tau=1,\ {\boldsymbol{D}}=\text{ diag}\left\{0.83,\ 1.20,\ 2.66\right\}$
                終端約束范圍單階段RO算法${\boldsymbol{L}}=\left[L_{rx},L_{ry},L_{rz},L_{vx},L_{vy},L_{vz}\right]=\left[7,35,7,35,35,35\right]$
                多階段RO算法${\boldsymbol{L}}=\left[4,20,4,4,4,4 \right]$
                多階段RO算法的階段數2
                下載: 導出CSV

                表  2  單階段RO算法和多階段RO算法的運行時間

                Table  2  Running time of single-stage RO algorithm and multi-stage RO algorithm

                算法運行時間(s)
                單階段RO算法6.61
                多階段RO算法13.14
                下載: 導出CSV
                1. <button id="qm3rj"><thead id="qm3rj"></thead></button>
                  <samp id="qm3rj"></samp>
                  <source id="qm3rj"><menu id="qm3rj"><pre id="qm3rj"></pre></menu></source>

                  <video id="qm3rj"><code id="qm3rj"></code></video>

                    1. <tt id="qm3rj"><track id="qm3rj"></track></tt>
                        亚洲第一网址_国产国产人精品视频69_久久久久精品视频_国产精品第九页
                      1. [1] McCurdy D, Roche J. Structural sizing of a horizontal take-off launch vehicle with an air collection and enrichment system. In: Proceedings of the 40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit. Fort Lauderdale, USA: AIAA, 2004. Article No. 3394
                        [2] Reynolds T, Malyuta D, Mesbahi M, Acikmese B, Carson J M. Funnel synthesis for the 6-DOF powered descent guidance problem. In: Proceedings of the AIAA Scitech 2021 Forum. Virtual Event: AIAA, 2021. Article No. 0504
                        [3] Scharf D P, Acikmese B, Dueri D, Benito J, Casoliva J. Implementation and experimental demonstration of onboard powered-descent guidance. Journal of Guidance, Control, and Dynamics, 2017, 40(2): 213-229 doi: 10.2514/1.G000399
                        [4] Klumpp A R. Apollo lunar descent guidance. Automatica, 1974, 10(2): 133-146 doi: 10.1016/0005-1098(74)90019-3
                        [5] McInnes C R. Direct adaptive control for gravity-turn descent. Journal of Guidance, Control, and Dynamics, 1999, 22(2): 373-375 doi: 10.2514/2.4392
                        [6] Zhou L Y, Xia Y Q. Improved ZEM/ZEV feedback guidance for Mars powered descent phase. Advances in Space Research, 2014, 54(11): 2446-2455 doi: 10.1016/j.asr.2014.08.011
                        [7] Liu Y H, Wang H L, Fan J X, Wu J F, Wu T C. Control-oriented UAV highly feasible trajectory planning: A deep learning method. Aerospace Science and Technology, 2021, 110: Article No.106435 doi: 10.1016/j.ast.2020.106435
                        [8] You S X, Wan C H, Dai R, Rea J R. Learning-based onboard guidance for fuel-optimal powered descent. Journal of Guidance, Control, and Dynamics, 2021, 44(3): 601-613 doi: 10.2514/1.G004928
                        [9] 周宏宇, 王小剛, 單永志, 趙亞麗, 崔乃剛. 基于改進粒子群算法的飛行器協同軌跡規劃. 自動化學報, 2022, 48(11): 2670-2676

                        Zhou Hong-Yu, Wang Xiao-Gang, Shan Yong-Zhi, Zhao Ya-li, Cui Nai-Gang. Synergistic path planning for multiple vehicles based on an improved particle swarm optimization method. Acta Automatica Sinica, 2022, 48(11): 2670-2676
                        [10] Wei H L, Shi Y. MPC-based motion planning and control enables smarter and safer autonomous marine vehicles: Perspectives and a tutorial survey. IEEE/CAA Journal of Automatica Sinica, 2023, 10(1): 8-24 doi: 10.1109/JAS.2022.106016
                        [11] Lawden D F. Optimal Trajectories for Space Navigation. London: Butter Worths, 1963. 42?51
                        [12] Lu P. Propellant-optimal powered descent guidance. Journal of Guidance, Control, and Dynamics, 2018, 41(4): 813-826 doi: 10.2514/1.G003243
                        [13] Acikmese B, Ploen S R. Convex programming approach to powered descent guidance for Mars landing. Journal of Guidance, Control, and Dynamics, 2007, 30(5): 1353-1366 doi: 10.2514/1.27553
                        [14] Acikmese B, Blackmore L. Lossless convexification of a class of optimal control problems with non-convex control constraints. Automatica, 2011, 47(2): 341-347 doi: 10.1016/j.automatica.2010.10.037
                        [15] Harris M W, Acikmese B. Lossless convexification of non-convex optimal control problems for state constrained linear systems. Automatica, 2014, 50(9): 2304-2311 doi: 10.1016/j.automatica.2014.06.008
                        [16] Liu X F, Lu P. Solving nonconvex optimal control problems by convex optimization. Journal of Guidance, Control, and Dynamics, 2014, 37(3): 750-765 doi: 10.2514/1.62110
                        [17] Liu X, Li S, Xin M. Mars entry trajectory planning with range discretization and successive convexification. Journal of Guidance, Control, and Dynamics, 2022, 45(4): 755-763 doi: 10.2514/1.G006237
                        [18] Szmuk M, Reynolds T P, Acikmese B. Successive convexification for real-time six-degree-of-freedom powered descent guidance with state-triggered constraints. Journal of Guidance, Control, and Dynamics, 2020, 43(8): 1399-1413 doi: 10.2514/1.G004549
                        [19] 王祝, 徐廣通, 龍騰. 基于定制內點法的多無人機協同軌跡規劃. 自動化學報, 2023, 49(11): 2374-2385

                        Wang Zhu, Xu Guang-Tong, Long Teng. Customized interior-point method for cooperative trajectory planning of unmanned aerial vehicles. Acta Automatica Sinica, 2023, 49(11): 2374-2385
                        [20] Boscariol P, Richiedei D. Robust point-to-point trajectory planning for nonlinear underactuated systems: Theory and experimental assessment. Robotics and Computer-Integrated Manufacturing, 2018, 50: 256-265 doi: 10.1016/j.rcim.2017.10.001
                        [21] Chen X L, Zhang R, Xue W C, Li H F. Robust neighboring optimal guidance for endoatmospheric powered descent under uncertain wind fields. In: Proceedings of the 34th Chinese Control and Decision Conference (CCDC). Hefei, China: IEEE, 2022. 1491?1496
                        [22] Brault P, Delamare Q, Giordano P R. Robust trajectory planning with parametric uncertainties. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Xi'an, China: IEEE, 2021. 11095?11101
                        [23] Zhang Y. General robust-optimization formulation for nonlinear programming. Journal of Optimization Theory and Applications, 2007, 132: 111-124 doi: 10.1007/s10957-006-9082-z
                        [24] Prabhakar A, Fisher J, Bhattacharya R. Polynomial chaos-Based analysis of probabilistic uncertainty in hypersonic flight dynamics. Journal of Guidance Control and Dynamics, 2010, 33(1): 222-234 doi: 10.2514/1.41551
                        [25] Wang F G, Yang S X, Xiong F F, Lin Q Z, Song J M. Robust trajectory optimization using polynomial chaos and convex optimization. Aerospace Science and Technology, 2019, 92: 314-325 doi: 10.1016/j.ast.2019.06.011
                        [26] Jiang X Q, Li S, Furfaro R, Wang Z B, Ji Y D. High-dimensional uncertainty quantification for Mars atmospheric entry using adaptive generalized polynomial chaos. Aerospace Science and Technology, 2020, 107: Article No.106240 doi: 10.1016/j.ast.2020.106240
                        [27] Zhang W, Wang Q, Zeng F Z, Yan C. An adaptive sequential enhanced PCE approach and its application in aerodynamic uncertainty quantification. Aerospace Science and Technology, 2021, 117: Article No.106911 doi: 10.1016/j.ast.2021.106911
                      2. 加載中
                      3. 圖(14) / 表(2)
                        計量
                        • 文章訪問數:  242
                        • HTML全文瀏覽量:  128
                        • PDF下載量:  96
                        • 被引次數: 0
                        出版歷程
                        • 收稿日期:  2023-09-05
                        • 錄用日期:  2023-11-09
                        • 網絡出版日期:  2024-02-23
                        • 刊出日期:  2024-03-29

                        目錄

                          /

                          返回文章
                          返回