Please wait a minute...
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (9): 1827-1838    DOI: 10.3785/j.issn.1008-973X.2020.09.020
    
Trajectory planning for carrier aircraft on deck using Newton Symplectic pseudo-spectral method
Jie LIU1(),Xian-zhou DONG1,Wei HAN2,Xin-wei WANG4,*(),Chun LIU3,Jun JIA1
1. War Research Institute, Academy of Military Sciences, Beijing 100850, China
2. Naval Aviation University, Yantai 264001, China
3. 650 Aircraft Design Institute of AVIC Hongdu, Nanchang 330024, China
4. Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, China
Download: HTML     PDF(1732KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

The kinematic models for three dispatch modes of carrier aircraft were established, including individually taxiing, off-axle hitching towing without drawbar, and off-axle hitching towing with drawbar. As the high nonlinearity in the kinematics, a towing system with drawbar was transformed into a simpler virtual on-axle hitching towing system so as to facilitate the trajectory planning. Considering the dispatch efficiency and security, the trajectory planning problems of three dispatch modes were formulated as time-energy hybrid optimal control problems. To solve the nonlinear optimal control problem efficiently, a Symplectic pseudo-spectral method (SPM) was firstly developed based on the third kind of generating function, Symplectic theory and pseudo-spectral discretization. Then the Newton iteration and the SPM were used to determine the optimal terminal time according to the terminal transversality condition. The developed method was applied to solve trajectory planning problems of three dispatch modes, and the direct pseudo-spectral method was implemented for comparison. The simulation results suggest that the developed method can generate smooth dispatch trajectories with higher accuracy and efficiency, where no infeasible solution occurs, leading to better operability and applicability.



Key wordscarrier aircraft      trajectory planning      Symplectic pseudo-spectral method (SPM)      Newton iteration method      optimal control     
Received: 17 October 2019      Published: 22 September 2020
CLC:  TP 13  
Corresponding Authors: Xin-wei WANG     E-mail: liuyexiaobao@163.com;wangxinwei@dlut.edu.cn
Cite this article:

Jie LIU,Xian-zhou DONG,Wei HAN,Xin-wei WANG,Chun LIU,Jun JIA. Trajectory planning for carrier aircraft on deck using Newton Symplectic pseudo-spectral method. Journal of ZheJiang University (Engineering Science), 2020, 54(9): 1827-1838.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.09.020     OR     http://www.zjujournals.com/eng/Y2020/V54/I9/1827


采用牛顿迭代保辛伪谱算法的舰载机甲板路径规划

建立单机滑行、离轴无杆牵引、离轴有杆牵引3类舰载机调运模式下的运动学模型. 考虑到有杆牵引系统运动学模型的强非线性,将其转化为一个更加简单的虚拟在轴无杆牵引系统,以便于轨迹的求解. 综合考虑调运效率和安全性,将3类调运模式的轨迹规划问题转化为时间-能量混合最优问题. 为了实现对非线性最优控制问题的高效求解,基于第三类生成函数、辛理论和伪谱离散提出保辛伪谱方法(SPM),并根据终端横截条件采用牛顿迭代和SPM确定终端时间. 将提出的方法应用于3类调运模式的轨迹规划问题,并将所得结果与直接伪谱法进行对比. 仿真结果表明:所提算法能够以更高的精度和效率规划出平滑的舰载机路径,且不会出现非可行解,具有更强的可操作性和适用性.


关键词: 舰载机,  路径规划,  保辛伪谱算法(SPM),  牛顿迭代法,  最优控制 
Fig.1 Virtual on-axle hitching tractor-aircraft system
Fig.2 Taxiing trajectory of aircraft
Fig.3 Change of control variables of sliding system with time
Fig.4 Changs in speed and steering angle of taxiing aircraft over time
滑行系统 方法 Mayer Lagrange $J$ ${t_{\rm f}}$/s tc/s
舰载机3 NSP 4.76 16.33 21.10 95.27 16.27
伪谱 5.23 15.68 20.91 104.59 91.87
舰载机5 NSP 7.27 1.92 20.91 145.49 7.73
伪谱 7.64 3.81 11.45 152.76 54.40
舰载机11 NSP 10.03 0.63 10.66 200.66 12.72
伪谱 10.11 0.51 10.63 202.28 76.29
Tab.1 Comparison results of NSP and pseudospectral method in trajectory planning for taxiing system
Fig.5 Trajectory of carrier aircraft and tractor of towed carrier aircraft system without drawbar
Fig.6 Control of towed carrier aircraft system without drawbar
Fig.7 Velocity and steering angle of aircraft of towed carrier aircraft system without drawbar
无杆牵引系统 方法 Mayer Lagrange $J$ ${t_{\rm f}}$/s tc/s
位置7到10 NSP 8.33 5.30 13.63 83.29 5.33
伪谱 9.53 3.81 13.34 95.27 45.27
位置7到12 NSP 8.47 4.24 12.71 84.69 4.78
伪谱 10.83 65.54 76.37 108.26 68.63
位置4到11 NSP 6.55 5.51 12.06 65.55 2.32
伪谱 6.32 7.80 14.12 63.25 21.03
Tab.2 Comparison results of NSP and pseudospectral method in trajectory planning for towed carrier aircraft system without drawbar
Fig.8 Trajectory of carrier aircraft and tractor of towed carrier aircraft system with drawbar by NPS algorithm
Fig.9 Velocity and steering angle of aircraft of towed carrier aircraft system with drawbar
[1]   TONG H, CHAO W W, DONG X Z, et al Path planning of UAV based on Voronoi diagram and DPSO[J]. Procedia Engineering, 2012, 29: 4198- 4203
doi: 10.1016/j.proeng.2012.01.643
[2]   CANDELORO M, LEKKAS A M, HEGDE J, et al. A 3D dynamic Voronoi diagram-based path-planning system for UUVs [C] // OCEANS 2016 MTS/IEEE Monterey. Monterey: IEEE. 2016: 1-8.
[3]   张智, 林圣琳, 朱齐丹, 等 考虑运动学约束的不规则目标遗传避碰规划算法[J]. 航空学报, 2015, 36 (4): 1348- 1358
ZHANG Zhi, LIN Sheng-lin, ZHU Qi-dan, et al Genetic collision avoidance planning algorithm for irregular shaped object with kinematics constraint[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36 (4): 1348- 1358
[4]   司维超, 齐玉东, 韩维 基于融合Dijkstra的凸壳算法的舰载机机库调运规划[J]. 系统工程与电子技术, 2015, 37 (3): 583- 588
SI Wei-chao, QI Yu-dong, HAN Wei Carrier plane transportation in hangar based on convex hull algorithm combined with Dijkstra[J]. Systems Engineering and Electronics, 2015, 37 (3): 583- 588
[5]   韩维, 司维超, 丁大春, 等 基于聚类PSO算法的舰载机舰面多路径动态规划[J]. 北京航空航天大学学报, 2013, 39 (5): 610- 614
HAN Wei, SI Wei-chao, DING Da-chun Multi-routes dynamic planning on deck of carrier plane based on clustering PSO[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39 (5): 610- 614
[6]   WU Y, QU X J Path planning for taxi of carrier aircraft launching[J]. Science Chine: Technology Sciences, 2013, 56 (6): 1561- 1570
doi: 10.1007/s11431-013-5222-5
[7]   WU Y, QU X J Obstacle avoidance and path planning for carrier aircraft launching[J]. Chinese Journal of Aeronautics, 2015, 28 (3): 695- 703
doi: 10.1016/j.cja.2015.03.001
[8]   WU Y, HU N, QU X J A general trajectory optimization method for aircraft taxiing on flight deck of carrier[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2019, 233 (4): 1340- 1353
doi: 10.1177/0954410017752224
[9]   FILIPPIS L D, GUGLIERI G, QUAGLIOTTI F A minimum risk approach for path planning of UAVs[J]. Journal of Intelligent and Robotic Systems, 2011, 61 (1-4): 203- 219
[10]   ZHANG K, LIU P P, KONG W R, et al An improved heuristic algorithm for UCAV path planning[J]. Journal of Optimization, 2017, (4): 1- 7
doi: 10.1155/2017/8936164
[11]   NIU H L, LU Y, SAVVARIS A, et al. Efficient path following algorithm for unmanned surface vehicle [C] // OCEANS 2016 - Shanghai. Shanghai: OCEANS, 2016:OCEANS 1-7.
[12]   ZHANG Y D, WU L N, WANG S H UCAV path planning by fitness-scaling adaptive chaotic particle swarm optimization[J]. Mathematical Problems in Engineering, 2013, 2013 (8): 147- 170
[13]   DAS P K, BEHERA H S, JENA P K, et al Multi-robot path planning in a dynamic environment using improved gravitational search algorithm[J]. Journal of Electrical Systems and Information Technology, 2016, 3 (2): 295- 313
doi: 10.1016/j.jesit.2015.12.003
[14]   GROH K, R?CK S A contribution to collision-free trajectory planning for handling systems in varying environments[J]. Production Engineering, 2010, 4 (1): 101- 106
doi: 10.1007/s11740-009-0202-0
[15]   HUPTYCH M, GROH K, R?CK S. Online path planning for industrial robots in varying environments using the curve shortening flow method [M] // Intelligent Robotics and Applications. Berlin Heidelberg: Springer, 2011: 73-82.
[16]   HUPTYCH M, R?CK S Online path planning in dynamic environments using the curve shortening flow method[J]. Production Engineering, 2015, 9 (5/6): 613- 621
[17]   MANCINI M, COSTANTE G, VALIGI P, et al Towards domain independence for learning-based monocular depth estimation[J]. IEEE Robotics and Automation Letters, 2017, 2 (3): 1778- 1785
doi: 10.1109/LRA.2017.2657002
[18]   MORALES N, TOLEDO J, ACOSTA L Path planning using a multiclass support vector machine[J]. Applied Soft Computing, 2016, 43: 498- 509
doi: 10.1016/j.asoc.2016.02.037
[19]   BLACKMORE L, A?IKME?E B, III J M C. Lossless convexification of control constraints for a class of nonlinear optimal control problems [C] // American Control Conference. Montreal: [s. n. ], 2012: 5519-5525.
[20]   MAO Y Q, SZMUK M, A?IKME?E B. Successive convexification of non-convex optimal control problems and its convergence properties [C] // IEEE 55th Conference on Decision and Control (CDC). Las Vegas: IEEE, 2016: 3636-3641.
[21]   A?IKME?E B, CARSON J M, BLACKMORE L Lossless convexification of nonconvex control bound and pointing constraints of the soft landing optimal control problem[J]. IEEE Transactions on Control Systems Technology, 2013, 21 (6): 2104- 2113
doi: 10.1109/TCST.2012.2237346
[22]   LIU X F, LU P, PAN B F Survey of convex optimization for aerospace applications[J]. Astrodynamics, 2017, 1 (1): 23- 40
doi: 10.1007/s42064-017-0003-8
[23]   LIU X F, LU P Solving nonconvex optimal control problems by convex optimization[J]. Journal of Guidance Control and Dynamics, 2014, 37 (3): 750- 765
doi: 10.2514/1.62110
[24]   GONG Q, KANG W, ROSS I M A pseudospectral method for the optimal control of constrained feedback linearizable systems[J]. IEEE Transactions on Automatic Control, 2006, 51 (7): 1115- 1129
doi: 10.1109/TAC.2006.878570
[25]   LEWIS L R, ROSS I M, GONG Q. Pseudospectral motion planning techniques for autonomous obstacle avoidance [C] // IEEE Conference on Decision and Control. New Orleans: IEEE, 2007: 5997-6002.
[26]   LI Y Y, ZHU Y F, LI Q Analysis of aircraft path planning optimal on carrier flight deck[J]. Advanced Materials Research, 2013, 664: 1122- 1127
doi: 10.4028/www.scientific.net/AMR.664.1122
[27]   PENG H J, GAO Q, WU Z G, et al Symplectic adaptive algorithm for solving nonlinear two-point boundary value problems in astrodynamics[J]. Celestial Mechanics and Dynamical Astronomy, 2011, 110 (4): 319- 342
doi: 10.1007/s10569-011-9360-4
[28]   PENG H J, GAO Q, WU Z G, et al Symplectic approaches for solving two-point boundary-value problems[J]. Journal of Guidance, Control, and Dynamics, 2012, 35 (2): 653- 659
doi: 10.2514/1.55795
[29]   PENG H J, GAO Q, WU Z G, et al Efficient sparse approach for solving receding-horizon control problems[J]. Journal of Guidance, Control, and Dynamics, 2013, 36 (6): 1864- 1872
doi: 10.2514/1.60090
[30]   JOHNSTON J S, SWENSON E D Feasibility study of global-positioning-system-based aircraft-carrier flight-deck persistent monitoring system[J]. Journal of Aircraft, 2010, 47 (5): 1624- 1635
doi: 10.2514/1.C000220
[31]   KARKEE M, STEWARD B L Study of the open and closed loop characteristics of a tractor and a single axle towed implement system[J]. Journal of Terramechanics, 2010, 47 (6): 379- 393
doi: 10.1016/j.jterra.2010.05.005
[32]   WANG X W, PENG H J, ZHANG S, et al A symplectic pseudospectral method for nonlinear optimal control problems with inequality constraints[J]. ISA Transactions, 2017, 335- 352
[33]   PENG H J, WANG X W, LI M W, et al An hp symplectic pseudospectral method for nonlinear optimal control[J]. Communications in Nonlinear Science and Numerical Simulation, 2017, 42: 623- 644
doi: 10.1016/j.cnsns.2016.06.023
[1] Kai-ming HU,Hua LI. Nonlinear stochastic optimal voltage bounded control for axial compressed beam[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(5): 940-946.
[2] QIN Chao, LIANG Xi-feng, LU Jie, PENG Ming, JIN Chao-qi. Trajectory planning and simulation for 7-DoF tomato harvesting manipulator[J]. Journal of ZheJiang University (Engineering Science), 2018, 52(7): 1260-1266.
[3] LIAO Xiang ping, GONG Guo fang, PENG Xiong bin, WU Wei qiang. Jam breakout characteristic of tunnel boring machine based on hydro viscous drive mechanism[J]. Journal of ZheJiang University (Engineering Science), 2016, 50(5): 902-912.
[4] XIAO Yang, GUAN Cheng, WANG Fei. Energy management strategy for torque coupling based hydraulic hybrid excavator[J]. Journal of ZheJiang University (Engineering Science), 2016, 50(1): 70-77.
[5] LIU Xiang qi, MENG Zhen, NI Jing, ZHU Ze fei. Trajectory planning algorithm for hydraulic servo manipulator of three freedom[J]. Journal of ZheJiang University (Engineering Science), 2015, 49(9): 1776-1782.
[6] YE Ling-jian, MA Xiu-shui. Optimal control strategy for chemical processes
based on soft-sensoring technique
[J]. Journal of ZheJiang University (Engineering Science), 2013, 47(7): 1253-1257.
[7] WANG Hui-fang, ZHU Shi-qiang, WU Wen-xiang. INSGA-Ⅱ based multi-objective trajectory planning for manipulators[J]. Journal of ZheJiang University (Engineering Science), 2012, 46(4): 622-628.
[8] CHAI An, DIAO Qian, JIANG Min. Design and development of urban intelligent traffic signal control system[J]. Journal of ZheJiang University (Engineering Science), 2010, 44(7): 1241-1246.
[9] LIU Xin-Gao, CHEN Long. Nested constraintpreferred optimization method for fixed boundary optimal control problem[J]. Journal of ZheJiang University (Engineering Science), 2010, 44(7): 1247-1250.
[10] HU Xian, LUO Yao-Chi, CHEN Yan-Ban. Nonlinear active control of tensegrity structures[J]. Journal of ZheJiang University (Engineering Science), 2010, 44(10): 1979-1984.
[11] ZHENG Hui-Feng, ZHOU Xiao-Jun, ZHANG Yang. Time optimization based trajectory planning of ultrasonic inspection[J]. Journal of ZheJiang University (Engineering Science), 2010, 44(1): 29-33+183.