Hypersonic Boost Glide Vehicle Trajectory Optimization Using Genetic Algorithm

被引：25

作者：

Kumar, G. Naresh ^{[1
]}

Penchalaiah, D. ^{[2
]}

Sarkar, A. K. ^{[1
]}

Talole, S. E. ^{[3
]}

机构：

[1] DRDL, Directorate Syst, Hyderabad, India

[2] Directorate Syst, Hyderabad, India

[3] DIAT, Dept Aerosp Engn, Pune, Maharashtra, India

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 01期

关键词：

Controllability; Genetic algorithms (GA)s; Hypersonic boost glide vehicle; Trajectory optimization;

D O I：

10.1016/j.ifacol.2018.05.020

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Trajectory optimization formulation for hypersonic boost glide class of vehicle to achieve maximum range under various in-flight and impact constraints is proposed. The formulation considers vehicle controllability using aerodynamic forces also as one of the path constraint by imposing a constraint on maximum altitude. Genetic algorithm is used to solve the dynamic optimization problem and a trajectory for maximum range has been generated with all the in-flight and terminal constraints. The results bring out the effectiveness of genetic algorithm in providing feasible solution to the proposed problem. Superiority of the approach over gradient based method in terms of range and simplicity in formulating complex in-flight constraints is also demonstrated. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：118 / 123

页数：6

共 20 条

[1] Launch strategy for Indian lunar mission and precision injection to the Moon using genetic algorithm [J].

Adimurthy, V ;

Ramanan, RV ;

Tandon, SR ;

Ravikumar, C .

JOURNAL OF EARTH SYSTEM SCIENCE, 2005, 114 (06) :711-716

[2]

[Anonymous], 2005, P AI AACIRA 13 INT S, DOI DOI 10.2514/6.2005-3269

[3] An updated survey of GA-based multiobjective optimization techniques [J].

Coello, CAC .

ACM COMPUTING SURVEYS, 2000, 32 (02) :109-143

[4]

Darwin C., 1959, The Origin of the Species (J. Murray)

[5]

Deb K., 2004, Optimization for engineering design: algorithms and examples

[6]

Gallais P., 2007, ATMOSPHERIC REENTRY, P36

[7]

Goldberg DE., 1989, Genetic algorithms in search, optimization and machine learning

[8] A survey of numerical algorithms for trajectory optimization of flight vehicles [J].

Huang GuoQiang ;

Lu YuPing ;

Nan Ying .

SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2012, 55 (09) :2538-2560

[9] Hybrid Genetic Algorithm Gauss Pseudospectral Method for the Electric Sail Trajectory Optimization [J].

Huo Mingying ;

Qi Naiming ;

Wang Tianye .

PROCEEDINGS OF THE 2012 SECOND INTERNATIONAL CONFERENCE ON INSTRUMENTATION & MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC 2012), 2012, :318-322

[10] Atmospheric vehicle trajectory optimization with minimum dynamic pressure constraint [J].

Kumar, G. Naresh ;

Sarkar, A. K. ;

Mangrulkar, K. K. ;

Talole, S. E. .

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, 2018, 232 (05) :837-846

← 1 2 →