DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

被引:1
作者
Li, Wan-Li [1 ]
Li, Jiong [2 ]
Ye, Ji-Kun [2 ]
Shao, Lei [2 ]
Zhou, Chi-Jun [2 ]
机构
[1] Air Force Engn Univ, Grad Coll, Xian 710051, Peoples R China
[2] Air Force Engn Univ, Air Def & Missile Def Coll, Xian 710051, Peoples R China
基金
中国国家自然科学基金;
关键词
trajectory planning; convex optimization; lateral distance domain; deep reinforcement learning; approximate solution error; POWERED DESCENT; OPTIMIZATION; ENTRY; DESIGN;
D O I
10.3390/aerospace11040314
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
To address the problem of low accuracy and efficiency in trajectory planning algorithms for interceptors facing multiple constraints during the midcourse guidance phase, an improved trajectory convex programming method based on the lateral distance domain is proposed. This algorithm can achieve fast trajectory planning, reduce the approximation error of the planned trajectory, and improve the accuracy of trajectory guidance. First, the concept of lateral distance domain is proposed, and the motion model of the midcourse guidance segment in the interceptor is converted from the time domain to the lateral distance domain. Second, the motion model and multiple constraints are convexly and discretely transformed, and the discrete trajectory convex model is established in the lateral distance domain. Third, the deep reinforcement learning algorithm is used to learn and train the initial solution of trajectory convex programming, and a high-quality initial solution trajectory is obtained. Finally, a dynamic adjustment method based on the distribution of approximate solution errors is designed to achieve efficient dynamic adjustment of grid points in iterative solving. The simulation experiments show that the improved trajectory convex programming algorithm proposed in this paper not only improves the accuracy and efficiency of the algorithm but also has good optimization performance.
引用
收藏
页数:19
相关论文
共 38 条
  • [1] Drone Deep Reinforcement Learning: A Review
    Azar, Ahmad Taher
    Koubaa, Anis
    Ali Mohamed, Nada
    Ibrahim, Habiba A.
    Ibrahim, Zahra Fathy
    Kazim, Muhammad
    Ammar, Adel
    Benjdira, Bilel
    Khamis, Alaa M.
    Hameed, Ibrahim A.
    Casalino, Gabriella
    [J]. ELECTRONICS, 2021, 10 (09)
  • [2] Convex Optimization-based Entry Guidance for Spaceplane
    Bae, Juho
    Lee, Sang-Don
    Kim, Young-Won
    Lee, Chang-Hun
    Kim, Sung-Yug
    [J]. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2022, 20 (05) : 1652 - 1670
  • [3] Cutting-Edge Trajectory Optimization through Quantum Annealing
    Carbone, Andrea
    De Grossi, Federico
    Spiller, Dario
    [J]. APPLIED SCIENCES-BASEL, 2023, 13 (23):
  • [4] Real-time trajectory optimization for powered planetary landings based on analytical shooting equations
    Cheng, Lin
    Shi, Peng
    Gong, Shengping
    Wang, Zhenbo
    [J]. CHINESE JOURNAL OF AERONAUTICS, 2022, 35 (07) : 91 - 99
  • [5] Efficient ascent trajectory optimization using convex models based on the Newton-Kantorovich/Pseudospectral approach
    Cheng, Xiaoming
    Li, Huifeng
    Zhang, Ran
    [J]. AEROSPACE SCIENCE AND TECHNOLOGY, 2017, 66 : 140 - 151
  • [6] Learning Fuel-Optimal Trajectories for Space Applications via Pontryagin Neural Networks
    D'Ambrosio, Andrea
    Furfaro, Roberto
    [J]. AEROSPACE, 2024, 11 (03)
  • [7] Artificial Bee Colony-Based Direct Collocation for Reentry Trajectory Optimization of Hypersonic Vehicle
    Duan, Haibin
    Li, Shuangtian
    [J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2015, 51 (01) : 615 - 626
  • [8] Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Metalearning
    Gaudet, Brian
    Furfaro, Roberto
    [J]. JOURNAL OF SPACECRAFT AND ROCKETS, 2023, 60 (01) : 286 - 298
  • [9] Rapid Indirect Trajectory Optimization for Conceptual Design of Hypersonic Missions
    Grant, Michael J.
    Braun, Robert D.
    [J]. JOURNAL OF SPACECRAFT AND ROCKETS, 2015, 52 (01) : 177 - 182
  • [10] Optimal deployment of spin-stabilized tethered formations with continuous thrusters
    Guang, Zhai
    Bi Xingzi
    Bin, Liang
    [J]. NONLINEAR DYNAMICS, 2019, 95 (03) : 2143 - 2162