Entry trajectory optimization for cross-domain morphing vehicles by hybrid sequential second-order cone programming

  • Zheng Li
  • , Zhenyue Jia
  • , Zheng Fang
  • , Hongmiao Zhou
  • , Jianqiao Yu*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a hybrid sequential second-order cone programming (HSSOCP) method with a three-layer scheme for the entry trajectory optimization of the cross-domain morphing vehicles (CDMVs). By defining the new morphing rate control variable and using relaxation techniques to relax the bank angle constraint, the SOCP-based entry problem is constructed. A dynamic relaxation penalization technique is developed in the first layer to overcome artificial infeasibility and significantly enhance initialization robustness. A novel standard oscillation identification (SOI) method is proposed to precisely identify the iteration oscillations of basic SSOCP in the second layer, which can significantly improve the solution accuracy. A soft-trust-region strategy is applied in the third layer to eliminate oscillations and accelerate convergence. Simulation results of two scenarios demonstrate that the proposed SOI method effectively avoids non-standard oscillation interference versus traditional methods. The morphing aircraft can complete tasks better with a 7.01% and 10.43% reduction in heat load respectively compared to fixed-wing aircraft. The HSSOCP method can maintain accuracy while reducing computation time by 63.47% and 73.86% versus VATSSOCP. Monte Carlo simulations further validate the robustness.

Original languageEnglish
JournalDefence Technology
DOIs
Publication statusAccepted/In press - 2025
Externally publishedYes

Keywords

  • Cross-domain vehicle
  • Entry trajectory optimization
  • Hybrid scheme
  • Iteration oscillation
  • Morphing vehicle
  • Sequential second-order cone programming

Fingerprint

Dive into the research topics of 'Entry trajectory optimization for cross-domain morphing vehicles by hybrid sequential second-order cone programming'. Together they form a unique fingerprint.

Cite this