PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 11, 2026Guidance Navigation and Control0 citations

Position-Domain GS-MPCP for Multi-Constraint Flight Vehicle Trajectory Planning

View Full Paper
YZYu ZhangZWZheng WangYLYufeng Liu

Key Points

  • To develop a trajectory planning framework that manages multiple constraints effectively without fixed time parameters.
  • Developed a position-domain guidance optimization framework
  • Proposed the position-domain generalized spectral model predictive convex programming (PGSMPCP) method
  • Focused on geometric constraints like detours and no-fly zones
  • Validated the method with a sea-skimming precision-strike planning problem
  • Conducted Monte Carlo studies for robustness verification
  • PGS-MPCP significantly improves terminal and waypoint accuracy
  • Achieves better feasibility than traditional MPSP variants
  • Provides a balance of computational efficiency and terminal performance
  • Demonstrated stable convergence under uncertainty scenarios

Abstract

This paper develops a position-domain guidance optimization framework that adopts along-track position as the independent variable, enabling spatially indexed enforcement of geometric constraints and decoupling trajectory shaping from time parameterization. Building on this framework, a position-domain generalized spectral model predictive convex programming (PGSMPCP) method is proposed to remove reliance on a prespecified terminal time or fixed time window, thereby mitigating time-window-induced infeasibility in multi-constraint missions involving detours, altitude corridors, and no-fly-zone avoidance. The method is validated on a representative sea-skimming precision-strike planning problem with waypoint and terminal impact-angle constraints. Numerical results demonstrate that PGS–MPCP provides substantial improvements over representative MPSP variants in terms of terminal/waypoint accuracy and feasibility preservation. Relative to convex-optimization and pseudospectral methods, it achieves a favorable balance among computational efficiency, constraint satisfaction, and terminal performance, which makes it promising for online planning and real-time guidance. Additional Monte Carlo studies under both initial-condition perturbations and waypoint-geometry uncertainty further verify stable convergence and robust feasibility preservation.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69d9e5ec78050d08c1b76231https://doi.org/10.1142/s2737480726500093
Ask AI
Helpful
Bookmark
Share
View Full Paper