Entry Guidance for Hypersonic Glide Vehicles via Two-Phase hp-Adaptive Sequential Convex Programming
This paper addresses the real-time trajectory generation problem for hypersonic glide vehicles (HGVs) during atmospheric entry, subject to complex constraints including aerothermal limits, actuator bounds, and no-fly zones (NFZs). To achieve efficient and reliable trajectory planning, a two-phase hp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/6/539 |
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| author | Xu Liu Xiang Li Houjun Zhang Hao Huang Yonghui Wu |
| author_facet | Xu Liu Xiang Li Houjun Zhang Hao Huang Yonghui Wu |
| author_sort | Xu Liu |
| collection | DOAJ |
| description | This paper addresses the real-time trajectory generation problem for hypersonic glide vehicles (HGVs) during atmospheric entry, subject to complex constraints including aerothermal limits, actuator bounds, and no-fly zones (NFZs). To achieve efficient and reliable trajectory planning, a two-phase hp-adaptive sequential convex programming (SCP) framework is proposed. NFZ avoidance is reformulated as a soft objective to enhance feasibility under tight geometric constraints. In Phase I, a shrinking-trust-region strategy progressively tightens the soft trust-region radius by increasing the penalty weight, effectively suppressing linearization errors. A sensitivity-driven mesh refinement method then allocates collocation points based on their contribution to the objective function. Phase II applies residual-based refinement to reduce discretization errors. The resulting reference trajectory is tracked using a linear quadratic regulator (LQR) within a reference-trajectory-tracking guidance (RTTG) architecture. Simulation results demonstrate that the proposed method achieves convergence in only a few iterations, generating high-fidelity trajectories within 2–3 s. Compared to pseudospectral solvers, the method achieves over 12× computational speed-up while maintaining kilometer-level accuracy. Monte Carlo tests under uncertainties confirm a 100% success rate, with all constraints satisfied. These results validate the proposed method’s robustness, efficiency, and suitability for onboard real-time entry guidance in dynamic mission environments. |
| format | Article |
| id | doaj-art-ad3126a50fe74557a36f35fc8bccbf12 |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-ad3126a50fe74557a36f35fc8bccbf122025-08-20T03:24:26ZengMDPI AGAerospace2226-43102025-06-0112653910.3390/aerospace12060539Entry Guidance for Hypersonic Glide Vehicles via Two-Phase hp-Adaptive Sequential Convex ProgrammingXu Liu0Xiang Li1Houjun Zhang2Hao Huang3Yonghui Wu4School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, ChinaChina Aerospace Science&Industry Delivery Technology Academy, Beijing 100039, ChinaSchool of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, ChinaThis paper addresses the real-time trajectory generation problem for hypersonic glide vehicles (HGVs) during atmospheric entry, subject to complex constraints including aerothermal limits, actuator bounds, and no-fly zones (NFZs). To achieve efficient and reliable trajectory planning, a two-phase hp-adaptive sequential convex programming (SCP) framework is proposed. NFZ avoidance is reformulated as a soft objective to enhance feasibility under tight geometric constraints. In Phase I, a shrinking-trust-region strategy progressively tightens the soft trust-region radius by increasing the penalty weight, effectively suppressing linearization errors. A sensitivity-driven mesh refinement method then allocates collocation points based on their contribution to the objective function. Phase II applies residual-based refinement to reduce discretization errors. The resulting reference trajectory is tracked using a linear quadratic regulator (LQR) within a reference-trajectory-tracking guidance (RTTG) architecture. Simulation results demonstrate that the proposed method achieves convergence in only a few iterations, generating high-fidelity trajectories within 2–3 s. Compared to pseudospectral solvers, the method achieves over 12× computational speed-up while maintaining kilometer-level accuracy. Monte Carlo tests under uncertainties confirm a 100% success rate, with all constraints satisfied. These results validate the proposed method’s robustness, efficiency, and suitability for onboard real-time entry guidance in dynamic mission environments.https://www.mdpi.com/2226-4310/12/6/539hypersonic glide vehicleentry guidancesequential convex programmingadaptive mesh refinementreal-time trajectory optimization |
| spellingShingle | Xu Liu Xiang Li Houjun Zhang Hao Huang Yonghui Wu Entry Guidance for Hypersonic Glide Vehicles via Two-Phase hp-Adaptive Sequential Convex Programming Aerospace hypersonic glide vehicle entry guidance sequential convex programming adaptive mesh refinement real-time trajectory optimization |
| title | Entry Guidance for Hypersonic Glide Vehicles via Two-Phase hp-Adaptive Sequential Convex Programming |
| title_full | Entry Guidance for Hypersonic Glide Vehicles via Two-Phase hp-Adaptive Sequential Convex Programming |
| title_fullStr | Entry Guidance for Hypersonic Glide Vehicles via Two-Phase hp-Adaptive Sequential Convex Programming |
| title_full_unstemmed | Entry Guidance for Hypersonic Glide Vehicles via Two-Phase hp-Adaptive Sequential Convex Programming |
| title_short | Entry Guidance for Hypersonic Glide Vehicles via Two-Phase hp-Adaptive Sequential Convex Programming |
| title_sort | entry guidance for hypersonic glide vehicles via two phase hp adaptive sequential convex programming |
| topic | hypersonic glide vehicle entry guidance sequential convex programming adaptive mesh refinement real-time trajectory optimization |
| url | https://www.mdpi.com/2226-4310/12/6/539 |
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