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arxiv: 2604.23013 · v1 · submitted 2026-04-24 · 📡 eess.SY · astro-ph.EP· astro-ph.IM· cs.SY

Integrated Lander-Propulsion-GNC Framework for Autonomous Lunar Powered Descent

Emre Aklan , Fatih Seker , Bekir Gencalioglu , Mehmet Batuhan Kaya , Yigit Serceoglu , Furkan Yavuz , Omer Burak Iskender , Burak Yaglioglu This is my paper

Pith reviewed 2026-05-08 10:26 UTC · model grok-4.3

classification 📡 eess.SY astro-ph.EPastro-ph.IMcs.SY
keywords lunar powered descentautonomous GNCsuccessive convexificationthrottleable propulsionlanding precisionMonte Carlo simulationvehicle-propellant integrationreal-time guidance
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The pith

An integrated lander-propulsion-GNC system with successive convexification guidance reaches sub-50 meter lunar landing precision in Monte Carlo tests.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper builds a complete framework that links the physical design of a vertical takeoff vertical landing test vehicle to a throttleable bipropellant engine and a real-time guidance algorithm. The successive convexification method converts thrust limits, fuel use, and engine dead zones into a single optimization problem that runs fast enough for onboard use. Parametric studies identify a direct link between how much the engine can throttle, its ability to point, and the Moon's gravity. Monte Carlo runs under modeled disturbances confirm the system lands within 50 meters of the target in most cases. This matters because precise autonomous landings reduce the risk and cost of placing equipment on the lunar surface without constant human oversight.

Core claim

The integrated framework for the BUG VTVL vehicle and YUNT V0 engine uses successive convexification to solve the full powered descent problem as a second-order cone program, incorporating mass depletion, thrust bounds, and dead-zone constraints, and Monte Carlo simulations under realistic perturbations demonstrate sub-50 meter landing accuracy.

What carries the argument

The successive convexification algorithm, which converts all nonconvex constraints of powered descent guidance into a unified second-order cone program solvable in real time while accounting for variable thrust, mass change, and engine limits.

Load-bearing premise

The computer models of the vehicle's motion, engine throttle behavior, and disturbance forces match how the actual hardware will perform during a real lunar descent.

What would settle it

Flight test data from the BUG VTVL vehicle during powered descent that records a final position error larger than 50 meters when using the described guidance under conditions matching the simulation disturbances.

Figures

Figures reproduced from arXiv: 2604.23013 by Bekir Gencalioglu, Burak Yaglioglu, Emre Aklan, Fatih Seker, Furkan Yavuz, Mehmet Batuhan Kaya, Omer Burak Iskender, Yigit Serceoglu.

Figure 1
Figure 1. Figure 1: BUG landing test vehicle (H = 2.26 m, D = 3.77 m, hCG = 1.47 m). Red ⊕ denotes centre of mass. (a) (b) view at source ↗
Figure 3
Figure 3. Figure 3: YUNT V0 hot-fire test. thermal-model anchoring, and multi-point operation at dis￾crete throttle settings spanning the targeted envelope. Across 16 firings ( view at source ↗
Figure 4
Figure 4. Figure 4: Chamber-pressure time history: ignition, steady-state, and shutdown. view at source ↗
Figure 6
Figure 6. Figure 6: Hop trajectories for θmax ∈ {10◦, . . . , 60◦}. Fuel cost and flight time decrease as tilt authority increases. 0 250 500 750 1000 1250 1500 1750 2000 Downrange (m) 0 100 200 300 Altitude (m) Closed-Loop Monte Carlo Hop Trajectories (N = 1000) Nominal Launch Target view at source ↗
Figure 7
Figure 7. Figure 7: Closed-loop Monte Carlo (N=1000): nominal (green), failure modes view at source ↗
Figure 5
Figure 5. Figure 5: Lossless convexification: (a) nonconvex set; (b) lifted SOC. view at source ↗
read the original abstract

This paper presents an integrated lander-propulsion-GNC framework for autonomous lunar powered descent. The BUG VTVL test vehicle serves as the reference platform, with the YUNT V0 throttleable bipropellant engine providing variable thrust across a wide operating envelope, integrated with a real-time successive convexification guidance solver. The vehicle design accounts for structural configuration, landing stability, center-of-mass migration, and inertia evolution, while the propulsion architecture defines the throttle ratio, dead-zone behavior, and gimbal authority that constrain the guidance problem. A successive convexification algorithm addresses all nonconvexities; thrust lower bounds, mass depletion coupling, and thruster dead-zone behavior are all handled within a unified second-order cone program solvable in near-real time. Parametric analysis reveals a fundamental coupling between throttle ratio, pointing authority, and surface gravity. Monte Carlo simulations validate guidance robustness, achieving sub-50-meter landing precision under realistic perturbations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. This paper presents an integrated lander-propulsion-GNC framework for autonomous lunar powered descent. It uses the BUG VTVL test vehicle with the YUNT V0 throttleable bipropellant engine, incorporating vehicle structural configuration, CoM migration, inertia evolution, throttle ratio, dead-zone behavior, and gimbal authority. A successive convexification algorithm formulates all nonconvexities (thrust lower bounds, mass depletion coupling, dead-zone) into a unified SOCP solvable in near real time. Parametric analysis identifies couplings between throttle ratio, pointing authority, and surface gravity. Monte Carlo simulations are reported to validate robustness with sub-50 m landing precision under realistic perturbations.

Significance. If the underlying models prove sufficiently accurate, the work offers a practical demonstration of embedding realistic propulsion constraints directly into a real-time convex guidance solver for lunar descent, which addresses a key implementation gap between theory and hardware limits. The unified SOCP treatment of multiple nonconvex effects and the parametric coupling analysis are clear technical strengths that could inform future mission design. The simulation-based precision result, however, has reduced significance absent explicit checks on model fidelity.

major comments (1)
  1. [Monte Carlo Simulations] Monte Carlo Simulations (as described in the abstract): The central claim of sub-50 m landing precision under realistic perturbations rests on the fidelity of the BUG VTVL vehicle dynamics, YUNT V0 propulsion constraints (throttle ratio, dead-zone, gimbal authority), mass depletion, and disturbance models. No hardware-in-the-loop testing, comparison to engine firing data, or sensitivity analysis quantifying the impact of unmodeled effects (e.g., propellant slosh, thermal thrust variation, sensor biases) is provided. This directly undermines the transferability of the reported robustness.
minor comments (1)
  1. [Abstract] Abstract: The statement that 'all nonconvexities' are handled would be clearer if it explicitly enumerated the full set (beyond the three mentioned) and briefly noted the SOCP reformulation steps for each.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for the detailed assessment and for identifying the need to strengthen the discussion of model fidelity supporting the Monte Carlo results. We address the comment below and propose targeted revisions.

read point-by-point responses

  1. Referee: Monte Carlo Simulations (as described in the abstract): The central claim of sub-50 m landing precision under realistic perturbations rests on the fidelity of the BUG VTVL vehicle dynamics, YUNT V0 propulsion constraints (throttle ratio, dead-zone, gimbal authority), mass depletion, and disturbance models. No hardware-in-the-loop testing, comparison to engine firing data, or sensitivity analysis quantifying the impact of unmodeled effects (e.g., propellant slosh, thermal thrust variation, sensor biases) is provided. This directly undermines the transferability of the reported robustness.

    Authors: The vehicle dynamics and propulsion models are parameterized from the documented specifications of the BUG VTVL platform and YUNT V0 engine, including CoM migration, inertia evolution, throttle ratio, dead-zone behavior, and gimbal limits. The Monte Carlo campaign applies perturbations consistent with these specifications. We acknowledge that the manuscript does not contain hardware-in-the-loop testing or direct comparisons against engine firing data. To improve the assessment of robustness, the revised manuscript will include an additional sensitivity analysis that quantifies the effects of propellant slosh, thermal thrust variation, and sensor biases on landing precision and guidance performance. This will be presented in a new subsection with corresponding figures. revision: partial

standing simulated objections not resolved
  • Hardware-in-the-loop testing results and direct comparisons to engine firing data, which are outside the simulation-based scope of the current study.

Circularity Check

0 steps flagged

No circularity detected in derivation or validation

full rationale

The paper describes an integrated lander-propulsion-GNC framework that models vehicle dynamics, propulsion constraints (throttle ratio, dead-zone, gimbal authority), mass depletion, and inertia evolution for the BUG VTVL platform with YUNT V0 engine. It applies a successive convexification algorithm to formulate the nonconvex guidance problem as a solvable second-order cone program. Monte Carlo simulations then validate robustness, reporting sub-50 m landing precision under modeled perturbations. No load-bearing step reduces a claimed result to a fitted input by construction, self-defines a quantity in terms of itself, or relies on a self-citation chain whose content is unverified outside the paper. The validation rests on external simulation execution rather than internal redefinition, and the central claims retain independent content from the optimization formulation and perturbation modeling.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Only the abstract is available, so the ledger is limited to assumptions stated at high level; no explicit free parameters or invented entities are named.

axioms (2)
  • domain assumption Vehicle mass, inertia, and center-of-mass evolve predictably with propellant depletion during descent.
    Invoked when stating that the vehicle design accounts for mass depletion coupling.
  • domain assumption Thrust lower bounds, dead-zone behavior, and gimbal limits can be expressed as convex constraints after successive convexification.
    Central to the claim that all nonconvexities are handled in a unified SOCP.

pith-pipeline@v0.9.0 · 5501 in / 1274 out tokens · 26603 ms · 2026-05-08T10:26:08.342893+00:00 · methodology

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Reference graph

Works this paper leans on

31 extracted references · 31 canonical work pages

  1. [1]

    Convex programming approach to powered descent guidance for Mars landing,

    B. Açıkme¸ se and S. R. Ploen, “Convex programming approach to powered descent guidance for Mars landing,”J. Guid. Control Dyn., vol. 30, no. 5, pp. 1353-1366, 2007

    work page 2007

  2. [2]

    Lossless convexification of a class of optimal control problems with non-convex control constraints,

    B. Açıkme¸ se and L. Blackmore, “Lossless convexification of a class of optimal control problems with non-convex control constraints,”Auto- matica, vol. 47, no. 2, pp. 341-347, 2011

    work page 2011

  3. [3]

    Minimum-landing-error powered-descent guidance for Mars landing using convex optimization,

    L. Blackmore, B. Açıkme¸ se, and D. P. Scharf, “Minimum-landing-error powered-descent guidance for Mars landing using convex optimization,” J. Guid. Control Dyn., vol. 33, no. 4, pp. 1161-1171, 2010

    work page 2010

  4. [4]

    Autonomous precision landing of space rockets,

    L. Blackmore, “Autonomous precision landing of space rockets,”The Bridge, vol. 46, no. 4, pp. 15-20, 2016

    work page 2016

  5. [5]

    Successive convexification of non-convex optimal control problems and its convergence properties,

    Y . Mao, M. Szmuk, and B. Açıkme¸ se, “Successive convexification of non-convex optimal control problems and its convergence properties,” in Proc. IEEE Conf. Decision and Control (CDC), Las Vegas, NV , 2016, pp. 3636-3641

    work page 2016

  6. [6]

    Successive convexification for 6-DoF Mars rocket powered landing with free-final-time,

    M. Szmuk and B. Açıkme¸ se, “Successive convexification for 6-DoF Mars rocket powered landing with free-final-time,” inProc. AIAA SciTech Forum, Kissimmee, FL, 2018, AIAA 2018-0617

    work page 2018

  7. [7]

    Convex optimization for trajectory generation: A tutorial on generating dynamically feasible trajectories reliably and efficiently,

    D. Malyuta, T. P. Reynolds, M. Szmuk, T. Lew, R. Bonalli, M. Pavone, and B. Açıkme¸ se, “Convex optimization for trajectory generation: A tutorial on generating dynamically feasible trajectories reliably and efficiently,”IEEE Control Syst. Mag., vol. 42, no. 5, pp. 40-113, 2022

    work page 2022

  8. [8]

    Dual quaternion based autonomous rendezvous and docking via model predictive control,

    O. B. Iskender, K. V . Ling, L. Simonini, M. Schlotterer, D. Seelbinder, and S. Theil, “Dual quaternion based autonomous rendezvous and docking via model predictive control,” inProc. 70th Int. Astronautical Congr. (IAC), Washington, DC, 2019, pp. 1-16

    work page 2019

  9. [9]

    Model predictive control for spacecraft rendezvous and docking with uncooperative targets,

    O. B. Iskender, “Model predictive control for spacecraft rendezvous and docking with uncooperative targets,” Ph.D. dissertation, Nanyang Technological Univ., Singapore, 2020

    work page 2020

  10. [10]

    Constraints tightening approach towards model predictive control based rendezvous and dock- ing with uncooperative targets,

    O. B. Iskender, K. V . Ling, and V . Dubanchet, “Constraints tightening approach towards model predictive control based rendezvous and dock- ing with uncooperative targets,” inProc. European Control Conf. (ECC), Limassol, Cyprus, 2018, pp. 380-385

    work page 2018

  11. [11]

    3D soft-landing dynamic theoretical model of legged lander: Modeling and analysis,

    Z. Wang, C. Chen, J. Chen, and G. Zheng, “3D soft-landing dynamic theoretical model of legged lander: Modeling and analysis,”Aerospace, vol. 10, p. 811, 2023

    work page 2023

  12. [12]

    The design of a common lunar lander,

    D. Driggers et al., “The design of a common lunar lander,” NASA Report, 1991

    work page 1991

  13. [13]

    Lunar lander structural design studies at NASA Langley,

    K. Wu et al., “Lunar lander structural design studies at NASA Langley,” NASA Technical Report, 2007

    work page 2007

  14. [14]

    Preliminary design of reusable lunar lander landing system,

    R. Weixiong, “Preliminary design of reusable lunar lander landing system,” M.S. thesis, 2017

    work page 2017

  15. [15]

    Touchdown dynamics and the probability of terrain related failure of planetary landing systems,

    L. Witte, “Touchdown dynamics and the probability of terrain related failure of planetary landing systems,” Ph.D. dissertation, 2015

    work page 2015

  16. [16]

    Landing gear design and stability evaluation of a lunar lander for soft landing,

    A. ¸ Sahinöz, “Landing gear design and stability evaluation of a lunar lander for soft landing,” inProceedings of the Bennett Conference on Mechanical Engineering, Pittsburgh, PA, USA, 2012, pp. 1-8

    work page 2012

  17. [17]

    Experimental study of over- turning prevention for lunar and planetary landers by optimizing footpad tilt angle,

    K. Matsuura, T. Maeda, and T. Hashimoto, “Experimental study of over- turning prevention for lunar and planetary landers by optimizing footpad tilt angle,” inProceedings of the Planetary Science and Technology Workshop, 2019, pp. 1-6

    work page 2019

  18. [18]

    Colibri VTVL hopper: Architecture and propulsion subsystem,

    Gruyère Space Program, “Colibri VTVL hopper: Architecture and propulsion subsystem,” École Polytechnique Fédérale de Lausanne, Switzerland, 2024. [Online]. Available: https://gruyerespaceprogram.ch/

    work page 2024

  19. [19]

    Open-loop flight testing of COBALT GN&C technologies for precise soft landing,

    J. M. Carson et al., “Open-loop flight testing of COBALT GN&C technologies for precise soft landing,” inProc. AIAA SPACE and Astronautics Forum and Exposition, Orlando, FL, 2017, pp. 1-10

    work page 2017

  20. [20]

    Project Morpheus: Lean development of a terrestrial flight testbed for maturing NASA lander technologies,

    J. D. Deaton et al., “Project Morpheus: Lean development of a terrestrial flight testbed for maturing NASA lander technologies,” NASA Johnson Space Center, Houston, TX, NTRS Doc. No. 20140017130, 2014

    work page 2014

  21. [21]

    Blue Ghost lunar lander: Payload user’s guide,

    Firefly Aerospace, “Blue Ghost lunar lander: Payload user’s guide,” Rev. 1.1, Cedar Park, TX, 2024

    work page 2024

  22. [22]

    Hakuto-R Mission 2: Resilience technical overview,

    ispace, inc., “Hakuto-R Mission 2: Resilience technical overview,” Tokyo, Japan, 2024

    work page 2024

  23. [23]

    CLPS Intuitive Machines 2 lunar mission press kit,

    NASA and Intuitive Machines, Inc., “CLPS Intuitive Machines 2 lunar mission press kit,” Houston, TX, 2024

    work page 2024

  24. [24]

    TRW pintle engine heritage and performance characteristics,

    G. A. Dressler and J. M. Bauer, “TRW pintle engine heritage and performance characteristics,” inProc. 36th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Huntsville, AL, 2000, AIAA-2000- 3871

    work page 2000

  25. [25]

    Liquid-propellant rocket en- gine throttling,

    M. J. Casiano, J. R. Hulka, and V . Yang, “Liquid-propellant rocket en- gine throttling,” inProc. 45th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Denver, CO, 2009, AIAA-2009-5135

    work page 2009

  26. [26]

    Design of pintle injector using kerosene-LOx as propellant and investigation of pintle tip thermal damage in hot-fire testing,

    D. Kang, S. Kim, H. Kim, and S. Kwon, “Design of pintle injector using kerosene-LOx as propellant and investigation of pintle tip thermal damage in hot-fire testing,”Acta Astronautica, vol. 201, pp. 48-59, 2022

    work page 2022

  27. [27]

    Green propellant infusion mission program development and technology maturation,

    C. H. McLean, “Green propellant infusion mission program development and technology maturation,” inProc. 50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Cleveland, OH, 2014, AIAA-2014- 3481

    work page 2014

  28. [28]

    Chambers,Liquid Rocket Engine Fluid-Cooled Combustion Chambers, NASA Space Publication 8087, 1972

    C. Chambers,Liquid Rocket Engine Fluid-Cooled Combustion Chambers, NASA Space Publication 8087, 1972

    work page 1972

  29. [29]

    Additive manufacturing of liquid rocket engine combustion devices: A summary of process developments and hot- fire testing results,

    P. R. Gradl et al., “Additive manufacturing of liquid rocket engine combustion devices: A summary of process developments and hot- fire testing results,” inProc. 54th AIAA/SAE/ASEE Joint Propulsion Conference, Cincinnati, OH, 2018, AIAA-2018-4625

    work page 2018

  30. [30]

    arXiv preprint arXiv:2405.12762 (2 024)

    P. J. Goulart and Y . Chen, “Clarabel: An interior-point solver for conic programs with quadratic objectives,” arXiv:2405.12762, 2024

    work page arXiv 2024

  31. [31]

    CVXPY: A Python-embedded modeling language for convex optimization,

    S. Diamond and S. Boyd, “CVXPY: A Python-embedded modeling language for convex optimization,”J. Mach. Learn. Res., vol. 17, no. 83, pp. 1-5, 2016

    work page 2016