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REVIEW 4 major objections 4 minor 52 references

Constrained Fuel and Time Optimal 6DOF Powered Descent Guidance Using Indirect Optimization

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper claims that a regularized indirect method can solve the constrained six-degree-of-freedom powered descent guidance problem for both minimum fuel and minimum time, producing extremal solutions with two and three thrust magnitude…

desk verdict First credible indirect solutions to constrained 6DOF powered descent guidance, with a small but real boundary-condition normalization issue and some unproven penalty claims. read the letter →

arxiv 2501.14173 v1 pith:BWEPOPPZ submitted 2025-01-24 math.OC cs.SYeess.SY

Nicholas P. Nurre , Ehsan Taheri This is my paper · ORCID
classification math.OCcs.SYeess.SY MSC 49K1549M0570Q05
keywords powereddescentguidancesix-degree-of-freedomdynamicsindirectoptimizationstate-onlypathconstraintsinteriorpenaltymethodmultipleshootingfuel-optimaltime-optimal
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims a regularized indirect optimization method can solve the constrained six-degree-of-freedom (6DOF) powered descent guidance problem in both fuel-optimal and time-optimal forms, with inequality constraints on thrust magnitude, gimbal angle, tilt angle, glideslope angle, and angular velocity. The payoff is that the method returns solutions satisfying the first-order necessary conditions of optimality, so the structure of the optimal control, in particular the number and timing of thrust-magnitude switches, comes out of the calculation rather than being guessed. For the studied parameters, the fuel-optimal solution has two thrust switches and the time-optimal solution has three, and the paper validates these against an independent pseudospectral solver. If the method holds, it gives a practical verification path for 6DOF landing guidance, a problem so far dominated by direct and convex methods and whose optimal control structure has been regarded as open.

What carries the argument

The central machinery is a regularized indirect method assembled from four parts. First, control constraints are directly adjoined to the Hamiltonian, producing closed-form piecewise controls: the thrust magnitude $T^*$ through a switching function $S_T$, and the steering direction $\hat{\boldsymbol{\alpha}}^*$ through a gimbal switching function $S_\delta$. Second, state-only path inequality constraints are enforced with secant penalty functions $\tilde S_i = \sec(\frac{\pi}{2} P_i)$ whose weights $\rho_i$ are driven toward zero by continuation, so a multipoint boundary-value problem becomes a smooth family of two-point boundary-value problems. Third, indirect multiple shooting with propagated sensitivity matrices keeps the numerically unstable integration tractable. Fourth, Conjecture 1 gives $\tilde\eta_i = -\rho_i \sec(\frac{\pi}{2}P_i)/S_i$ as an a posteriori approximation of the direct-adjoining Lagrange multiplier for each active constraint, allowing complementarity to be checked without solving the constrained problem directly.

What would settle it

Reduce the tilt-angle penalty weight well below $10^{-12}$ in the fuel-optimal case and check whether the initial tilt angle approaches exactly $90^\circ$ while the Hamiltonian stays constant; if the constraint boundary is violated, the multiplier diverges, or the Hamiltonian drifts, then the penalty-family convergence that the method depends on does not hold.

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Extended reading notes

Core claim

The central claim, stated on the paper's own terms, is that indirect methods can be applied to the full 6DOF powered descent guidance problem and yield high-accuracy extremal solutions without prior knowledge of the active-constraint sequence. The paper derives closed-form expressions for the thrust magnitude and the thrust-steering direction under the gimbal-angle constraint, enforces the three state-only path inequality constraints (SOPICs) with secant penalty functions, and solves the resulting boundary-value problems with an indirect multiple-shooting method and numerical continuation. It also proposes an empirical relation, Conjecture 1, that recovers the direct-adjoining Lagrange multipliers of the active path constraints from penalty solutions, and it validates the entire approach by comparing states, controls, costates, and multipliers against an independent pseudospectral solver and against a benchmark problem with an analytic solution.

Load-bearing premise

The load-bearing premise is that the secant penalty solutions converge to exact path-constraint satisfaction as the penalty weights go to zero, and that the empirical multiplier formula recovers the true direct-adjoining multipliers; the paper explicitly says neither is rigorously proven.

Editorial extensions

If this is right

  • For the studied parameter set, the fuel-optimal thrust magnitude is bang-bang with exactly two switches and the time-optimal with three, giving a concrete control structure that direct methods can be checked against.
  • The closed-form gimbal-constrained steering expression removes the steering control from the set of numerical unknowns, shrinking the boundary-value problem and removing a source of discretization error.
  • The secant-penalty continuation solves the state-only path constraints without requiring a priori knowledge of which constraints are active or when, so the method applies to problems where the active-constraint sequence is unknown.
  • The a posteriori multiplier recovery from Conjecture 1 lets practitioners verify complementarity slackness and detect whether an active constraint has been missed.
  • The same machinery transfers to simpler constrained optimal control problems, as demonstrated on the benchmark problem with an analytic solution.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, if the penalty-family convergence is rigorously established, the approach would give verification-quality extremal solutions for other 6DOF guidance problems, including those with state-triggered or attitude-dependent constraints, without the need for convexification.
  • The paper's own observation that hundreds of shooting segments are needed when path constraints are active suggests that an adaptive or mesh-refined segment placement would materially reduce cost; implementing such a scheme is a natural testable extension.
  • Because the model leaves roll uncontrollable, the observed constant offset in the angular-velocity costate points to a gauge freedom; fixing a roll-costate condition would make the multiplier comparison with direct methods fully one-to-one.
  • The empirical multiplier relation, if proven, would close the gap between penalty methods and exact constrained necessary conditions; a first test is to apply it to a problem with a third-order state constraint, where the impulse structure differs.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper develops a regularized indirect method for fuel- and time-optimal six-degree-of-freedom powered descent guidance (6DOF PDG) with state-only path inequality constraints on tilt angle, glideslope angle, and angular velocity magnitude, plus control constraints on thrust magnitude and gimbal angle. First-order necessary conditions are derived from the Hamiltonian, the thrust and steering controls are obtained in closed form under a gimbal-angle constraint, and the state path constraints are enforced through secant penalty functions embedded in a numerical continuation over smoothing parameters. The resulting multipoint boundary-value problems are solved with an indirect multiple-shooting scheme, and the solutions are compared with independent DIDO solutions and with the analytic Breakwell problem. The paper reports that the fuel-optimal trajectory has two thrust-magnitude switches and the time-optimal trajectory has three, and claims this is the first application of indirect methods to the full 6DOF PDG problem.

Significance. If the results hold, the paper makes a useful contribution: it demonstrates that a carefully regularized indirect method can produce high-accuracy extremal solutions for a constrained 6DOF rocket landing problem, with closed-form control expressions and an a posteriori recovery of state-constraint multipliers. The validation strategy is a clear strength: the indirect solutions are compared against independent DIDO solutions and against the analytic Breakwell benchmark, and the derivation of the necessary conditions is standard and transparent. However, the paper currently contains a load-bearing boundary-condition issue (non-unit final quaternion), an unsupported assertion that modified boundary conditions have no impact, and an unproven convergence claim for the secant penalty method. These issues must be resolved before the central quantitative claims can be accepted.

major comments (4)
  1. [Section VI, Table 2; Eqs. (1), (2), (8)] The final orientation boundary condition q_f = [0, 0, 0.01, 1]^T in Table 2 has Euclidean norm sqrt(1.0001), not 1. Since the quaternion kinematics in Eq. (2) conserve q^T q, the entire solved trajectory is a non-unit quaternion history. Consequently, the direction cosine matrix in Eq. (1) is not orthonormal, and the tilt angle computed from Eq. (8) is not the physical angle between the body and inertial vertical axes. This means the boundary-value problem actually solved is not the stated rigid-body landing problem, and the reported thrust-switch counts, final mass, and optimality checks pertain to that modified problem. The assertion that this slight modification has no impact is not substantiated; please re-solve with a unit-norm final quaternion (for example, by normalizing q_f or by imposing an explicit unit-norm constraint) and report whether the switching structure, objective values, and costate profiles change.
  2. [Section VI, Table 2; Section IV, remark after Eq. (15)] The final boundary conditions are modified from Ref. [13]: r_z(t_f) is changed to 0.01 LU and q_f is changed to [0, 0, 0.01, 1]. The first modification is motivated by avoiding a glideslope-angle singularity, but no sensitivity analysis shows that the solution is representative of the original r_z(t_f) = 0 problem; the second modification is likewise asserted to have no impact. Please provide a convergence study as the perturbation sizes tend to zero, or solve the original boundary conditions with a robust formulation (for example, an atan2-based glideslope angle and a normalized quaternion), and quantify changes in the final mass, time of flight, and switching structure.
  3. [Section IV, Conjecture 1, Eq. (16)] The formula for the approximate Lagrange multiplier is presented as an equality between the direct-adjoining Hamiltonian and the penalty Hamiltonian, but the algebra does not support the sign: with S_i ≤ 0 and rho_i sec(pi/2 P_i) ≥ 0, the term eta_i S_i in the direct adjoining approach is non-positive while the penalty term is non-negative, so the negative sign in Eq. (16) is imposed to satisfy complementarity rather than derived. Moreover, no asymptotic argument is given for the limit rho_i → 0; the ratio in Eq. (16) could in principle depend on how rho_i and S_i approach zero. Since this conjecture is one of the three stated contributions, it needs either a rigorous asymptotic derivation or a clear reframing as an empirical approximation with convergence evidence beyond the two test cases.
  4. [Section V, Eqs. (12)-(13); Section VI] The central claim that high-accuracy solutions satisfy the necessary conditions rests on the secant penalty functions in Eq. (12) converging to exact state-only path constraint satisfaction as rho_i → 0. The paper explicitly states that this is not rigorously shown, and the cited convergence results for interior penalty methods (Refs. 37 and 42) do not cover the singular secant penalty used here. Please provide a convergence argument adapted to this penalty, or at least a systematic error study (constraint violation, final mass, Hamiltonian constancy as functions of rho_i) to quantify how close the reported solutions are to the exact constrained optimum.
minor comments (4)
  1. [Section VI, first paragraph] The text reads '6DOG PDG problems'; this should be '6DOF PDG problems'.
  2. [Abstract and Section IV, Conjecture 1] The abstract states that an empirical relation is 'derived' for the Lagrange multipliers, but the paper itself presents the relation as a conjecture without rigorous proof; the wording should be softened to 'proposed and empirically validated'.
  3. [Section VI.A, Fig. 10(d)] The discrepancy in lambda_omega_z is attributed to the roll degree of freedom being uncontrollable, but no verification is given that the offset is a gauge freedom. Please state explicitly whether the roll dynamics and costate equation are invariant under the observed offset, or provide a numerical check that the offset does not affect the other variables.
  4. [Section V and VI] No code or data availability statement is provided; for reproducibility, the continuation schedules, shooting-segment counts, and solver settings would be valuable as supplementary material.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the derivation is self-contained and validated against independent DIDO and analytic Breakwell solutions.

full rationale

The paper's central derivation is self-contained: it starts from the Hamiltonian (Eq. 14), derives closed-form control expressions from the strong form of optimality (Eqs. 22-30), and solves the resulting multipoint boundary-value problem via multiple shooting with numerical continuation. No fitted parameter is renamed as a prediction; the continuation and smoothing parameters rho_T, rho_delta, rho_omega, rho_gamma, and rho_theta are homotopy knobs that are driven toward small values, not calibrated against the validation targets. The SOPIC Lagrange-multiplier relation (Conjecture 1, Eq. 16) is explicitly presented as a conjecture rather than a derived first-principles result, and it is empirically validated against DIDO and the analytic Breakwell problem, both of which are obtained independently of the indirect method. The self-citations (e.g., Refs. [32], [39], [44], [51], [52]) support algorithmic ingredients, but the relevant equations are reproduced in the paper and the central claim of first indirect solution of the 6DOF PDG problem does not reduce to those citations. The manuscript's own stated limitations, such as the lack of a rigorous proof of penalty-function convergence and the slight modification of the final quaternion boundary condition with an asserted 'no impact', are assumptions or correctness concerns rather than circular reasoning, so they do not raise the circularity score.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central claim rests on standard optimal control theory plus several unproven numerical assumptions. The free parameters are continuation and penalty weights plus small boundary-condition offsets chosen to make the BVP well-posed; none are fitted to the validation data. No new physical entities are introduced.

free parameters (7)
  • rho_T (thrust regularization parameter) = 1e-7 (final continuation value)
    Smoothing parameter in the L2-regularized thrust expression (Eq. (31)); hand-chosen continuation parameter, not fitted to data, but affects the bang-bang approximation and convergence.
  • rho_delta (gimbal regularization parameter) = 1e-4 (final continuation value)
    Smoothing parameter in the regularized gimbal switching expression (Eq. (32)); hand-chosen through continuation.
  • rho_omega (angular velocity penalty weight) = 0 for fuel-optimal; 1e-7 for time-optimal
    Secant penalty weight for the angular velocity SOPIC (Eq. (12)); zero when the constraint never becomes active.
  • rho_gamma (glideslope penalty weight) = 0 in both cases
    Secant penalty weight for the glideslope SOPIC; the constraint is never active in the presented solutions.
  • rho_theta (tilt angle penalty weight) = 1e-12 (fuel), 1e-7 (time)
    Secant penalty weight for the tilt-angle SOPIC; selected through continuation to approximate active constraint arcs.
  • Final vertical position offset, r_z(tf) = 0.01 LU
    Modified from the Ref. [13] boundary value 0 to avoid the tan^-1(0/0) singularity in the glideslope definition; claimed to have no impact on the solution.
  • Final orientation quaternion q_f = [0, 0, 0.01, 1]^T
    Slightly non-unit quaternion used as the final orientation to avoid the singularity in d(theta)/dq at q1=q2=0; the authors assert no impact without demonstration.
assumptions (5)
  • standard math Pontryagin's minimum principle and the Legendre-Clebsch condition are applicable to this nonsmooth optimal control problem.
    Used in Sec. IV to derive T* and alpha* and to select the positive sign for mu_3; standard results from Refs. [33, 45, 46].
  • ad hoc to paper Secant penalty functions (Eq. (12)) converge to exact state-only path constraint satisfaction as rho_i -> 0.
    The authors explicitly state 'we don't rigorously show this'; convergence is cited to Refs. [37, 42] rather than proven for this problem.
  • ad hoc to paper Conjecture 1: eta_i = -rho_i sec(pi/2 P_i)/S_i approximates the true direct-adjoining Lagrange multiplier as rho_i -> 0.
    Presented without proof in Sec. IV; only empirically validated with DIDO and the Breakwell problem.
  • ad hoc to paper The slightly modified final boundary conditions do not alter the optimal solution.
    The vertical position and final quaternion are adjusted to avoid singularities in Eq. (15) and S2; the paper asserts no impact without demonstration.
  • domain assumption The dynamical model in Sec. II, including flat non-rotating Earth, constant gravity and density, and no roll control, is a sufficient representation of the landing problem.
    Taken from Refs. [10, 13]; the paper notes the rocket is controllable in 5 of 6 DOF.

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Cite this review

Pith. "Pith review of Constrained Fuel and Time Optimal 6DOF Powered Descent Guidance Using Indirect Optimization." pith.science (2026). https://pith.science/paper/BWEPOPPZ

@misc{pith2026250114173,
  author       = {Pith},
  title        = {Pith review of: Constrained Fuel and Time Optimal 6DOF Powered Descent Guidance Using Indirect Optimization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BWEPOPPZ}},
  note         = {Machine review of arXiv:2501.14173}
}
read the original abstract

Powered descent guidance (PDG) problems subject to six-degrees-of-freedom (6DOF) dynamics allow for enforcement of practical attitude constraints. However, numerical solutions to 6DOF PDG problems are challenging due to fast rotational dynamics coupled with translational dynamics, and the presence of highly nonlinear state/control path inequality constraints. In this work, constrained fuel- and time-optimal 6DOF PDG problems are solved leveraging a regularized indirect method, subject to inequality constraints on the thrust magnitude, thruster gimbal angle, rocket tilt angle, glideslope angle, and angular velocity magnitude. To overcome the challenges associated with solving the resulting multipoint boundary-value problems (MPBVPs), the state-only path inequality constraints (SOPICs) are enforced through an interior penalty function method, which embeds the resulting MPBVPs into a multi-parameter smooth neighboring families of two-point BVPs. Extremal solutions are obtained using an indirect multiple-shooting solution method with numerical continuation. Moreover, an empirical relation is derived for the directly-adjoined Lagrange multipliers associated with SOPICs. The fuel- and time-optimal trajectories are compared against solutions of DIDO -- a capable pseudospectral-based software for solving practical constrained optimal control problems.

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Reference graph

Works this paper leans on

52 extracted references · 29 canonical work pages

  1. [13]

    Six-Degree-of-Freedom Rocket Landing Optimization via Augmented Convex–Concave Decomposition,

    Sagliano, M., Seelbinder, D., Theil, S., and Lu, P., “Six-Degree-of-Freedom Rocket Landing Optimization via Augmented Convex–Concave Decomposition,”Journal of Guidance, Control, and Dynamics, Vol. 47, No. 1, 2024, pp. 20–35. https://doi.org/10.2514/1.G007570

    work page doi:10.2514/1.g007570 2024

  2. [1]

    On the problem of optimal thrust programming for a lunar soft landing,

    Meditch, J., “On the problem of optimal thrust programming for a lunar soft landing,”IEEE Transactions on Automatic Control, Vol. 9, No. 4, 1964, pp. 477–484. https://doi.org/10.1109/TAC.1964.1105758

    work page arXiv 1964

  3. [2]

    Apollo lunar descent guidance,

    Klumpp, A. R., “Apollo lunar descent guidance,”Automatica, Vol. 10, No. 2, 1974, pp. 133–146. https://doi.org/10.1016/0005- 1098(74)90019-3

    doi:10.1016/0005- 1974

  4. [3]

    Introducing Computational Guidance and Control,

    Lu, P., “Introducing Computational Guidance and Control,”Journal of Guidance, Control, and Dynamics, Vol. 40, No. 2, 2017, pp. 193–193. https://doi.org/10.2514/1.G002745

    doi:10.2514/1.g002745 2017

  5. [4]

    Autonomous Precision Landing of Space Rockets,

    Blackmore, L., “Autonomous Precision Landing of Space Rockets,”Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2016 Symposium, Vol. 46, 2016, pp. 15–20

    2016

  6. [5]

    Convex Optimization for Trajectory Generation

    Malyuta, D., Reynolds, T. P., Szmuk, M., Lew, T., Bonalli, R., Pavone, M., and Acikmese, B., “Convex Optimization for Trajectory Generation,”arXiv:2106.09125 [cs, eess, math], 2021. ArXiv: 2106.09125

    work page Pith review arXiv 2021

  7. [6]

    Asurveyonconvexoptimizationforguidanceandcontrolofvehicularsystems,

    Wang, Z., “Asurveyonconvexoptimizationforguidanceandcontrolofvehicularsystems,” AnnualReviewsinControl , Vol.57, 2024, p. 100957. https://doi.org/10.1016/j.arcontrol.2024.100957

    arXiv 2024

  8. [7]

    Advances in trajectory optimization for space vehicle control,

    Malyuta, D., Yu, Y., Elango, P., and Açıkmeşe, B., “Advances in trajectory optimization for space vehicle control,”Annual Reviews in Control, Vol. 52, 2021, pp. 282–315. https://doi.org/10.1016/j.arcontrol.2021.04.013

    doi:10.1016/j.arcontrol.2021.04.013 2021

Show all 52 references
  1. [8]

    Optimal Control and Applications to Aerospace: Some Results and Challenges,

    Trélat, E., “Optimal Control and Applications to Aerospace: Some Results and Challenges,”Journal of Optimization Theory and Applications, Vol. 154, No. 3, 2012, pp. 713–758. https://doi.org/10.1007/s10957-012-0050-5

    2012 doi

  2. [9]

    Implementation and Experimental Demonstration of Onboard Powered-Descent Guidance,

    Scharf, D. P., Açıkmeşe, B., Dueri, D., Benito, J., and Casoliva, J., “Implementation and Experimental Demonstration of Onboard Powered-Descent Guidance,”Journal of Guidance, Control, and Dynamics, Vol. 40, No. 2, 2017, pp. 213–229. https://doi.org/10.2514/1.G000399

    2017 doi

  3. [10]

    Successive Convexification for Fuel-Optimal Powered Landing with Aerodynamic Drag and Non-Convex Constraints,

    Szmuk, M., Acikmese, B., and Berning, A. W., “Successive Convexification for Fuel-Optimal Powered Landing with Aerodynamic Drag and Non-Convex Constraints,”AIAA Guidance, Navigation, and Control Conference, American Institute of Aeronautics and Astronautics, San Diego, Califor...

    2016 doi

  4. [11]

    Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time,

    Szmuk, M., and Acikmese, B., “Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time,” 2018AIAAGuidance,Navigation,andControlConference ,2018,p.15. https://doi.org/10.2514/6.2018-0617,arXiv:1802.03827 [math]. 37

    2018 arXiv

  5. [12]

    Successive Convexification for Real-Time Six-Degree-of-Freedom Powered Descent Guidance with State-Triggered Constraints,

    Szmuk, M., Reynolds, T. P., and Açıkmeşe, B., “Successive Convexification for Real-Time Six-Degree-of-Freedom Powered Descent Guidance with State-Triggered Constraints,”Journal of Guidance, Control, and Dynamics, Vol. 43, No. 8, 2020, pp. 1399–1413. https://doi.org/10.2514/1.G004549

    2020 doi

  6. [14]

    Solving singular control problems using uniform trigonometrization method,

    Mall, K., Taheri, E., and Prabhu, P., “Solving singular control problems using uniform trigonometrization method,”AIChE Journal, Vol. 67, No. 6, 2021, p. e17209. https://doi.org/10.1002/aic.17209

    2021 doi

  7. [15]

    A switched system approach for the direct solution of singular optimal control problems,

    Andrés-Martínez, O., and Ricardez-Sandoval, L. A., “A switched system approach for the direct solution of singular optimal control problems,”Computers & Chemical Engineering, Vol. 168, 2022, p. 108058. https://doi.org/10.1016/j.compchemeng. 2022.108058

    2022

  8. [16]

    Method for solving bang-bang and singular optimal control problems using adaptive Radau collocation,

    Pager, E. R., and Rao, A. V., “Method for solving bang-bang and singular optimal control problems using adaptive Radau collocation,”Computational Optimization and Applications, Vol. 81, No.3, 2022, pp.857–887. https://doi.org/10.1007/s10589- 022-00350-6

    2022 doi

  9. [17]

    OptimalPlanarPoweredDescentwithIndependentThrustandTorque,

    Reynolds,T.P.,andMesbahi,M.,“OptimalPlanarPoweredDescentwithIndependentThrustandTorque,” JournalofGuidance, Control, and Dynamics, Vol. 43, No. 7, 2020, pp. 1225–1231. https://doi.org/10.2514/1.G004701

    2020 doi

  10. [18]

    Using Uniform Trigonometrization Method for Aviation Based Optimal Control Problems,

    Mall, K., Nolan, S. M., Levin, W. C., Risany, L., and DeLaurentis, D. A., “Using Uniform Trigonometrization Method for Aviation Based Optimal Control Problems,”AIAA AVIATION 2023 Forum, American Institute of Aeronautics and Astronautics, San Diego, CA and Online, 2023, p. 16. ...

    2023 doi

  11. [19]

    Uniform Trigonometrization Method for Optimal Control Problems with Control and State Constraints,

    Mall, K., Grant, M. J., and Taheri, E., “Uniform Trigonometrization Method for Optimal Control Problems with Control and State Constraints,”Journal of Spacecraft and Rockets, Vol. 57, No. 5, 2020, pp. 995–1007. https://doi.org/10.2514/1.A34624

    2020 doi

  12. [20]

    Trajectoryplanningofafree-flyingrobotbyusingtheoptimalcontrol,

    Sakawa,Y.,“Trajectoryplanningofafree-flyingrobotbyusingtheoptimalcontrol,” OptimalControlApplicationsandMethods , Vol. 20, No. 5, 1999, pp. 235–248. https://doi.org/10.1002/(SICI)1099-1514(199909/10)20:5<235::AID-OCA658>3.0.CO;2-I

    1999 doi

  13. [21]

    Optimal Low Thrust Trajectories to the Moon,

    Betts, J. T., and Erb, S. O., “Optimal Low Thrust Trajectories to the Moon,”SIAM Journal on Applied Dynamical Systems, Vol. 2, No. 2, 2003, pp. 144–170. https://doi.org/10.1137/S1111111102409080

    2003 doi

  14. [22]

    GPOPS-II: A MATLAB Software for Solving Multiple-Phase Optimal Control Problems Using hp-Adaptive Gaussian Quadrature Collocation Methods and Sparse Nonlinear Programming,

    Patterson, M. A., and Rao, A. V., “GPOPS-II: A MATLAB Software for Solving Multiple-Phase Optimal Control Problems Using hp-Adaptive Gaussian Quadrature Collocation Methods and Sparse Nonlinear Programming,”ACM Transactions on Mathematical Software, Vol. 41, No. 1, 2014, pp. 1...

    2014 doi

  15. [23]

    Real-Time Guidance for Low-Thrust Transfers Using Deep Neural Networks,

    Izzo, D., and Öztürk, E., “Real-Time Guidance for Low-Thrust Transfers Using Deep Neural Networks,”Journal of Guidance, Control, and Dynamics, Vol. 44, No. 2, 2021, pp. 315–327. https://doi.org/10.2514/1.G005254. 38

    2021 doi

  16. [24]

    Efficient Low-Thrust Trajectory Data Generation Method Based on the Switch Function,

    Zhang, Y., Wen, C., and Qiao, D., “Efficient Low-Thrust Trajectory Data Generation Method Based on the Switch Function,” Journal of Guidance, Control, and Dynamics, 2024, pp. 1–12. https://doi.org/10.2514/1.G008305

    2024 doi

  17. [25]

    Theoretical Analysis of Fuel-Optimal Powered Descent Problem with State Constraints,

    You, S., Dai, R., and Rea, J. R., “Theoretical Analysis of Fuel-Optimal Powered Descent Problem with State Constraints,” Journal of Guidance, Control, and Dynamics, Vol. 45, No. 12, 2022, pp. 2350–2359. https://doi.org/10.2514/1.G006815

    2022 doi

  18. [26]

    Optimal Abort Guidance and Experimental Verification Based on Feature Learning,

    Kenny, V., You, S., Pei, C., Hendrix, G., Gul, R., Dai, R., and Rea, J., “Optimal Abort Guidance and Experimental Verification Based on Feature Learning,”Journal of Aerospace Engineering, Vol. 37, No. 2, 2024, p. 04023124. https: //doi.org/10.1061/JAEEEZ.ASENG-5030

    2024 doi

  19. [27]

    Propellant-Optimal Powered Descent Guidance,

    Lu, P., “Propellant-Optimal Powered Descent Guidance,”Journal of Guidance, Control, and Dynamics, Vol. 41, No. 4, 2018, pp. 813–826. https://doi.org/10.2514/1.G003243

    2018 doi

  20. [28]

    Propellant-OptimalPoweredDescentGuidanceRevisited,

    Lu,P.,andCallan,R.,“Propellant-OptimalPoweredDescentGuidanceRevisited,” JournalofGuidance,Control,andDynamics , Vol. 46, No. 2, 2023, pp. 215–230. https://doi.org/10.2514/1.G007214

    2023 doi

  21. [29]

    Structure of optimal control for planetary landing with control and state constraints,

    Leparoux, C., Hérissé, B., and Jean, F., “Structure of optimal control for planetary landing with control and state constraints,” ESAIM: Control, Optimisation and Calculus of Variations, Vol. 28, 2022, p. 67. https://doi.org/10.1051/cocv/2022065

    2022

  22. [30]

    Direct–Indirect Hybrid Strategy for Optimal Powered Descent and Landing,

    Spada, F., Sagliano, M., and Topputo, F., “Direct–Indirect Hybrid Strategy for Optimal Powered Descent and Landing,”Journal of Spacecraft and Rockets, 2023, pp. 1–18. https://doi.org/10.2514/1.A35650

    2023 doi

  23. [31]

    Generic Smoothing for Optimal Bang-Off-Bang Spacecraft Maneuvers,

    Taheri, E., and Junkins, J. L., “Generic Smoothing for Optimal Bang-Off-Bang Spacecraft Maneuvers,”Journal of Guidance, Control, and Dynamics, Vol. 41, No. 11, 2018, pp. 2470–2475. https://doi.org/10.2514/1.G003604

    2018 doi

  24. [32]

    Generalized Vectorized Trigonometric Regularization for Solving Optimal Control Problems with Complex Solution Structures,

    Kovryzhenko, Y., Nurre, N. P., and Taheri, E., “Generalized Vectorized Trigonometric Regularization for Solving Optimal Control Problems with Complex Solution Structures,”AIAA SCITECH 2024 Forum, American Institute of Aeronautics and Astronautics, Orlando, FL, 2024, p. 19. htt...

    2024 doi

  25. [33]

    A Survey of the Maximum Principles for Optimal Control Problems with State Constraints,

    Hartl, R. F., Sethi, S. P., and Vickson, R. G., “A Survey of the Maximum Principles for Optimal Control Problems with State Constraints,”SIAM Review, Vol. 37, No. 2, 1995, pp. 181–218. https://doi.org/10.1137/1037043

    1995 doi

  26. [34]

    The Switch Point Algorithm,

    Aghaee, M., and Hager, W. W., “The Switch Point Algorithm,”SIAM Journal on Control and Optimization, Vol. 59, No. 4, 2021, pp. 2570–2593. https://doi.org/10.1137/21M1393315

    2021 doi

  27. [35]

    An Indirect Method for Inequality Constrained Optimal Control Problems,

    Wang, Z., and Li, Y., “An Indirect Method for Inequality Constrained Optimal Control Problems,”IFAC-PapersOnLine, Vol. 50, No. 1, 2017, pp. 4070–4075. https://doi.org/10.1016/j.ifacol.2017.08.790

    2017 doi

  28. [36]

    Path Constraint Regularization in Optimal Control Problems using Saturation Functions,

    Antony, T., and Grant, M. J., “Path Constraint Regularization in Optimal Control Problems using Saturation Functions,”2018 AIAA Atmospheric Flight Mechanics Conference, American Institute of Aeronautics and Astronautics, Kissimmee, Florida, 2018, p. 10. https://doi.org/10.2514...

    2018 doi

  29. [37]

    Fuel Optimization for Continuous-Thrust Orbital Rendezvous with Collision Avoidance Constraint,

    Epenoy, R., “Fuel Optimization for Continuous-Thrust Orbital Rendezvous with Collision Avoidance Constraint,”Journal of Guidance, Control, and Dynamics, Vol. 34, No. 2, 2011, pp. 493–503. https://doi.org/10.2514/1.50996. 39

    2011 doi

  30. [38]

    Fuel optimization for low-thrust Earth–Moon transfer via indirect optimal control,

    Pérez-Palau, D., and Epenoy, R., “Fuel optimization for low-thrust Earth–Moon transfer via indirect optimal control,”Celestial Mechanics and Dynamical Astronomy, Vol. 130, No. 2, 2018, p. 21. https://doi.org/10.1007/s10569-017-9808-2

    2018 doi

  31. [39]

    Costate mapping for indirect trajectory optimization,

    Taheri, E., Arya, V., and Junkins, J. L., “Costate mapping for indirect trajectory optimization,”Astrodynamics, Vol. 5, No. 4, 2021, pp. 359–371. https://doi.org/10.1007/s42064-021-0114-0

    2021 doi

  32. [40]

    Enhancements to the DIDO Optimal Control Toolbox,

    Ross, I. M., “Enhancements to the DIDO Optimal Control Toolbox,” , Jun. 2020. ArXiv:2004.13112 [math]

    2020 arXiv

  33. [41]

    E., and Ho, Y.-C.,Applied optimal control: optimization, estimation, and control, rev

    Bryson, A. E., and Ho, Y.-C.,Applied optimal control: optimization, estimation, and control, rev. print ed., Cambridge University Press, Cambridge, 1975

    1975

  34. [42]

    Aninteriorpenaltymethodforoptimalcontrolproblemswithstateandinputconstraintsof nonlinearsystems,

    Malisani,P.,Chaplais,F.,andPetit,N.,“Aninteriorpenaltymethodforoptimalcontrolproblemswithstateandinputconstraintsof nonlinearsystems,” OptimalControlApplicationsandMethods ,Vol.37,No.1,2016,pp.3–33. https://doi.org/10.1002/oca.2134

    2016 doi

  35. [43]

    CasADi: asoftwareframeworkfornonlinearoptimization andoptimalcontrol,

    Andersson,J.A.E.,Gillis,J.,Horn,G.,Rawlings,J.B.,andDiehl,M.,“CasADi: asoftwareframeworkfornonlinearoptimization andoptimalcontrol,” MathematicalProgrammingComputation ,Vol.11,No.1,2019,pp.1–36. https://doi.org/10.1007/s12532- 018-0139-4

    2019 doi

  36. [44]

    End-to-End Operationally-Constrained Low-Thrust Transfers to Gateway’s Near-Rectilinear Halo Orbit,

    Nurre, N. P., and Taheri, E., “End-to-End Operationally-Constrained Low-Thrust Transfers to Gateway’s Near-Rectilinear Halo Orbit,”AIAA SCITECH 2024 Forum, American Institute of Aeronautics and Astronautics, Orlando, FL, 2024, p. 19. https://doi.org/10.2514/6.2024-0838

    2024 doi

  37. [45]

    M.,A primer on Pontryagin’s principle in optimal control, second edition ed., Collegiate Publisher, San Francisco,

    Ross, I. M.,A primer on Pontryagin’s principle in optimal control, second edition ed., Collegiate Publisher, San Francisco,

  38. [46]

    Optimal Programming Problems With Inequality Constraints,

    Bryson, A. E., Denham, W. F., and Dreyfus, S. E., “Optimal Programming Problems With Inequality Constraints,”AIAA Journal, Vol. 1, No. 11, 1963, pp. 2544–2550. https://doi.org/10.2514/3.2107

    1963 doi

  39. [47]

    Primer Vector Theory Applied to Global Low-Thrust Trade Studies,

    Russell, R. P., “Primer Vector Theory Applied to Global Low-Thrust Trade Studies,”Journal of Guidance, Control, and Dynamics, Vol. 30, No. 2, 2007, pp. 460–472. https://doi.org/10.2514/1.22984

    2007 doi

  40. [48]

    G.,Optimal control theory for applications, Mechanical engineering series, Springer, New York, 2003

    Hull, D. G.,Optimal control theory for applications, Mechanical engineering series, Springer, New York, 2003

    2003

  41. [49]

    New smoothing techniques for solving bang-bang optimal control problems - numerical results and statistical interpretation,

    Bertrand, R., and Epenoy, R., “New smoothing techniques for solving bang-bang optimal control problems - numerical results and statistical interpretation,”Optimal Control Applications and Methods, Vol. 23, No. 4, 2002, pp. 171–197. https://doi.org/10.1002/oca.709

    2002 doi

  42. [50]

    Method of continuation for optimization of interplanetary low-thrust trajectories,

    Petukhov, V. G., “Method of continuation for optimization of interplanetary low-thrust trajectories,”Cosmic Research, Vol. 50, No. 3, 2012, pp. 249–261. https://doi.org/10.1134/S0010952512030069

    2012 doi

  43. [51]

    L2 Norm-Based Control Regularization for Solving Optimal Control Problems,

    Taheri, E., and Li, N., “L2 Norm-Based Control Regularization for Solving Optimal Control Problems,”IEEE Access, Vol. 11, 2023, pp. 125959–125971. https://doi.org/10.1109/ACCESS.2023.3331382

    2023

  44. [52]

    Duty-cycle-awarelow-thrusttrajectoryoptimizationusingembeddedhomotopy,

    Nurre,N.P.,andTaheri,E.,“Duty-cycle-awarelow-thrusttrajectoryoptimizationusingembeddedhomotopy,” ActaAstronautica, Vol. 212, 2023, pp. 630–642. https://doi.org/10.1016/j.actaastro.2023.08.022. 40

    2023 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.