Skip to main content
Log in

Sundman-Transformed Differential Dynamic Programming with Modified Equinoctial Elements

  • Technical Note
  • Published:
The Journal of the Astronautical Sciences Aims and scope Submit manuscript

Abstract

Previous efforts addressed the challenge of low-thrust many-revolution trajectory optimization by applying a Sundman transformation to change the independent variable of the spacecraft equations of motion to an orbit anomaly and performing the optimization with differential dynamic programming (DDP). The approach may be enhanced by representing the spacecraft state with orbital elements rather than position and velocity coordinates. This paper shows how the modified equinoctial element state representation enters the DDP algorithm. Example transfers from geostationary transfer orbit (GTO) to geosynchronous orbit (GEO) demonstrate how gains in computational efficiency are possible with minimal impact to solution quality. Those gains are leveraged to compute the Pareto front of time versus delivered mass for a benchmark orbit transfer from the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

References

  1. Jacobson, D.H., Mayne, D.Q.: Differential Dynamic Programming. American Elsevier Publishing Company, New York (1970)

    MATH  Google Scholar 

  2. Whiffen, G.J.: Static/dynamic control for optimizing a useful objective. United States Patent No. 6496741 (2002)

  3. Whiffen, G.J.: Mystic: implementation of the static dynamic optimal control algorithm for high-fidelity, low-thrust trajectory design. AIAA/AAS astrodynamics specialist conference and exhibit, Keystone, CO, AIAA Paper 2006-6741, https://doi.org/10.2514/6.2006-6741 (2006)

  4. NASA Technology Transfer Program: Mystic low-thrust trajectory design and visualization software. https://software.nasa.gov/software/NPO-43666-1. Accessed October 2016

  5. Lantoine, G., Russell, R.P.: A hybrid differential dynamic programming algorithm for constrained optimal control problems. Part 1: theory. J. Optim. Theory Appl. 154(2), 382–417 (2012). https://doi.org/10.1007/s10957-012-0039-0

    Article  MathSciNet  MATH  Google Scholar 

  6. Lantoine, G., Russell, R.P.: A hybrid differential dynamic programming algorithm for constrained optimal control problems. part 2: application. J. Optim. Theory Appl., 154(2), 418–442 (2012). https://doi.org/10.1007/s10957-012-0038-1

    Article  MathSciNet  MATH  Google Scholar 

  7. Aziz, J.D., Parker, J.S., Scheeres, D.J., Englander, J.A.: Low-thrust many-revolution trajectory optimization via differential dynamic programming and a sundman transformation. J. Astronaut. Sci. 65, 205–228 (2018). https://doi.org/10.1007/s40295-017-0122-8

    Article  Google Scholar 

  8. Sundman, K.: Memoire sur le probleme des trois corps. Acta Math. 36, 105–179 (1913). https://doi.org/10.1007/BF02422379

    Article  MathSciNet  MATH  Google Scholar 

  9. Janin, G., Bond, V.R.: The elliptic anomaly. NASA technical memorandum 58228 (1980)

  10. Aziz, J.D., Parker, J.S., Scheeres, D.J., Englander, J.A.: Low-thrust many-revolution trajectory optimization via differential dynamic programming and a sundman transformation. AAS/AIAA space flight mechanics meeting, San Antonio, TX, AAS Paper 17–253 (2017)

  11. Petropoulos, A.E., Tarzi, Z.B., Lantoine, G., Dargent, T., Epenoy, R.: Techniques for designing Manyrevolution, electric propulsion trajectories. AAS/AIAA space flight mechanics meeting, Santa Fe, NM, AAS Paper 14–373, (2014)

  12. Walker, M.J.H., Ireland, B., Owens, J.: A set of modified equinoctial elements. Celest. Mech. 36, 409–419 (1985)

    Article  Google Scholar 

  13. Betts, J.T.: Optimal low thrust orbit transfers with eclipsing. Optimal Control Appl. Methods 36, 218–240 (2015)

    Article  MathSciNet  Google Scholar 

  14. Prince, P., Dormand, J.: High order embedded Runge-Kutta formulae. J. Comput. Appl. Math. 7(1), 67–75 (1981). https://doi.org/10.1016/0771-050X(81)90010-3

    Article  MathSciNet  MATH  Google Scholar 

  15. Anderson, J., Burns, P.J., Milroy, D., Ruprecht, P., Hauser, T., Siegel, H.J.: Deploying RMACC summit: an HPC resource for the rocky mountain region. PEARC17. https://doi.org/10.1145/3093338.3093379 (2017)

  16. Dagum, L., Menon, R.: OpenMP: an industry-standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5, 46–55 (1998)

    Article  Google Scholar 

  17. Petropoulos, A.E.: Low-thrust orbit transfers using candidate lyapunov functions with a mechanism for coasting. In: AIAA/AAS Astrodynamics Specialist Conference and Exhibit, Providence, RI, AIAA Paper 2004–5089, (2004)

  18. Ruggiero, A., Pergola, P., Marcuccio, S., Andrenucci, M.: Low-thrust maneuvers for the efficient correction of orbital elements. In: 32nd International Electric Propulsion Conference, Wiesbaden, Germany IEPC-2011-102 (2011)

  19. Dargent, T., Martinot, V.: An integrated tool for low thrust optimal control orbit transfers. In: 18Th International Symposium on Space Flight Dynamics, Munich Germany (2004)

  20. Dargent, T.: Averaging technique in T-3D an integrated tool for continuous thrust optimal control in orbit transfers. AAS/AIAA spaceflight mechanics meeting, Santa Fe, NM, AAS Paper 14–312 (2014)

Download references

Acknowledgments

Thank you to Anastassios Petropoulos for sharing Q-law Case E data that was used for comparison. This work was supported by a NASA Space Technology Research Fellowship. This work utilized the RMACC Summit supercomputer, which is supported by the National Science Foundation (awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado State University. The Summit supercomputer is a joint effort of the University of Colorado Boulder and Colorado State University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jonathan D. Aziz.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: The Dynamics Matrix and Tensor for the Augmented Modified Element State Vector

Appendix: The Dynamics Matrix and Tensor for the Augmented Modified Element State Vector

In order to compute the STMs for the augmented MEE state vector, the dynamics matrix and tensor must be obtained for each term in Eq. 14. Derivatives of b are available directly, but the preceding terms require careful application of the chain rule. First, the thrust term is restated.

$$ A\boldsymbol{\Delta}_{\text{Thrust}} =\frac{ A\boldsymbol{u}T_{a}}{m} $$
(27)

The thrust contribution to the dynamics matrix is the first derivative of Eq. 27 with respect to the augmented MEE state vector.

$$ \frac{\partial (A\boldsymbol{u}T_{a}/m)}{\partial\boldsymbol{X}_{MEE}} = \frac{A\boldsymbol{u}}{m}\frac{\partial T_{a}}{\partial \boldsymbol{X}_{MEE}} + \frac{A}{m}\frac{\partial\boldsymbol{u}}{\partial\boldsymbol{X}_{MEE}}T_{a} + A\frac{\partial}{\partial \boldsymbol{X}_{MEE}}\left( \frac{1}{m}\right)\boldsymbol{u}T_{a} + \frac{\partial A}{\partial \boldsymbol{X}_{MEE}}\frac{\boldsymbol{u}T_{a}}{m} $$
(28)

Power models frequently use the Cartesian inertial state, so it is convenient to obtain the derivative of the thrust available with respect to the XIJK and then make the transformation to the XMEE sensitivity.

$$ \begin{array}{@{}rcl@{}} \frac{\partial T_{a}}{\partial \boldsymbol{X}_{MEE}} &=& \frac{\partial T_{a}}{\partial \boldsymbol{X}_{IJK}}\frac{\partial \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}_{MEE}} \end{array} $$
(29a)
$$ \begin{array}{@{}rcl@{}} {\left( \frac{\partial^{2} T_{a}}{\partial \boldsymbol{X}^{2}_{MEE}}\right)}^{i,a} &=& \left( \frac{\partial T_{a}}{\partial \boldsymbol{X}_{IJK}}\right)^{\gamma_{1}}\left( \frac{\partial^{2} \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}^{2}_{MEE}}\right)^{\gamma_{1},ia}\\ &&+ \left( \frac{\partial^{2} T_{a}}{\partial \boldsymbol{X}^{2}_{IJK}}\right)^{\gamma_{1},\gamma_{2}}\left( \frac{\partial \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}_{MEE}}\right)^{\gamma_{1},i}\left( \frac{\partial \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}_{MEE}}\right)^{\gamma_{2},a} \end{array} $$
(29b)

Tensor notation has been introduced with the convention that superscripts are indices, and repeated indices of γ are summed over.

With the second derivative of the thrust available now defined with respect to the XMEE, the dynamics tensor contributions from thrust can be obtained.

$$ \begin{array}{@{}rcl@{}} \frac{\partial^{2} (A\boldsymbol{u}T_{a}/m)}{\partial\boldsymbol{X}^{2}_{MEE}} &=& t_{11} + t_{12} + t_{13} + t_{14}\\ &&+ t_{21} + t_{22} + t_{23} + t_{24}\\ &&+ t_{31} + t_{32} + t_{33} + t_{34}\\ &&+ t_{41} + t_{42} + t_{43} + t_{44} \end{array} $$
(30a)
$$ \begin{array}{@{}rcl@{}} t_{11}^{i,ab} &=& \left( \frac{A}{m}\right)^{i,\gamma_{1}}\boldsymbol{u}^{\gamma_{1}}\left( \frac{\partial^{2} T_{a}}{\partial \boldsymbol{X}^{2}_{MEE}}\right)^{a,b} \end{array} $$
(30b)
$$ \begin{array}{@{}rcl@{}} t_{12}^{i,ab} &=& \left( \frac{A}{m}\right)^{i,\gamma_{1}}\left( \frac{\partial\boldsymbol{u}}{\partial\boldsymbol{X}_{MEE}}\right)^{\gamma_{1},a}\left( \frac{\partial T_{a}}{\partial \boldsymbol{X}_{MEE}}\right)^{b} \end{array} $$
(30c)
$$ \begin{array}{@{}rcl@{}} t_{13}^{i,ab} &=& A^{i,\gamma_{1}}\boldsymbol{u}^{\gamma_{1}}\left[\frac{\partial}{\partial\boldsymbol{X}_{MEE}}\left( \frac{1}{m}\right)\right]^{a}\left( \frac{\partial T_{a}}{\partial\boldsymbol{X}_{MEE}}\right)^{b} \end{array} $$
(30d)
$$ \begin{array}{@{}rcl@{}} t_{14}^{i,ab} &=& \left( \frac{\partial A}{\partial \boldsymbol{X}_{MEE}}\right)^{i,\gamma_{1}a}\left( \frac{\boldsymbol{u}}{m}\right)^{\gamma_{1}}\left( \frac{\partial T_{a}}{\partial\boldsymbol{X}_{MEE}}\right)^{b} \end{array} $$
(30e)
$$ \begin{array}{@{}rcl@{}} t_{22}^{i,ab} &=& \left( \frac{A}{m}\right)^{i,\gamma_{1}}\left( \frac{\partial^{2}\boldsymbol{u}}{\partial\boldsymbol{X}^{2}_{MEE}}\right)^{\gamma_{1},ab}T_{a} \end{array} $$
(30f)
$$ \begin{array}{@{}rcl@{}} t_{23}^{i,ab} &=& A^{i,\gamma_{1}}\left[\frac{\partial}{\partial\boldsymbol{X}_{MEE}}\left( \frac{1}{m}\right)\right]^{a}\left( \frac{\partial\boldsymbol{u}}{\partial\boldsymbol{X}_{MEE}}\right)^{\gamma_{1},b}T_{a} \end{array} $$
(30g)
$$ \begin{array}{@{}rcl@{}} t_{24}^{i,ab} &=& \left( \frac{\partial A}{\partial \boldsymbol{X}_{MEE}}\right)^{i,\gamma_{1}a}\left( \frac{\partial\boldsymbol{u}}{\partial\boldsymbol{X}_{MEE}}\right)^{\gamma_{1},b}\left( \frac{T_{a}}{m}\right) \end{array} $$
(30h)
$$ \begin{array}{@{}rcl@{}} t_{33}^{i,ab} &=& A^{i,\gamma_{1}}\boldsymbol{u}^{\gamma_{1}}\left[\frac{\partial^{2}}{\partial\boldsymbol{X}^{2}_{MEE}}\left( \frac{1}{m}\right)\right]^{a,b}T_{a} \end{array} $$
(30i)
$$ \begin{array}{@{}rcl@{}} t_{34}^{i,ab} &=& \left( \frac{\partial A}{\partial \boldsymbol{X}_{MEE}}\right)^{i,\gamma_{1}a}\boldsymbol{u}^{\gamma_{1}}\left[\frac{\partial}{\partial\boldsymbol{X}_{MEE}}\left( \frac{1}{m}\right)\right]^{b}T_{a} \end{array} $$
(30j)
$$ \begin{array}{@{}rcl@{}} t_{44}^{i,ab} &=& \left( \frac{\partial^{2} A}{\partial \boldsymbol{X}^{2}_{MEE}}\right)^{i,\gamma_{1}ab}\boldsymbol{u}^{\gamma_{1}}\left( \frac{T_{a}}{m}\right) \end{array} $$
(30k)
$$ \begin{array}{@{}rcl@{}} t_{21}^{i,ab} &=& t_{12}^{i,ba}, \quad t_{31}^{i,ab} = t_{13}^{i,ba}, \quad t_{32}^{i,ab} = t_{23}^{i,ba} \\ t_{41}^{i,ab} &=& t_{14}^{i,ba}, \quad t_{42}^{i,ab} = t_{24}^{i,ba}, \quad t_{43}^{i,ab} = t_{34}^{i,ba} \end{array} $$
(30l)

Proceeding to the mass flow rate term,

$$ \frac{\partial (\dot{\boldsymbol{m}}T_a)}{\partial\boldsymbol{X}_{MEE}} = \dot{\boldsymbol{m}}\frac{\partial T_a}{\partial \boldsymbol{X}_{MEE}} + \frac{\partial \dot{\boldsymbol{m}}}{\partial \boldsymbol{X}_{MEE}}T_, $$
(31a)
$$ \begin{array}{@{}rcl@{}} \left( \frac{\partial^{2} (\dot{{\boldsymbol{m}}}T_{a})}{\partial{X}^{2}_{MEE}}\right)^{i,ab} &=& \dot{{\boldsymbol{m}}}^i\left( \frac{\partial^{2}T_{a}}{\partial{X}^{2}_{MEE}}\right)^{a,b} + \left( \frac{\partial \dot{{\boldsymbol{m}}}}{\partial \boldsymbol{X}_{MEE}}\right)^{i,a}\left( \frac{\partial T_a}{\partial \boldsymbol{X}_{MEE}}\right)^{b}\\ &&+ \left( \frac{\partial \dot{{\boldsymbol{m}}}}{\partial \boldsymbol{X}_{MEE}}\right)^{i,b}\left( \frac{\partial T_a}{\partial \boldsymbol{X}_{MEE}}\right)^{a}, \end{array} $$
(31b)

where the second derivatives of \(\dot {\boldsymbol {m}}\) are zero and ignored in presentation.

The effects of perturbations are included by considering the summation of their first and second derivatives in an inertial frame.

$$ \begin{array}{@{}rcl@{}} \frac{\partial \boldsymbol{\delta}_{p}}{\partial \boldsymbol{X}_{IJK}} &=& \sum\limits_{i=0}^{n_{p}}\frac{\partial \boldsymbol{\delta}_{p_{i}}}{\partial \boldsymbol{X}_{IJK}} \end{array} $$
(32a)
$$ \begin{array}{@{}rcl@{}} \frac{\partial^{2} \boldsymbol{\delta}_{p}}{\partial \boldsymbol{X}^{2}_{IJK}} &=& \sum\limits_{i=0}^{n_{p}}\frac{\partial^{2} \boldsymbol{\delta}_{p_{i}}}{\partial \boldsymbol{X}^{2}_{IJK}} \end{array} $$
(32b)

As previously obtained for the thrust available, the perturbation sensitivities must be found with respect to the augmented MEE state vector.

$$ \begin{array}{@{}rcl@{}} \frac{\partial \boldsymbol{\delta}_{p}}{\partial \boldsymbol{X}_{MEE}} &=& \frac{\partial \boldsymbol{\delta}_{p}}{\partial \boldsymbol{X}_{IJK}}\frac{\partial \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}_{MEE}} \end{array} $$
(33a)
$$ \begin{array}{@{}rcl@{}} \left( \frac{\partial^{2} \boldsymbol{\delta}_{p}}{\partial \boldsymbol{X}^{2}_{MEE}}\right)^{i,ab} &=& \left( \frac{\partial \boldsymbol{\delta}_{p}}{\partial \boldsymbol{X}_{IJK}}\right)^{i,\gamma_{1}}\left( \frac{\partial^{2} \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}^{2}_{MEE}}\right)^{\gamma_{1},ab} \\&&+ \left( \frac{\partial^{2} \boldsymbol{\delta}_{p}}{\partial \boldsymbol{X}^{2}_{IJK}}\right)^{i,\gamma_{1}\gamma_{2}}\left( \frac{\partial \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}_{MEE}}\right)^{\gamma_{1},a}\left( \frac{\partial \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}_{MEE}}\right)^{\gamma_{2},b} \end{array} $$
(33b)

Derivatives of the rotation matrix QT are also required. Those too are first found inertially.

$$ \begin{array}{@{}rcl@{}} \left( \frac{\partial Q^{T}}{\partial \boldsymbol{X}_{IJK}}\right)^{i,ab} &= \frac{\partial (Q^{T})^{i,a}}{\partial \boldsymbol{X}^{b}_{IJK}} \end{array} $$
(34a)
$$ \begin{array}{@{}rcl@{}} \left( \frac{\partial^{2} Q^{T}}{\partial \boldsymbol{X}^{2}_{IJK}}\right)^{i,abc} &= \frac{\partial (Q^{T})^{i,a}}{\partial \boldsymbol{X}^{b}_{IJK}\partial \boldsymbol{X}^{c}_{IJK}} \end{array} $$
(34b)

Next, the derivatives of QT are found with respect to the augmented MEE state vector.

$$ \begin{array}{@{}rcl@{}} \left( \frac{\partial Q^{T}}{\partial \boldsymbol{X}_{MEE}}\right)^{i,ab} &=& \left( \frac{\partial Q^{T}}{\partial \boldsymbol{X}_{IJK}}\right)^{i,a\gamma_{1}}\left( \frac{\partial \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}_{MEE}}\right)^{\gamma_{1},b} \end{array} $$
(35a)
$$ \begin{array}{@{}rcl@{}} \left( \frac{\partial^{2} Q^{T}}{\partial \boldsymbol{X}^{2}_{MEE}}\right)^{i,abc} &=& \left( \frac{\partial Q^{T}}{\partial \boldsymbol{X}_{IJK}}\right)^{i,a\gamma_{1}}\left( \frac{\partial^{2} \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}^{2}_{MEE}}\right)^{\gamma_{1},bc}\\ &&+ \left( \frac{\partial^{2} Q^{T}}{\partial \boldsymbol{X}^{2}_{IJK}}\right)^{i,a\gamma_{1}\gamma_{2}}\left( \frac{\partial \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}_{MEE}}\right)^{\gamma_{1},b}\left( \frac{\partial \boldsymbol{X}_{IJK}}{\partial \boldsymbol{X}_{MEE}}\right)^{\gamma_{2},c} \end{array} $$
(35b)

The necessary terms have been defined to assemble the dynamics matrix and tensor contributions from perturbations.

$$ \begin{array}{@{}rcl@{}} \left( \frac{\partial (AQ^{T}\boldsymbol{\delta}_{p})}{\partial\boldsymbol{X}_{MEE}}\right)^{i,a} &=& \left( AQ^{T}\frac{\partial \boldsymbol{\delta}_{p}}{\partial \boldsymbol{X}_{MEE}}\right)^{i,a}\\&&+ A^{i,\gamma_{1}}\left( \frac{\partial Q^{T}}{\!\partial\boldsymbol{X}_{MEE}\!}\right)^{\gamma_{1},\gamma_{2}a}\boldsymbol{\delta}_{p}^{\gamma_{2}} + \left( \frac{\partial A}{\!\partial \boldsymbol{X}_{MEE}}\!\right)^{i,\gamma_{1}a}(Q^{T}\boldsymbol{\delta}_{p})^{\gamma_{1}} \end{array} $$
(36)
$$ \begin{array}{@{}rcl@{}} \frac{\partial^{2} (AQ^{T}\boldsymbol{\delta}_{p})}{\partial\boldsymbol{X}^{2}_{MEE}} &=& t_{11} + t_{12} + t_{13}\\ &&+ t_{21} + t_{22} + t_{23}\\ &&+ t_{31} + t_{32} + t_{33} \end{array} $$
(37a)
$$ \begin{array}{@{}rcl@{}} t_{11}^{i,ab} &=&A^{i,\gamma_{1}}(Q^{T})^{\gamma_{1},\gamma_{2}}\left( \frac{\partial^{2}\boldsymbol{\delta}_{p}}{\boldsymbol{X}^{2}_{MEE}}\right)^{\gamma_{2},ab} \end{array} $$
(37b)
$$ \begin{array}{@{}rcl@{}} t_{12}^{i,ab} &=& A^{i,\gamma_{1}}\left( \frac{\partial Q^{T}}{\partial\boldsymbol{X}_{MEE}}\right)^{\gamma_{1},\gamma_{2}a}\left( \frac{\partial \boldsymbol{\delta}_{p}}{\partial \boldsymbol{X}_{MEE}}\right)^{\gamma_{2},b} \end{array} $$
(37c)
$$ \begin{array}{@{}rcl@{}} t_{13}^{i,ab} &=& \left( \frac{\partial A}{\partial \boldsymbol{X}_{MEE}}\right)^{i,\gamma_{1}a}(Q^{T})^{\gamma_{1},\gamma_{2}}\left( \frac{\partial \boldsymbol{\delta}_{p}}{\partial \boldsymbol{X}_{MEE}}\right)^{\gamma_{2},b} \end{array} $$
(37d)
$$ \begin{array}{@{}rcl@{}} t_{22}^{i,ab} &=& A^{i,\gamma_{1}}\left( \frac{\partial^{2} Q^{T}}{\partial \boldsymbol{X}^{2}_{MEE}}\right)^{\gamma_{1},\gamma_{2}ab} \boldsymbol{\delta}_{p}^{\gamma_{2}} \end{array} $$
(37e)
$$ \begin{array}{@{}rcl@{}} t_{23}^{i,ab} &=& \left( \frac{\partial A}{\partial \boldsymbol{X}_{MEE}}\right)^{i,\gamma_{1}a}\left( \frac{\partial Q^{T}}{\partial\boldsymbol{X}_{MEE}}\right)^{\gamma_{1},\gamma_{2}b}\boldsymbol{\delta}_{p}^{\gamma_{2}} \end{array} $$
(37f)
$$ \begin{array}{@{}rcl@{}} t_{33}^{i,ab} &=& \left( \frac{\partial^{2} A}{\partial \boldsymbol{X}^{2}_{MEE}}\right)^{i,\gamma_{1}ab}(Q^{T})^{\gamma_{1},\gamma_{2}}\boldsymbol{\delta}^{\gamma_{2}} \end{array} $$
(37g)
$$ \begin{array}{@{}rcl@{}} t_{21}^{i,ab} &=& t_{12}^{i,ba}, \quad t_{31}^{i,ab} = t_{13}^{i,ba}, \quad t_{32}^{i,ab} = t_{23}^{i,ba} \end{array} $$
(37h)

The complete assembly of the dynamics matrix is the summation of Eqs. 2831a and 33a and derivatives of b.

$$ \frac{\partial\dot{\boldsymbol{X}}_{MEE}}{\partial\boldsymbol{X}_{MEE}} = \frac{\partial (A\boldsymbol{u}T_{a}/m)}{\partial\boldsymbol{X}_{MEE}} + \frac{\partial (\dot{\boldsymbol{m}}T_{a})}{\partial\boldsymbol{X}_{MEE}} + \frac{\partial (AQ^{T}\boldsymbol{\delta}_{p})}{\partial\boldsymbol{X}_{MEE}} + \frac{\partial \boldsymbol{b}}{\partial \boldsymbol{X}_{MEE}} $$
(38)

Finally, the complete assembly of the dynamics tensor is the summation of Eqs. 30a31b and 33b and second derivatives of b.

$$ \frac{\partial^{2}\dot{\boldsymbol{X}}_{MEE}}{\partial\boldsymbol{X}^{2}_{MEE}} = \frac{\partial^{2} (A\boldsymbol{u}T_{a}/m)}{\partial\boldsymbol{X}^{2}_{MEE}} + \frac{\partial^{2} (\dot{\boldsymbol{m}}T_{a})}{\partial\boldsymbol{X}^{2}_{MEE}} + \frac{\partial^{2} (AQ^{T}\boldsymbol{\delta}_{p})}{\partial\boldsymbol{X}^{2}_{MEE}} + \frac{\partial^{2} \boldsymbol{b}}{\partial \boldsymbol{X}^{2}_{MEE}} $$
(39)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aziz, J.D., Scheeres, D.J. Sundman-Transformed Differential Dynamic Programming with Modified Equinoctial Elements. J Astronaut Sci 66, 419–445 (2019). https://doi.org/10.1007/s40295-019-00173-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40295-019-00173-4

Keywords

Navigation