Spacecraft employing solar-electric propulsion generates low magnitudes of thrust, limited by the amount of power generated by the solar panels onboard the spacecraft. Computation of optimal low-thrust geocentric orbit-raising trajectories requires the solution of a nonlinear, non-convex, multi-phase optimal control problem that is challenging to solve. In addition, orbit-raising in cislunar space has added complexity owing to the effect of strong lunar gravitational forces. 

KEY CONTRIBUTIONS 

  • Developed h-e orbital elements for describing the dynamics of a spacecraft. 
  • Developed a sequential algorithm for computing sub-optimal low-thrust trajectories in a fast, robust and automated manner. 
  • Demonstrated that a traditional direct optimization framework using adjoint sensitivities can compute good quality orbit-raising trajectories using the sequential solution as an initial guess. 
  • Developed a cascaded deep reinforcement learning framework to compute low-thrust orbit-raising trajectories. 

SELECTED PUBLICATIONS 

Arustei, A., Dutta, A (2024). Direct Optimization of Low-Thrust Orbit-Raising Maneuvers using Adjoint Sensitivities,. Acta Astronautica. Vol. 219, pp. 965-981.    

Zaidi, A., Chadalavada, P., Ullah, H., Munir, A., and Dutta, A (2023). Cascaded Deep Reinforcement
Learning-Based Multi-Revolution Low-Thrust Spacecraft Orbit-Transfer. IEEE Access,
vol. 11, pp. 82894-82911, .  

S. Sreesawet, A. Dutta (2018). 鈥淔ast and Robust Computation of Low-Thrust Orbit-Raising Trajectories,鈥� AIAA Journal of Guidance, Control and Dynamics, Vol 41, No 9, pp. 1888-1905.   

NASA EPSCOR CAN PROJECT 

(2020-2025)  

Project Team

Science-PI:

    Atri Dutta, Aerospace Engineering, 黑洞社区 (黑洞社区) 

Co-Investigators:

   James Steck, Aerospace Engineering, 黑洞社区 

   , Aerospace Engineering, University of Kansas (KU)  

   , Computer Science, Kansas State University (KSU)

NASA Technical Monitor: 

   , Chief Technologist, NASA Marshall Space Flight Center 

Industry Collaborator: 

   Pradipto Ghosh, Senior Mission Design and Navigation Engineer, JHU Applied Physics Laboratory  

STEM Partner: 

   William Polite, Director of Equity, Diversity and Accountability, Wichita Public School 

Students (directly funded by project): 

   Amrutha Dasyam, PhD Student, Aerospace Engineering, 黑洞社区 (2022-Current)

   Adrian Arustei, PhD Student, Aerospace Engineering, 黑洞社区 (2022-2023) 

   Kyle Messick, PhD Student, Aerospace Engineering, 黑洞社区 (2022)  

   Yrithu, PhD Student, Aerospace Engineering, 黑洞社区 (2020-2023) 

   Matthew Chace, MS Student, Aerospace Engineering, 黑洞社区 (2022-2024) 

   Ella Kreger, UG Student, Aerospace Engineering (2024)  

   Melvin Rafi, PhD Student, Aerospace Engineering (2020) 

   Syed Talha Zaidi, PhD Student, Computer Science, KSU (2022-2024)  

   Ali H. Mughal, MS Student, Computer Science, KSU (2021-2022)   

   Mahmood Azhar Qureshi, Computer Science, KSU (2021)  

   Hayat Ullah, PhD Student, Computer Science, KSU (2022)  

   Charles A. Fry, MS Student, Aerospace Engineering, KU (2021-2023)     

 Other students engaged for research experience: 

   Pardhasai Chadalavada 

   Tanzimul Farabi 

   Ramses Young   

Project  Publications

Arustei, A., Dutta, A (2024). Direct Optimization of Low-Thrust Orbit-Raising Maneuvers using Adjoint Sensitivities,. Acta Astronautica. Vol. 219, pp. 965-981.    

Pillay, Y., Chace, M., Steck, J., Watkins, J., and Dutta, A (2024). Neuro-adaptive Model Reference Tracking Controller for Cislunar Missions. AIAA Guidance Navigation and Control Conference,
AIAA SciTech Forum, Orlando FL. AIAA 2024-0509.  

Dutta, A., Arustei, A., Chace, M., Chadalavada, P., Steck,  J., Zaidi, T. & Munir, A (2024). Machine
Learning Assisted Low-Thrust Orbit-Raising: A Comparative Assessment of a Sequential Al-
gorithm and a Deep Reinforcement Learning Approach (2024).  AAS/AIAA Space Flight Mechanics Meeting, AIAA SciTech Forum. Orlando FL. AIAA 2024-1669.  

Zaidi, A., Chadalavada, P., Ullah, H., Munir, A., and Dutta, A (2023). Cascaded Deep Reinforcement
Learning-Based Multi-Revolution Low-Thrust Spacecraft Orbit-Transfer. IEEE Access,
vol. 11, pp. 82894-82911, .  

Fry, C. A (2023). An Exploration of Solar Radio Flux Forecasting Using Long Short-Term Memory Artificial Neural Networks. MS Thesis, University of Kansas.

Dasyam, A., Dutta, A (2023). Artificial Neural Network based Atmospheric Density Model for Aerobraking Trajectory Design. AAS/AIAA Space Flight Mechanics Meeting. Austin TX.

Mughal, A., Chadalavada, P., Munir, A., Dutta, A., & Qureshi, M (2022). Design of deep neural networks for transfer time prediction of spacecraft electric orbit-raising. Elsevier Intelligent Systems with Application. Vol. 15, Article No 200092.

Pillay, Y., Chace, M., Steck, J., & Dutta, A (2022). Neural network for predicting unmodelled dynamics in multi-revolution transfers in cis-lunar missions. AAS/AIAA Astrodynamics Specialist Meeting. Charlotte, NC.

Arustei, A., Dutta (2022), A. An adjoint sensitivity method for the sequential low-thrust orbit-raising problem. AAS/AIAA Astrodynamics Specialist Conference. Charlotte NC.

Fry, C., McLaughlin, C (2022). Optimizing Long Short-Term Memory Neural Network to Forecast Solar Radio Flux. AAS/AIAA Astrodynamics Specialist Conference. Charlotte NC.  

Dasyam, A., Chadalavada, P., Fry, C., Dutta, A., & McLaughlin, C (2021). Neural Network Based Estimation of Atmospheric Density during Aerobraking. AAS/AIAA Astrodynamics Specialist Meeting. Held virtually.

Pillay, Y., Chace, M., Messick, K., Steck, J., & Dutta, A (2021). Modified State Observer for Characterization of Unmodeled Dynamics in Cis-lunar Missions. AAS/AIAA Astrodynamics Specialist Meeting. Held virtually.

Chadalavada, P., Dutta, A., & Ghosh, P (2021). An Efficient Algorithm for the Longitude-Targeted Ascent of All-Electric Satellites. AAS/AIAA Space Flight Mechanics Meeting (AIAA Scitech Forum). San Diego CA.

Farabi, T., & Dutta, A. (2021). Artificial Neural Network Based Prediction of Solar Array Degradation during Electric Orbit-Raising. AAS/AIAA Space Flight Mechanics Meeting. Virtual Conference. AAS 21-424.

Outreach

Astrodynamics Workshop for High-School Students. 2024. 

Astronautics Workshop for High-School Teachers, 2024. 

Lessons Learned with NASA Innovation and Technology Development. Public tak by NASA Chief Technologist John Dankanich, at 黑洞社区. January 13, 2022.  

Astronautics Summer Camp, 2022. 

FUNDING ACKNOWLEDGMENT 

This research has been sponsored by:

  • Kansas NASA EPSCOR Program grant.  
  • NASA EPSCOR CAN program grant number 80NSSC20M0217 (2020-2025).