Stochastic Optimal Control for Space Autonomy in Challenging Dynamical Environments

Interdisciplinary Areas: Autonomous and Connected Systems, CISLunar (Space science and Engineering)

Project Description

As the frontiers of robotic space exploration expand in our Solar System, the demand for space autonomy has been increasingly surging in order to enable more efficient space exploration with minimal ground contacts. A primary challenge in space autonomy is to ensure its safety and resilience under uncertainty. Such resilience is particularly important when the spacecraft has to operate in challenging dynamical environments, e.g., cislunar space (a region around the Earth-Moon system) and the vicinity of minor celestial bodies. For instance, mission design and operations of spacecraft in cislunar space have to be robust against maneuver execution errors and undesirable events such as engine failures, while dealing with the chaotic, nonlinear nature of the multi-body dynamical environment.

The objective of this project is to develop mathematical and computational frameworks that enable safe, autonomous spacecraft operations under operational constraints and uncertainties. A key of the research is to combine various methodologies from different areas, including, but not limited to, stochastic systems, controls, optimization, and astrodynamics. An emphasis will be placed on the theoretical analysis and development of the control/planning frameworks that advance the state of the art of stochastic optimal control and space autonomy.

Start Date

Summer 2022

Postdoctoral Qualifications

Successful candidates must hold a Ph.D. in Aerospace, Electrical, Mechanical Engineering, or in a related area by the date of the position start. Solid mathematical skills and background in relevant areas such as control, optimization, uncertainty quantification, and/or astrodynamics are preferred. Experience in spacecraft GNC or space mission design will be also a plus.


- Kenshiro Oguri
- Email:
- Affiliation: Assistant Professor of Aeronautics and Astronautics (Jan. 2022-)
- URL:;
- Jianghai Hu
- Email:
- Affiliation: Professor of Electrical and Computer Engineering
- URL: 

External Collaborators

- Gregory Lantoine, Ph.D.
- Affiliation: Mission Design Engineer, Mission Design and Navigation Section, NASA JPL/Caltech
- Contact:


- J. Ridderhof, K. Okamoto, and P. Tsiotras, “Nonlinear Uncertainty Control with Iterative Covariance Steering,” 2019 IEEE 58th Conference on Decision and Control (CDC), 2019, pp. 3484–3490.
- K. Oguri, M. Ono, and J. W. McMahon, "Convex Optimization over Sequential Linear Feedback Policies with Continuous-Time Chance Constraints," 2019 IEEE 58th Conference on Decision and Control (CDC), 2019, pp. 6325–6331.
- N. Ozaki, S. Campagnola, and R. Funase, "Tube Stochastic Optimal Control for Nonlinear Constrained Trajectory Optimization Problems," Journal of Guidance, Control, and Dynamics, Vol. 43, No. 4, 2020, pp. 645–655.
- C. Greco, M. Di Carlo, M. Vasile, and R. Epenoy, "Direct multiple shooting transcription with polynomial algebra for optimal control problems under uncertainty," Acta Astronautica, Vol. 170, No. October 2019, 2020, pp. 224–234.
- K. Oguri, and J. W. McMahon, "Robust Spacecraft Guidance Around Small Bodies Under Uncertainty: Stochastic Optimal Control Approach," Journal of Guidance, Control, and Dynamics, Vol. 44, No. 7, 2021, pp. 1295–1313.