Phd Proposal by Jack Ridderhof

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  • Date/Time:
    • Friday July 31, 2020 - Saturday August 1, 2020
      1:00 pm - 2:59 pm
  • Location: REMOTE
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Summary Sentence: Applied Stochastic Optimal Control for Atmospheric Entry, Descent, and Landing

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Jack Ridderhof
(Advisor: Prof. Panagiotis Tsiotras)

will propose a doctoral thesis entitled,

Applied Stochastic Optimal Control for Atmospheric Entry, Descent, and Landing


Friday, July 31 at 1:00 p.m.

Entry, descent, and landing (EDL) refers to the process during which a spacecraft enters the atmosphere of a planet, descends through the atmosphere, and then safely lands on the surface of the planet. Due to the large degree of uncertainty involved, performance of an EDL system is measured in terms of probabilities. The landing accuracy, for example, is measured by the size of the ellipse containing 99.97% of the possible landing sites. Other performance metrics, such as the conditions at parachute deployment, the maximum loading, or the velocity at touchdown can all only be described before flight as random variables. Consequently, extensive Monte Carlo simulations are used throughout the EDL design process, and design changes are evaluated with respect to the resulting probability distributions of performance metrics.

While managing uncertainty is a fundamental problem in EDL, the guidance schemes employed for EDL are not derived explicitly in response to uncertainty. This approach reflects the general methodology to mitigating uncertainty in control systems engineering: controls are parameterized into state-feedback form to implicitly decrease the sensitivity of performance to uncertainties in the system.

The proposed work, in contrast, considers uncertainty in the system model for the purposes of guidance design. Then the system state, and by extension, the closed-loop controls, are described as random variables, and the selection of the feedback law has the effect of shaping the evolution of the state probability density. In particular, methods and principles from stochastic optimal control are applied to powered descent guidance (PDG) and to entry guidance for Martian EDL, and, in addition, the proposed entry guidance methodology is extended for application to aerocapture guidance. For each of these proposed guidance schemes, the feedback controls are determined as a function of the problem uncertainties with the condition that the closed-loop system satisfies probabilistic constraints, such as a maximum covariance of the landing position or a maximum probability that the feedback controls exceed their pre-assigned limits. By explicitly modeling the uncertainty at the stage of guidance design, the proposed methods aim to improve EDL system performance in the presence of uncertainty.


  • Prof. Panagiotis Tsiotras – School of Aerospace Engineering (advisor)
  • Dr. Soumyo Dutta – NASA Langley Research Center
  • Prof. Glenn Lightsey – School of Aerospace Engineering
  • Prof. Bechet Acikmese – Department of Aeronautics and Astronautics, University of Washington

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In Campus Calendar

Graduate Studies

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Faculty/Staff, Public, Graduate students, Undergraduate students
Phd proposal
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Jul 17, 2020 - 4:23pm
  • Last Updated: Jul 17, 2020 - 4:23pm