This paper addresses trajectory optimization for hypersonic vehicles
under atmospheric and aerodynamic uncertainties using
techniques from desensitized optimal control (DOC), wherein
open-loop optimal controls are obtained by minimizing the sum
of the standard objective function and a first-order penalty
on trajectory variations due to parametric uncertainty. The
proposed approach is demonstrated via numerical simulations
of a minimum-final-time Earth reentry trajectory for an X-33
vehicle with an uncertain atmospheric scale height and drag
coefficient. Monte Carlo simulations indicate that dispersions
in the final position footprint and the final energy can be significantly
reduced without closed-loop control and with little tradeoff
in the performance metric set for the trajectory.
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Decoupled Data-Based Approach for Learning to Control Nonlinear Dynamical Systems
This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynam- ical. This problem is subject to the ‘curse of dimension- ality’ associated with the dynamic programming method. This paper proposes a novel decoupled data-based con- trol (D2C) algorithm that addresses this problem using a decoupled, ‘open-loop - closed-loop’, approach. First, an open-loop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical system. Then, closed-loop control is developed around this open-loop trajectory by linearization of the dynamics about this nominal trajectory. By virtue of linearization, a linear quadratic regulator based algorithm can be used for this closed-loop control. We show that the performance of D2C algorithm is approximately optimal. Moreover, simulation performance suggests a significant reduction in training time compared to other state of the art algorithms.
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- Award ID(s):
- 1850206
- NSF-PAR ID:
- 10315328
- Date Published:
- Journal Name:
- IEEE Transactions on Automatic Control
- ISSN:
- 0018-9286
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TAC.2021.3108552
