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Solving Optimal Control with Nonlinear Dynamics Using Sequential Convex Programming

Foust, Rebecca and Chung, Soon-Jo and Hadaegh, Fred Y. (2019) Solving Optimal Control with Nonlinear Dynamics Using Sequential Convex Programming. In: AIAA Scitech 2019 Forum, 7-11 January 2019, San Diego, CA.

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Sequential convex programming (SCP) is a useful tool in obtaining real-time solutions to direct optimal control, but it is unable to adequately model nonlinear dynamics due to the linearization and discretization required. As nonlinear program solvers are not yet functioning in real-time, a tool is needed to bridge the gap between satisfying the nonlinear dynamics and completing execution fast enough to be useful. This paper presents a real-time control algorithm, sequential convex programming with nonlinear dynamics correction (SCPn), which ameliorates the performance of SCP under nonlinear dynamics. Simulations are presented to validate the efficacy of the method.

Item Type:Conference or Workshop Item (Paper)
Related URLs:
URLURL TypeDescription
Foust, Rebecca0000-0003-1470-1716
Chung, Soon-Jo0000-0002-6657-3907
Additional Information:© 2019 AIAA. AIAA Paper 2019-0652. The work of Rebecca Foust was supported by a NASA Space Technology Research Fellowship and in part by the Jet Propulsion Laboratory (JPL). Government sponsorship is acknowledged. The authors also thank Amir Rahmani and Christian Chilan for their technical input.
Funding AgencyGrant Number
NASA Space Technology Research FellowshipUNSPECIFIED
Other Numbering System:
Other Numbering System NameOther Numbering System ID
AIAA Paper2019-0652
Record Number:CaltechAUTHORS:20190109-132944040
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Official Citation:Solving Optimal Control with Nonlinear Dynamics Using Sequential Convex Programming. Rebecca Foust, Soon-Jo Chung, and Fred Y. Hadaegh. AIAA Scitech 2019 Forum. San Diego, California.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92176
Deposited By: Tony Diaz
Deposited On:09 Jan 2019 22:32
Last Modified:16 Nov 2021 03:47

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