Abstract
This paper aims to propose convex-optimization-based entry guidance for a spaceplane, which has potential in online implementation with less sensitivity to initial guess accuracy while mitigating a high-frequency jittering issue in the entry trajectory optimization problem. To this end, a highly nonlinear, constrained, and nonconvex entry guidance problem is converted into sequential convex sub-problems in the second-order cone programming (SOCP) form by an appropriate combination of successive linearization and convexification techniques. From the investigation on the potential sub-problem infeasibility due to a rough initial guess for radial distance, a linear penalized term associated with a virtual control for an inequality constraint is used to relieve the sub-problem infeasibility while preserving the standardized SOCP form. An adjustable trust-region bound is also adopted in the proposed approach to improve the convergence property further. Additionally, a change of control variables and a relaxation technique are utilized to relieve the high-frequency jittering issue. It is proven that the Lossless convexification property is preserved for the relaxed problem even in the presence of the penalty terms. The feasibility of the proposed method is investigated through numerical simulations.
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This work has been supported by the Korea Aerospace Research Institute (KARI)’s own research project titled “Base Technology for Flight Control of Gliding and Landing for Spaceplanes.”
Juho Bae is currently working on a B.S. degree in electrical engineering and mathematics from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. His research interests include adaptive control, entry guidance, numerical optimization, and convex programming.
Sang-Don Lee received his B.S. degree in mechanical engineering from Hanyang University, Seoul, Korea, in 2019, and an M.S. degree in aerospace engineering from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 2021. He is currently a Ph.D. student in the Department of Aerospace Engineering from KAIST, Daejeon, Korea. His research interests include trajectory optimization, convex programming, and reinforcement learning.
Young-Won Kim received his B.S. degree in mechanical engineering from Handong University in 2014, and an M.S. degree in aerospace engineering from Korea Advanced Institute of Science and Technology (KAIST), in 2016, respectively. Currently, he is a Ph.D. candidate in aerospace engineering from KAIST, Daejeon, Korea. His area of scientific interest includes advanced missile guidance and control, and robust control of Personal Air Vehicle (PAV).
Chang-Hun Lee received his B.S., M.S., and Ph.D. degrees in aerospace engineering from Korea Advanced Institute of Science and Technology (KAIST), in 2008, 2010, and 2013, respectively. From 2013 to 2015, he was a Senior Researcher for Guidance and Control Team, Agency for Defense Development, Daejeon, Korea. From 2016 to 2018, he was a Research Fellow for School of Aerospace, Transportation, and Manufacturing, Cranfield University, Bedford, United Kingdom. Since 2019, he has been with the Department of Aerospace Engineering, KAIST, Daejeon, Korea, where he is currently an Associate Professor. His recent research interests include advanced missile guidance and control, cooperative control for unmanned aerial vehicles, target tracking filter, deep learning, and aviation data analytics. Currently, he is a technical editor of International Journal of Aeronautical and Space Science.
Sung-Yug Kim received his B.S. degrees in naval architecture and ocean engineering from Inha University in 1987, Incheon, Korea. He served as a navy Lieutenant from 1987 to 1994. He was a Senior Researcher with the Korea Aerospace Industries, Ltd., Changwon, from 1994 to 2000. He has been a Principal Researcher with the Unmanned Aircraft System Research Division, Korea Aerospace Research Institute (KARI) since 2001. His research interests include system integration and hardware design. He has been involved in projects developing UAVs, especially in the design and implementation of hardware for flight dynamics and control.
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Bae, J., Lee, SD., Kim, YW. et al. Convex Optimization-based Entry Guidance for Spaceplane. Int. J. Control Autom. Syst. 20, 1652–1670 (2022). https://doi.org/10.1007/s12555-021-0580-4
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DOI: https://doi.org/10.1007/s12555-021-0580-4