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Convexification and Real-Time Optimization for MPC with Aerospace Applications

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Handbook of Model Predictive Control

Part of the book series: Control Engineering ((CONTRENGIN))

Abstract

This chapter gives an overview of recent developments of convexification and real-time convex optimization based control methods, in the context of Model Predictive Control (MPC). Lossless Convexification is a technique that formulates a class of non-convex control constraints as equivalent convex ones, while Successive Convexification gives an algorithm that targets nonlinear dynamics and certain non-convex state constraints. A large class of real-world optimal control problems can be solved with either method or a combination of both. For some time-critical applications, such as autonomous vehicles, it is crucial to have real-time capabilities. The real-time solution to these problems requires highly efficient customized convex programming solvers, which is also discussed as a part of this chapter. The effectiveness of convexification methods and real-time computation is demonstrated by a planetary soft landing problem throughout the chapter.

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Acknowledgements

We would like to thank David S. Bayard, John M. Carson, and Daniel P. Scharf of JPL, Lars Blackmore of SpaceX, John Hauser of University of Colorado, and Eric Feron of Georgia Institute of Technology for insightful discussions in this area. This research was supported in part by the Office of Naval Research Grant No. N00014-16-1-2318 and by the National Science Foundation Grants No. CMMI-1613235 and CNS-1619729.

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Correspondence to Behçet Açıkmeşe .

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Mao, Y., Dueri, D., Szmuk, M., Açıkmeşe, B. (2019). Convexification and Real-Time Optimization for MPC with Aerospace Applications. In: Raković, S., Levine, W. (eds) Handbook of Model Predictive Control. Control Engineering. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-77489-3_15

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  • DOI: https://doi.org/10.1007/978-3-319-77489-3_15

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