TR2018-086

Real-time optimization and model predictive control for aerospace and automotive applications

    •  Di Cairano, S., Kolmanovsky, I.V., "Real-time optimization and model predictive control for aerospace and automotive applications", American Control Conference (ACC), DOI: 10.23919/​ACC.2018.8431819, June 2018, pp. 2392-2409.
      BibTeX TR2018-086 PDF
      • @inproceedings{DiCairano2018jun,
      • author = {Di Cairano, Stefano and Kolmanovsky, Ilya V.},
      • title = {Real-time optimization and model predictive control for aerospace and automotive applications},
      • booktitle = {American Control Conference (ACC)},
      • year = 2018,
      • pages = {2392--2409},
      • month = jun,
      • doi = {10.23919/ACC.2018.8431819},
      • url = {https://www.merl.com/publications/TR2018-086}
      • }
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  • Research Area:

    Control

Abstract:

In recent years control methods based on realtime optimization (RTO) such as model predictive control (MPC) have been investigated for a significant number of applications in the automotive and aerospace (A&A) domains. This paper provides a tutorial overview of RTO in automotive and aerospace, with particular focus on MPC which is probably the most largely investigated method. First, we review the features that make RTO appealing for A&A applications. Then, due to the model-based nature of these control methods, we describe the key first principle models and opportunities that these provide for RTO. Next, we detail the key steps and guidelines of the MPC design process which are tailored to A&A systems. Finally, we discuss numerical algorithms for implementing RTO, and their suitability for implementation in embedded computing platforms to in A&A domains

 

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