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Implicit Non-convex Model Predictive Control

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Handbook of Model Predictive Control

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

Abstract

Model Predictive Control (MPC) techniques often need to be deployed on a nonlinear dynamic model of the system to be controlled. This type of application of MPC is usually referred to as Nonlinear MPC (NMPC). Explicit approaches for NMPC are difficult to deploy, and one typically resorts to computing the solutions to the NMPC scheme on-line, i.e. implicitly. The difficulty then becomes one of performing the fairly heavy computations required to compute the NMPC solutions within the allotted time budget. In this chapter, we will present a summarized overview of the most commonly used techniques to approach this problem. We will focus on the main aspects of these approaches that are arguably keys to deploying real-time NMPC, namely: the problem discretization, path-following methods, and the structure of the underlying linear algebra. Our focus here will be on offering the reader an accessible overview of these crucial aspects.

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Authors and Affiliations

  1. Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden

    Sebastien Gros

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  1. Sebastien Gros
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Correspondence to Sebastien Gros .

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Editors and Affiliations

  1. Independent Researcher, London, UK

    Saša V. Raković

  2. Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA

    William S. Levine

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Gros, S. (2019). Implicit Non-convex Model Predictive Control. 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_14

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  • DOI: https://doi.org/10.1007/978-3-319-77489-3_14

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