Model Predictive Control for continuous piecewise affine systems using optimistic optimization

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This paper considers model predictive control for continuous piecewise affine (PWA) systems. In general, this leads to a nonlinear, nonconvex optimization problem. We introduce an approach based on optimistic optimization to solve the resulting optimization problem. Optimistic optimization is based on recursive partitioning of the feasible set and is characterized by an efficient exploration strategy seeking for the optimal solution. The advantage of optimistic optimization is that one can guarantee bounds on the suboptimality with respect to the global optimum for a given computational budget. The 1-norm and ∞-norm objective functions often considered in model predictive control for continuous PWA systems are continuous PWA functions. We derive expressions for the core parameters required by optimistic optimization for the resulting optimization problem. By applying optimistic optimization, a sequence of control inputs is designed satisfying linear constraints. A bound on the suboptimality of the returned solution is also discussed. The performance of the proposed approach is illustrated with a case study on adaptive cruise control.
Original languageEnglish
Title of host publicationProceedings of the 2016 American Control Conference (ACC)
Subtitle of host publicationACC 2016
EditorsGeorge Chiu, Katie Johnson, Danny Abramovitch
Place of PublicationPiscataway, NJ, USA
ISBN (Print)978-1-4673-8682-1
Publication statusPublished - 2016
EventAmerican Control Conference (ACC), 2016 - Boston, MA, United States
Duration: 6 Jul 20168 Jul 2016


ConferenceAmerican Control Conference (ACC), 2016
Abbreviated titleACC 2016
Country/TerritoryUnited States
CityBoston, MA

Bibliographical note

Accepted Author Manuscript


  • Optimization
  • Partitioning algorithms
  • Linear programming
  • Predictive control
  • Complexity theory
  • Prediction algorithms
  • Cruise control


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