Model predictive control with memory-based discrete search for switched linear systems

Rie B. Larsen*, Bilge Atasoy, Rudy R. Negenborn

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

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Controlling systems with both continuous and discrete actuators using model predictive control is often impractical, since mixed-integer optimization problems are too complex to solve sufficiently fast. This paper proposes a parallelizable method to control both the continuous input and the discrete switching signal for linear switched systems. The method uses ideas from Bayesian optimization to limit the computation to a predefined number of convex optimization problems. The recursive feasibility and stability of the method is guaranteed for initially feasible solutions. Results from simulated experiments show promising performances and computation times.

Original languageEnglish
Pages (from-to)6769-6774
Issue number2
Publication statusPublished - 2020
Event21st IFAC World Congress 2020 - Berlin, Germany
Duration: 12 Jul 202017 Jul 2020


  • Computational methods
  • Integer control
  • Mixed-integer optimization
  • Parallel computation
  • Predictive control
  • Rolling horizon
  • Stability analysis


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