Solving Hybrid Model Predictive Control Problems via a Mixed-Integer Approach

Iman Nodozi () and Ricardo G. Sanfelice ()
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Iman Nodozi: University of California
Ricardo G. Sanfelice: University of California

Chapter Chapter 4 in Model Predictive Control, 2025, pp 83-109 from Springer

Abstract: Abstract This book chapter presents a method to efficiently solve hybrid model predictive control (MPC) problems for a class of discretized hybrid control systems. The proposed method recasts the optimal control problem associated with hybrid MPC into a mixed-integer quadratic problem (MIQP), which can be efficiently solved using well-established algorithms. The approach consists of transforming the discretized hybrid control system into a mixed logical dynamical (MLD) system. This transformation enables the use of MIQP tools for the solution of the hybrid MPC problem. To arrive to such a system, an intermediate step converting the discretized hybrid control system into a discrete-time control system with set-valued dynamics is formulated. The proposed method is illustrated in several examples that demonstrate the ability of the approach to handle state jumps and logic variables present in the hybrid control system, highlighting its suitability for real-world applications featuring hybrid dynamics.

Keywords: Hybrid Model Predictive Control (MPC); Mixed-Integer Quadratic Programming (MIQP); Mixed Logical Dynamical (MLD) Systems; Switching dynamics; Discrete-event systems (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:dymchp:978-3-031-85256-5_4

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DOI: 10.1007/978-3-031-85256-5_4

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