A dynamic programming-inspired approach for Mixed Integer Optimal Control Problems with dwell time constraints

This paper introduces a dynamic programming-inspired approach for solving the Combinatorial Integral Approximation (CIA) problem within the CIA decomposition approach for Mixed-Integer Optimal Control Problems (MIOCPs). Additionally, we incorporate general dwell time constraints into this framework....

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Bibliographic Details
Published inJournal of process control Vol. 154; p. 103522
Main Authors Abbasi-Esfeden, Ramin, Plate, Christoph, Sager, Sebastian, Swevers, Jan
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.10.2025
Subjects
Dynamic programming
Optimal control
Switched systems
Switched systems
Optimal control
Dynamic programming
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Summary:This paper introduces a dynamic programming-inspired approach for solving the Combinatorial Integral Approximation (CIA) problem within the CIA decomposition approach for Mixed-Integer Optimal Control Problems (MIOCPs). Additionally, we incorporate general dwell time constraints into this framework. The proposed method is tested on four MIOCPs with a minimum dwell time constraint, and its performance is compared to that of the state-of-the-art general purpose solver GuRoBi (MILP) and to the tailored branch-and-bound (BnB) solver from the pycombina package. The results show that the proposed approach is more computationally efficient, and its flexible cost-to-go function formulation makes it suitable for handling cases where simple approximations of the relaxed solution are insufficient. •The CIA decomposition provides efficient solutions for MIOCPs.•A Dynamic Programming-Inspired method (DP) solves the second step in the CIA decomposition.•Dwell time constraints are efficiently handled within DP.•The flexible framework of DP adapts to various value functions.
ISSN:0959-1524
DOI:10.1016/j.jprocont.2025.103522