Distributed Model Predictive Control for Piecewise Affine Systems Based on Switching ADMM

In this article, we present a novel approach for distributed model predictive control (MPC) for piecewise affine (PWA) systems. Existing approaches rely on solving mixed-integer optimization problems, requiring significant computation power or time. We propose a distributed MPC scheme that requires...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 70; no. 6; pp. 3727 - 3741
Main Authors: Mallick, Samuel, Dabiri, Azita, De Schutter, Bart
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Alternating direction method of multipliers (ADMM)
Analytical models
Closed loops
Computational complexity
Control systems
Convex functions
Convexity
Costs
Couplings
distributed model predictive control (MPC)
Heuristic algorithms
Mixed integer
networked systems
Optimal control
Optimization
piecewise affine (PWA) systems
Predictive control
Subsystems
Switches
Vectors
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Summary:In this article, we present a novel approach for distributed model predictive control (MPC) for piecewise affine (PWA) systems. Existing approaches rely on solving mixed-integer optimization problems, requiring significant computation power or time. We propose a distributed MPC scheme that requires solving only convex optimization problems. The key contribution is a novel method, based on the alternating direction method of multipliers, for solving the nonconvex optimal control problem that arises due to the PWA dynamics. We present a distributed MPC scheme, leveraging this method, that explicitly accounts for the coupling between subsystems by reaching agreement on the values of coupled states. Stability and recursive feasibility are shown under additional assumptions on the underlying system. Two numerical examples are provided, in which the proposed controller is shown to significantly improve the CPU time and closed-loop performance over existing state-of-the-art approaches.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2024.3512334