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Impact of structure on the performance of distributed model predictive control - Insights from an experimental case study
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DOI：10.1016/j.jprocont.2026.103695.png)
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Abstract
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Distributed model predictive control (DMPC) has emerged as an effective strategy for large-scale integrated systems, offering a balance between performance, computational efficiency and practical implementability. The performance of DMPC strongly depends on the underlying distributed architecture and choosing the right decomposition, especially for an integrated system, is a nontrivial task. The objective of this study is to use an experimental system to assess the impact of distributed architecture on the eventual closed-loop performance under practical challenges. To this end, the well-known quadruple tank system with two inputs and two outputs, resulting in two distributed architectures, is selected. This study presents a comprehensive comparison of these architectures in terms of closed-loop performance and computation time, under practical constraints such as plant-model mismatch, measurement noise, transport lag, etc. Furthermore, experimental verification of the theoretical results in the context of distributed architecture synthesis, quantification of the extent of the performance improvement as well as comparison with centralized model predictive control is pursued to draw key insights.
Keywords:
Model predictive control
Distributed control
Quadruple tank system
Experimental control
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