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A Complexity Analysis of Event-Triggered Model Predictive Control on\n Industrial Hardware

2019/05/10 by Patrik Simon Berner, Martin Mönnigmann, Berner, Patrik Simon +1
Medicine · Engineering · Chemistry · #Advanced MRI Techniques and Applications #Advanced Control Systems Optimization #Metal-Organic Frameworks: Synthesis and Applications

paper · pdf · doi:10.48550/arxiv.1905.03998

Abstract

We implement a recently proposed event-triggered networked MPC approach on\nindustrial hardware to analyze its practical relevance. There exist several\nalternatives for such an implementation that differ with respect to the\ndistribution of computational load between local and central nodes, and with\nrespect to network bandwidth requirements. These alternatives have been\nanalyzed theoretically before, but when implemented it becomes evident that\ntheir usefulness cannot be predicted based on theoretical considerations alone.\nIt is the purpose of the present paper to account for both practical and\ntheoretical aspects in determining which alternative is most appropriate for an\nimplementation on industrial hardware. The smallest possible bandwidth is known\nto result for a variant in which only the active set of constraints is\ntransmitted from the central to the local nodes. Since local nodes must\ndetermine the control law from the active set in this case, which requires\nmatrix inversions, an unattractive computational cost results at first sight.\nSomewhat surprisingly, the computational cost scales practically linearly in\nthe problem size when implemented. We confirm this result with a more detailed\ntheoretical complexity analysis than given in previous papers. All results are\nillustrated with data obtained with an implementation on industrial hardware\ncomponents.\n

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