Alternative solutions to multi-variate control performance assessment problems |
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| Authors: | Biao Huang Steven X Ding Nina Thornhill |
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| Affiliation: | aDepartment of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada T6G 2G6;bUniversity of Duisburg-Essen, Institute for Automatic Control and Complex Systems/Faculty 5, Bismarckstrasse 81, BB511, 47048 Duisburg, Germany;cDepartment of Electronic and Electrical Engineering, University College London, Torrington Place, WC1E 7JE London, UK |
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| Abstract: | Performance assessment of multi-variate control with minimum variance control as the benchmark requires an interactor matrix to filter the closed-loop output. This is to transfer the coordinate of the original variables into a new one in order to identify the control invariant disturbance dynamics from the first few terms of the closed-loop output Markov parameters. There has been a great deal of interest to simplify this approach, in particular, to find methods that do not need the interactor matrix. With this motivation, this paper explores alternative solutions to multi-variate control performance assessment problems. In particular, we will consider two practical scenarios: (1) known time delays between each pair of inputs and outputs, (2) no a priori knowledge about the process model or time delays at all. Solutions to these two scenarios are proposed. Two data-driven algorithms based on subspace approach are derived for the calculation of performance measures. Several examples illustrate the feasibility of the proposed approaches. |
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| Keywords: | Performance monitoring Performance assessment Control monitoring Multi-variate systems Subspace methods Projection |
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