Approximate steady-state performance prediction of large-scale constrained model predictive control systems

TitleApproximate steady-state performance prediction of large-scale constrained model predictive control systems
Publication TypeJournal Article
Year of Publication2005
AuthorsFan, J., G. E. Stewart, G. A. Dumont, J. U. Backstrom, and P. He
JournalControl Systems Technology, IEEE Transactions on
Pagination884 - 895
Date Publishednov.
Keywordsclosed loop systems, closed-loop simulation, closed-loop transfer function, constrained model predictive controller, cross-directional predictive control, large-scale constrained model, large-scale system, large-scale systems, MPC, optimisation, optimization method, paper machine, paper making, paper making industry, parameter tuning, predictive control, predictive control system, spatial system, spatially distributed system, static optimization, steady-state performance prediction, temporal system

When tuning the parameters of a constrained model predictive controller (MPC), one usually will use closed-loop simulations in order to predict closed-loop performance. Closed-loop simulation can be very time-consuming and inconvenient for large-scale constrained MPC, such as paper machine cross-directional (CD) predictive control. Paper machine CD processes are two-dimensional (2-D) (temporal and spatial) systems with up to 600 inputs and 6000 outputs. It is very important to predict the steady-state values for the closed-loop CD MPC systems during the tuning process, as the variances of these values are used as the control performance indexes in paper making industry. This article proposes to use a direct one-step static optimizer for approximating the closed-loop steady-state performance of constrained CD MPC. The parameters of this static optimizer can be obtained through minimizing the difference of two closed-loop transfer functions. Experiments with industrial data demonstrate that the static optimizer is computationally much more efficient (up to two orders of magnitude) than closed-loop simulation while reliably and accurately predicting the steady-state performance.


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