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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12617

Title: On-line tuning strategy for model predictive controllers
Authors: Al-Ghazzawi, A.a.
Ali, E.b.
Nouh, A.a.
Zafiriou, E.
Keywords: Algorithms; Closed loop control systems; Distillation columns; Least squares approximations; Linear control systems; Mathematical models; Optimization; Parameter estimation; Process control; System stability; Tuning
Issue Date: 2001
Publisher: Elsevier Science Ltd, Exeter, United Kingdom
Citation: Journal of Process Control Volume 11, Issue 3, June 2001, Pages 265-284
Abstract: This paper presents an intuitive on-line tuning strategy for linear model predictive control (MPC) algorithms. The tuning strategy is based on the linear approximation between the closed-loop predicted output and the MPC tuning parameters. By direct utilization of the sensitivity expressions for the closed-loop response with respect to the MPC tuning parameters, new values of the tuning parameters can be found to steer the MPC feedback response inside predefined time-domain performance specifications. Hence, the algorithm is cast as a simple constrained least squares optimization problem which has a straightforward solution. The simplicity of this strategy makes it more practical for on-line implementation. Effectiveness of the proposed strategy is tested on two simulated examples. One is a linear model for a three-product distillation column and the second is a non-linear model for a CSTR. The effectiveness of the proposed tuning method is compared to an exiting offline tuning method and showed superior performance
URI: http://hdl.handle.net/123456789/12617
ISSN: 09591524
Appears in Collections:College of Engineering

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