Move Suppression Calculations for Well-Conditioned MPC. (March 2017)

• Record Type: Journal Article
• Authors: Short, Michael
• Is Part Of: ISA transactions. Volume 67(2017:Mar.)
• Page Start: 371
• Page End: 381
• Publication Date: 2017-03
• Subjects: Predictive Control -- Generalized Predictive Control -- Dynamic Matrix Control -- Move Suppression -- Numerical Conditioning; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2016.11.020
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Several popular tuning strategies applicable to Model Predictive Control (MPC) schemes such as GPC and DMC have previously been developed. Many of these tuning strategies require an approximate model of the controlled process to be obtained, typically of the First Order Plus Dead Time type. One popular method uses such a model to analytically calculate an approximate value of the move suppression coefficient to achieve a desired condition number for the regularized system dynamic matrix; however it is not always accurate and tends to under-estimate the required value. In this paper an off-line method is presented to exactly calculate the move suppression coefficient required to achieve a desired condition number directly from the unregularized system dynamic matrix. This method involves an Eigendecomposition of the system dynamic matrix - which may be too unwieldy in some cases –and a simpler analytical expression is also derived. This analytical expression provides a guaranteed tight upper bound on the required move suppression coefficient yielding a tuning formula which is easy to apply, even in on-line situations. Both methods do not require the use of approximate or reduced order process models for their application. Simulation examples and perturbation studies illustrate the effectiveness of the methods in both off-line and on-line MPC configurations. It is shown that accurate conditioning and improved closed loop robustness can be achieved. Highlights: A method is presented to calculate the move suppression parameter for well-conditioned MPC. A reduced-order approximate model is not required for its application. A simpler analytical bound is also developed for use with adaptive MPC. Simulations and perturbation studies illustrate improved accuracy and robustness.