Die Bedeutung der Bronchospirometrie in der Lungenchirurgie

Maurath, J

doi:10.1007/bf01403367

Cited by 2 publications

(2 citation statements)

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“…[15]) are considered. The two process dynamic models were as follows: The target condition number in each case was 500 as suggested in [6].…”

Section: Off-line Mpc Designmentioning

confidence: 99%

“…Before describing the proposed methods, it must be stressed that the use of move suppression (regularization) is not the only possible method to have been proposed to improve the numerical properties and robustness of MPC algorithms. Methods based upon Principal Components Analysis (PCA) [15] or the use of a 'shifted' DMC algorithm [16] can both achieve such goals and correct rank deficiencies in MPC controllers without the need to obtain a reduced order approximation of the actual progress dynamics [16]. However in this paper, the focus will be upon move suppression only, and specifically improvements to the methods proposed in the works [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Move Suppression Calculations for Well-Conditioned MPC

Short

2017

ISA Transactions

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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 Eigen decomposition 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.

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“…[15]) are considered. The two process dynamic models were as follows: The target condition number in each case was 500 as suggested in [6].…”

Section: Off-line Mpc Designmentioning

confidence: 99%