An analytical tuning approach to multivariable model predictive controllers

Bagheri, Peyman; Khaki‐Sedigh, Ali

doi:10.1016/j.jprocont.2014.09.002

Cited by 22 publications

(29 citation statements)

References 21 publications

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“…Also, it is shown that for the FOPDT models control horizon of two provides the maximum achievable performance. The proposed method in [10] is extended for unstable plants with fractional dead time and multivariable plants in [11] and [12] respectively. Some efforts have been done in on-line tuning like gradient decrement, fuzzy logic and on-line optimization algorithms.…”

Section: Imentioning

confidence: 99%

Tuning of generalized predictive controllers for first order plus dead time models based on ANOVA

Ebrahimi

Bagheri

Khaki‐Sedigh

2015

2015 23rd Iranian Conference on Electrical Engineering

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Successful implementation of predictive controller requires an appropriate tuning of its parameters. Closed form tuning equations are practically rewarding as they can be easily implemented with relatively low computational costs. In this paper, a tuning strategy for the generalized predictive control of single input-single output and multi inputmulti output plants is presented. First order plus dead time model of the plant is considered and analysis of variance and nonlinear fitting is employed to derive tuning equations. Finally, simulation results are used to verify the efficiency of the proposed tuning strategy.

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Section: Imentioning

confidence: 99%

Tuning of generalized predictive controllers for first order plus dead time models based on ANOVA

Ebrahimi

Bagheri

Khaki‐Sedigh

2015

2015 23rd Iranian Conference on Electrical Engineering

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“…Due to the complex relationships between these tuneable parameters and the closed loop system properties, many previous authors have suggested 'tuning rules' that allow a user to configure an MPC instance to achieve a desired level of closed-loop performance [3][4][5][6][7][8][9][10][11][12][13][14]. This paper is concerned with the selection of the move suppression coefficient, which serves a dual purpose of conditioning the system matrix before its inversion and suppressing aggressive control actions [3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…This paper is concerned with the selection of the move suppression coefficient, which serves a dual purpose of conditioning the system matrix before its inversion and suppressing aggressive control actions [3][4][5][6][7][8][9][10][11][12][13][14]. This non-negative dimensionless parameter is known to have a significant impact upon performance and robustness [4][5][6][7][8], and in practice proves difficult to tune empirically (even for experienced control engineers) as recent work has highlighted [12].…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [9] describe an analytical method to compute the required move suppression coefficients to achieve a pre-specified closed loop performance for FOPDT processes when the control horizon M is equal to either 1 or 2. The method has also been extended to multivariable processes which can be approximated as a matrix of FOPDT responses [10].…”

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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“…Considering First Order plus Dead Time (FOPDT) model of real system, closed loop transfer function is derived and it is shown that maximum achievable performance can be obtained by control horizon of one. Also, this approach is extended to unstable plants with fractional delay in [7] and multivariable plants in [8]. However, these methods are not appropriate for unknown or time varying plants.…”

Section: Introductionmentioning

confidence: 99%

Adaptive tuning of model predictive control based on analytical results

Gholaminejad

Khaki‐Sedigh

Bagheri

2016

2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA)

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In dealing with model predictive controllers (MPC), controller tuning is a key design step. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a recently proposed analytical MPC tuning approach based on low order models. The performance of such methods deteriorates in dealing with unknown or time varying parameter plants. To overcome this problem, adaptive MPC tuning strategies are practical alternatives. The adaptive MPC tuning approach proposed in this paper is based on on-line identification and analytical tuning formulas. Simulation results are used to show the effectiveness of the proposed methodology. Keywords: Adaptive model predictive control; analytical tuning; first order plus dead time models I.

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An analytical tuning approach to multivariable model predictive controllers

Cited by 22 publications

References 21 publications

Tuning of generalized predictive controllers for first order plus dead time models based on ANOVA

Tuning of generalized predictive controllers for first order plus dead time models based on ANOVA

Move Suppression Calculations for Well-Conditioned MPC

Adaptive tuning of model predictive control based on analytical results

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