Model Predictive Control Tuning Methods: A Review

Garriga, Jorge L.; Soroush, Masoud

doi:10.1021/ie900323c

Cited by 320 publications

(219 citation statements)

References 66 publications

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“…The unitary value in the equality constraint represents the total mass population according to (5). The proper prioritization of these objectives might vary over time as exogenous disturbances affecting the system also vary.…”

Section: Dynamical Weighting Proceduresmentioning

confidence: 99%

“…Most of the existing strategies to tune an MPC controller utilize an off-line approach, and sometimes it is a trial and error procedure. In [5], a review of some tuning strategies has been made, and some approaches such as off-line and on-line strategies have been classified. Since the conditions over the system might vary over time, it has become a relevant issue the development of strategies that allow to tune MPC controllers permanently in a dynamical manner.…”

Section: Introductionmentioning

confidence: 99%

“…Also, it has been highlighted its simplicity as an advantage for implementation. More approaches to solve this problem have been proposed after the review presented in [5]. In [4], a tuning methodology based on a matching to a desired reference controller has been proposed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary Game-Based Dynamical Tuning for Multi-objective Model Predictive Control

Barreiro-Gómez

Ocampo‐Martínez

Quijano

2015

Developments in Model-Based Optimization and Control

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Model predictive control (MPC) is one of the most used optimizationbased control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights associated to each objective in the cost function, is treated in this work. All the possible combinations of weights within the cost function affect the optimal result in a given Pareto front. Furthermore, when the system has time-varying parameters, e.g., periodic disturbances, the appropriate weight tuning might also vary over time. Moreover, taking into account the computational burden and the selected sampling time in the MPC controller design, the computation time to find a suitable tuning is limited. In this regard, the development of strategies to perform a dynamical tuning in function of the system conditions potentially improves the closed-loop performance. In order to adapt in a dynamical way the weights in the MPC multi-objective cost function, an evolutionary-game approach is proposed. This approach allows to vary the prioritization weights in the proper direction taking as a reference a desired region within the Pareto front. The proper direction for the prioritization is computed by only using the current system values, i.e., the current optimal control action and the measurement of the current states, which establish the system cost function over a certain point in the Pareto front. Finally, some simulations of a multi-objective MPC for a real multi-variable case study show a comparison between the system performance obtained with static and dynamical tuning.

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Section: Dynamical Weighting Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Game-Based Dynamical Tuning for Multi-objective Model Predictive Control

Barreiro-Gómez

Ocampo‐Martínez

Quijano

2015

Developments in Model-Based Optimization and Control

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“…MPC tuning, however, remains somewhat ad hoc. Though there now exists a fair body of literature for good heuristic tuning choices, which can allow an operator to tune the MPC offline before applying it to the real process (Garriga and Soroush (2010)), the obtained parameters will only be nominally optimal and, when the model uncertainty is significant, may be unable to yield satisfactory performance in practice. A particular case where this problem can be solved in a general, algorithmic manner is the one of batch processes, where the MPC controller may be asked to track the same reference profile many timesfor example, when maintaining a cooling profile in a crystallizer (Shen et al (1999)).…”

Section: Mpc Formulation and Tuningmentioning

confidence: 99%

Run-to-Run MPC Tuning via Gradient Descent

Bunin

Tirado

François

et al. 2012

Computer Aided Chemical Engineering

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“…Model predictive control (MPC) has been implemented widely in the chemical and process control industries since its introduction in the 1970s [1][2][3]. Although a large number of different approaches to MPC have been formulated, two of the most popular with practitioners have proved to be the Dynamic Matrix Control (DMC) technique [4] and the Generalized Predictive Control (GPC) technique [5].…”

Section: Introductionmentioning

confidence: 99%

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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Model Predictive Control Tuning Methods: A Review

Cited by 320 publications

References 66 publications

Evolutionary Game-Based Dynamical Tuning for Multi-objective Model Predictive Control

Evolutionary Game-Based Dynamical Tuning for Multi-objective Model Predictive Control

Run-to-Run MPC Tuning via Gradient Descent

Move Suppression Calculations for Well-Conditioned MPC

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