Tuning of methods for offset free MPC based on ARX model representations

Huusom, Jakob Kjøbsted; Poulsen, Niels Kjølstad; Jørgensen, Sten Bay; Jørgensen, John Bagterp

doi:10.1109/acc.2010.5530560

Cited by 21 publications

(17 citation statements)

References 10 publications

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“…In such cases, the observer that guarantees offset free control introduces a plant model mismatch. This plant model mismatch complicates the tuning of the controller [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

A tuning procedure for ARX-based MPC of multivariate processes

Olesen

Huusom

Jørgensen

2013

2013 American Control Conference

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Abstract-We present an optimization based tuning procedure with certain robustness properties for an offset free Model Predictive Controller (MPC). The MPC is designed for multivariate processes that can be represented by an ARX model. The stochastic model of the ARX model identified from inputoutput data is modified with an ARMA model designed as part of the MPC-design procedure to ensure offset-free control. The MPC is designed and implemented based on a state space model in innovation form. Expressions for the closed-loop dynamics of the unconstrained system is used to derive the sensitivity function of this system. The closed-loop expressions are also used to numerically evaluate absolute integral performance measures. Due to the closed-loop expressions these evaluations can be done relative quickly. Consequently, the tuning may be performed by numerical minimization of the integrated absolute error subject to a constraint on the maximum of the sensitivity function. The latter constraint provides a robustness measure that is essential for the procedure. The method is demonstrated for two simulated examples: A Wood-Berry distillation column example and a cement mill example.

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“…In such cases, the observer that guarantees offset free control introduces a plant model mismatch. This plant model mismatch complicates the tuning of the controller [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

A tuning procedure for ARX-based MPC of multivariate processes

Olesen

Huusom

Jørgensen

2013

2013 American Control Conference

Self Cite

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“…The MPC is composed of an optimal regulator and a state estimator. The regulator has the structure described in [12]- [14], with the modification that we use a deterministic linearised first principles engineering model instead of an innovation form state space model identified from data. The model used for simulation is also based on the non-linear first principles engineering model.…”

Section: Contentmentioning

confidence: 99%

“…The problem in (14) can be converted to a constrained quadratic problem. Appendix A shows the details of the derivation of the regulator, the tuning parameters Q z and S u and the constraints.…”

Section: Regulatormentioning

confidence: 99%

“…Algorithm 1 list the on-line computations in the linear MPC algorithm. The computation of u k = µ(x k|k , r k , d k , u k−1 ) is performed by solving (14). The solution of the QP (14) is the computational expensive step in the MPC algorithm.…”

Section: Regulatormentioning

confidence: 99%

“…III-C is solved by formulating the corresponding convex quadratic problem. The tracking problem is similar to [12]- [14].…”

Section: A Regulatormentioning

confidence: 99%

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Application of constrained linear MPC to a spray dryer

Petersen

Poulsen

Niemann

et al. 2014

2014 IEEE Conference on Control Applications (CCA)

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Abstract-In this paper we develop a linear model predictive control (MPC) algorithm for control of a two stage spray dryer. The states are estimated by a stationary Kalman filter. A non-linear first-principle engineering model is developed to simulate the spray drying process. The model is validated against experimental data and able to precisely predict the temperatures, the air humidity and the residual moisture in the dryer. The MPC controls these variables to the target and reject disturbances. Spray drying is a cost-effective method to evaporate water from liquid foods and produces a free flowing powder. The main challenge of spray drying is to meet the residual moisture specification and prevent powder from sticking to the chamber walls. By simulation we compare the performance of the MPC against the conventional PID control strategy. During an industrially recorded disturbance scenario, the MPC increases the production rate by 7.9%, profit of production by 8.2% and the energy efficiency by 4.1% on average.

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From multi‐physics models to neural network for predictive control synthesis

Blaud

Chevrel

Claveau

et al. 2021

Optim Control Appl Methods

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The aim of this document is to present an efficient and systematic method of model-based predictive control synthesis. Model predictive control requires using a model of a dynamical system, that can be linear, time-varying, non-linear or identified from data. Finding a model that is both precise and simulatable at low computational cost can be challenging and time consuming due to requiring extensive knowledge of the system and physics as well as a large volume of data with relevant scenarios and sometimes a complicated identification work (filtering noises and bias, data formatting, etc.). The proposed methodology begins with fine-scale multi-physics modelling, which is possible thanks to open model libraries (see Modelica). The obtained model is then simulated by considering ad hoc scenarios to generate data, which are then used to identify a neural network, that will support the predictive control syntheses. The systematic methodology is detailed and applied to the widely used control benchmark known as the quadruple tanks process. Results show that the methodology is accurately applied to optimize hyperparameters in finding a neural network model and to control the quadruple tanks process with the predictive controller.

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Tuning of methods for offset free MPC based on ARX model representations

Cited by 21 publications

References 10 publications

A tuning procedure for ARX-based MPC of multivariate processes

A tuning procedure for ARX-based MPC of multivariate processes

Application of constrained linear MPC to a spray dryer

From multi‐physics models to neural network for predictive control synthesis

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