@MASTERSTHESIS\{IMM2013-06680,
    author       = "E. S. Christensen",
    title        = "Industrial Model Predictive Control",
    year         = "2013",
    school       = "Technical University of Denmark, Department of Applied Mathematics and Computer Science",
    address      = "Matematiktorvet, Building 303B, {DK-}2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk",
    type         = "",
    note         = "{DTU} supervisor: John Bagterp J{\o}rgensen, jbjo@dtu.dk, {DTU} Compute",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "This thesis presents a procedure for controlling an industrial process using Model Predictive Control (MPC).
The first part of the thesis introduces the basic ideas of model predictive control and the mathematical theory on which the procedure is based. In particular, it is investigated how a linear model of the process to be controlled can be identified from input-output data of the process. Furthermore, it is discussed how a simulation model of the industrial process can be modeled.
Disturbance rejection and offset free control are important concepts in industrial control. To achieve offset free control in the face of unknown disturbances and/or plant-model mismatch, integrators are added to the identified linear model. Three different approaches to adding these integrators are presented.
Based on the identified linear model extended with integrators an unconstrained {MPC} is formulated and subsequently transformed into a convex quadratic optimization problem. This optimization problem can be solved explicitly and the resulting optimal control law is linear.
The linear controller is combined with the linear process model forming a closedloop state-space model. For the purpose of tuning the developed {MPC,} an optimization based tuning approach was studied. To set up this optimization problem different performance measures for the closed-loop control system have been analyzed. One of the key elements of the optimization is the addition of a non-linear constraint, which is used to ensure robustness of the resulting controller.
In the second part of the thesis a case study has been conducted for a modified {4-}tank process, this process has been used as a representative of an industrial process. The process exhibits some of the typical behavior of an industrial process such as strong interaction and non-minimum phase behavior.
The first part of the case study identifies a linear model of the modified {4-}tank system from input-output data. Since no real industrial process has been available for this project, the input-output data were obtained by simulation using a first-principles non-linear model of the process. Secondly, tuning parameters were obtained from the optimization based tuning approach.
Finally, closed-loop simulations have been carried out using the tuning parameters obtained by the optimization problem. In these simulations the first-principles non-linear model for the modified {4-}tank process was used as the plant.
In general it was seen that the optimization approach produced some reasonably good tuning parameters for the modified {4-}tank process. Furthermore, in closedloop simulation it was illustrated that the closed-loop performance obtained was satisfactory with respect to both tracking and disturbance rejection even under a high level of noise in the system."
}