Nonlinear Predictive Control of a Semi Batch Polymerization Reactor by an Extended DMC

Peterson, T.J.; Hernández, Evelio; Arkun, Yaman; Schork, F. Joseph

doi:10.23919/acc.1989.4790430

Cited by 39 publications

(17 citation statements)

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“…A similar approach is employed by to characterize the effect of model uncertainty on the stability of a single-step NMPC method. The contraction mapping theorem has also been used to study the stability of a NMPC strategy based on secant iterations (Peterson et al 1989, Hernandez andArkun 1991). The major limitation of these techniques is that the stability analysis is based on the Downloaded by [University of Bristol] at 07:39 16 March 2015 computational approach employed.…”

Section: Analysis Of Nonlinear Model Predictive Controlmentioning

confidence: 99%

Theoretical analysis of unconstrained nonlinear model predictive control

Henson

Seborg

1993

International Journal of Control

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Model predictive control (MPC) is a well-established controller design strategy for linear process models. Because many chemical and biological processes exhibit significant nonlinear behaviour, several MPC techniques based on nonlinear process models have recently been proposed. The most significant difference between these techniques is the computational approach used to solve the nonlinear model predictive control (NMPC) optimization problem. Consequently, analysis of NMPC techniques is often connected to the computational approach employed. In this paper, a theoretical analysis of unconstrained NMPC is presented that is independent of the computational approach. A nonlinear discrete-time, state-space model is used to predict the effects of future inputs on future process outputs. It is shown that model inverse, pole-placement, and steady-state controllers can be obtained by suitable selection of the control and prediction horizons. Moreover, the NMPC optimization problem can be modified to yield nonlinear internal model control (NIMC). The computational requirements of NIMC are considerably less than NMPC, but the NIMC approach is currently restricted to nonlinear models with well-defined and stable inverses. The NIMC controller is shown to provide superior servo and regulatory performance to a linear IMC controller for a continuous stirred tank reactor.

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Section: Analysis Of Nonlinear Model Predictive Controlmentioning

confidence: 99%

Theoretical analysis of unconstrained nonlinear model predictive control

Henson

Seborg

1993

International Journal of Control

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“…Then they designed PID, Smith predictor, and Dahlin controllers for temperature control. Peterson et al [2] presented a nonlinear predictive strategy for semi-batch polymerization of methyl methacrylate (MMA). Soroush and Kravaris [3] applied a global linearization control (GLC) method to control the reactor temperature.…”

Section: Introductionmentioning

confidence: 99%

Generalization of Takagi-Sugeno Fuzzy Controller and its Application to Control of MMA Batch Polymerization Reactor

Solgi

Rafizadeh

2006

Polymer-Plastics Technology and Engineering

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In this research a generalization of Takagi-Sugeno fuzzy controllers is presented. In this generalization all or some of the inputs of the fuzzy controllers are fuzzy numbers. Also, it is proved that this generalization is well defined, which means that if the inputs of a generalized Takagi-Sugeno fuzzy controller are singleton fuzzy sets, then the generalized Takagi-Sugeno fuzzy controller will be reduced to a Takagi-Sugeno fuzzy controller. This controller was applied to temperature control of a methyl methacrylate (MMA) batch polymerization reactor, which uses jacket temperature error in addition to reactor temperature error. But the desired jacket temperature is affected by noise and disturbance. Therefore, there is uncertainty in the desired value of this variable. Fuzzy numbers are applied to model this uncertainty and a fuzzy trajectory was achieved for jacket desired temperature. After that an adaptation mechanism was designed. Experimental results present the fine performance of this controller in temperature control of solution polymerization of methyl methacrylate.

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“…In most applications of techniques with predictive model, a linear model is used to predict the behavior of the process on the horizon of interest [7,8]. The majority of the real processes show a nonlinear behavior and some works have extended the techniques of the predictive control incorporating nonlinear models [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Multivariable predictive control of a pressurized tank using neural networks

Duarte‐Mermoud

Suárez-García

Bassi

2005

Neural Comput & Applic

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The behavior of a multivariable predictive control scheme based on neural networks applied to a model of a nonlinear multivariable real process, consisting of a pressurized tank is investigated in this paper. The neural scheme consists of three neural networks; the first is meant for the identification of plant parameters (identifier), the second one is for the prediction of future control errors (predictor) and the third one, based on the two previous, compute the control input to be applied to the plant (controller). The weights of the neural networks are updated on-line, using standard and dynamic backpropagation. The model of the nonlinear process is driven to an operation point and it is then controlled with the proposed neural control scheme, analyzing the maximum range over the neural control works properly.

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Nonlinear Predictive Control of a Semi Batch Polymerization Reactor by an Extended DMC

Cited by 39 publications

References 1 publication

Theoretical analysis of unconstrained nonlinear model predictive control

Theoretical analysis of unconstrained nonlinear model predictive control

Generalization of Takagi-Sugeno Fuzzy Controller and its Application to Control of MMA Batch Polymerization Reactor

Multivariable predictive control of a pressurized tank using neural networks

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