Predictive Control in Process Engineering

This paper studies simulations of a possible appliance of an explicit form of the model predictive control method. Targeted system is a heat exchanger burdened with a time-delay. An approach is formulated within the MATLAB/SIMULINK development environment as well as the resulting simulation. The conclusion evaluates resulting system behaviour with focus on conditions in areas of practice benefiting from its use.

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“…The following section describes a predictive control approach based on state-space internal model (Haber et al, 2011).…”

Section: Model Predictive Controlmentioning

confidence: 99%

Method Of Explicit Predictive Control In Case Of Time-Delayed Heat System

Talas¹,

Bobál²,

Krhovjak³

et al. 2015

ECMS 2015 Proceedings Edited By: Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova

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“…1. ) Utilizing the dynamic decoupling procedure presented in section II for described above plant operating points ( o x , o u ) we obtain state feedback matrices F of the following structure 14 15 16 75…”

Section: B Neural Controller Synthesismentioning

confidence: 99%

“…One can also find some works for linear time variant (LTV) [8][9][10] but it is still an open question for nonlinear plants. These need a global linearization [5,[11][12][13], which may be done for square plants, or the dynamic decoupling may be obtained by the use of some nonlinear predictive controllers [14][15][16]. However, frequently these methods are computationally unrealizable when used on-line.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for a dynamic decoupling of a nonlinear MIMO dynamic plant

Dworak

Jaroszewski

2015

2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR)

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In the paper a method of synthesis of a neural controller which goal is to reduce effects of coupling of the nonlinear multi-input multi-output (MIMO) plant inputs and outputs is presented. The designed neural controller contains a set of neural nets that determine values of parameters of linear decoupling controllers calculated for the adopted nonlinear plant model at its operating points. A known dynamic decoupling technique is used to generate training and evaluation data for the synthetized neural nets. The resulting neural (gain-scheduling) controller varies its parameters depending on the current plant operating point. I. 788978-1-4799-8701-6/15/$31.00 ©2015 IEEE.

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“…3. Predictive model reference control trajectory is known, a series of predicted error values over a given prediction horizon can be used to improve the control system performances, similarly to predictive PID control [18]. A block diagram of the predictive model reference control is shown in Fig.…”

Section: Predictive Model Reference Control Designmentioning

confidence: 99%

“…, u(k + d + n e2 |k) = u(k|k) can be considered. The predictor can be realized based on (17) and (18). The output of the Laguerre model of the system for IMC control can be calculated by the following relationship y r (k) = ηL(k) .…”

Section: Predictionmentioning

confidence: 99%

Predictive Synchronous Generator Excitation Control Based on Laguerre Model

Skultety¹,

Miklovičová²,

Bars³

2013

Journal of Electrical Engineering

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In this paper the synchronous generator excitation control is designed using the model reference control approach and its predictive extension. The control objective is to satisfy requirements on the synchronous generator performances imposed by the transmission system operator. The structure of both controllers is expressed in the form of modified Laguerre network. The efficiency of the proposed control design procedure is illustrated by simulation of the 259 MVA synchronous generator of the nuclear power plant Mochovce in Slovakia.K e y w o r d s: model reference control, predictive control, Laguerre model, synchronous generator

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Predictive Control in Process Engineering

Cited by 47 publications

References 0 publications

Method Of Explicit Predictive Control In Case Of Time-Delayed Heat System

Method Of Explicit Predictive Control In Case Of Time-Delayed Heat System

Neural networks for a dynamic decoupling of a nonlinear MIMO dynamic plant

Predictive Synchronous Generator Excitation Control Based on Laguerre Model

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