Engineering Methods and Software Support for Modeling and Design of Discrete-time Control of Distributed Parameter Systems

Hulkó, G.; Belavý, C.; Mészáros, A.; Buĉek, P.; Ondrejkovic, Karol; Zajíček, P.

doi:10.3166/ejc.15.407-417

Cited by 13 publications

(9 citation statements)

References 2 publications

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“…Being thoroughly solidified and straightened, the strand is cut to lengths and prepared for further processing (8). Production quality is considerably int1uenced by temperature variations during cooling in secondary cooling zone.…”

Section: Introductionmentioning

confidence: 99%

“…'fOlRi (x ,oo) (13),(14) and (15) represent the best approximation of reference and controlled variables in k-th time step and steady-state, respectively. to(7) and(8). In block SS2 we obtain the vector oflumped quantities for time step k: {Y; Relating {Vi (k )} i = I ,5 with {1; (Xi ' k )} i = I ,5 transfer functions {SH,(x i ,z)} i = I ,5 were introduced (4).…”

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confidence: 99%

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Control of billet temperature field by the secondary cooling in continuous casting of steel

Ondrejkovic

Noga

Tkáč

et al. 2013

2013 International Conference on Process Control (PC)

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The paper presents simulation study on control of temperature field of steel billet during continuous casting. The cooling of steel billet is controlled with secondary cooling zone -a set of water jets divided into independent sub zones. The temperature field evolution in time and space is described by non-linear partial differential equation. The solution of this equation is obtained numerically, using specialized finite element software ProCAST. Lumped-input and distributed-parameter output system (LDS) is introduced, with the inputs being cooling water flow rates and the temperature field being the system's distributed output. Control synthesis in the system's feedback is solved in space and time direction. To represent the temperature field during setpoint changes a co-simulation interface was added to the DPS Blockset (Third-Party software product of The MathWorks). The interface block enables you to couple a finite element simulation in ProCAST and control scheme in Simulink.This feature brings you the full power of Simulink to extend the capabilities of finite element simulation.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Control of billet temperature field by the secondary cooling in continuous casting of steel

Ondrejkovic

Noga

Tkáč

et al. 2013

2013 International Conference on Process Control (PC)

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“…1). In recent works [2][3][4][5], engineering methods for the control of LDS have been developed, along with software support in the MATLAB & Simulink environment. The aim of this paper is to demonstrate the potential of these results for realizing innovations in ES control methodology and development of software support for control of ES as LDS.…”

Section: Introductionmentioning

confidence: 99%

Control of energy systems as distributed parameter systems with software support by virtual software environments

Hulkó

Rohal’-Ilkiv²,

Noga³

et al. 2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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Thanks to development of information technology, the so-called virtual software environments offer wide possibilities for the estimation of time-space dynamical characteristics of energy systems, including modeling, control and design of distributed parameter systems. Based on these advances, we present a novel approach to control energy systems as lumped-input and distributed-parameter-output systems. An adaptive-predictive controller is deployed to control temperature fields to ensure optimal conditions for the desulphurization process in a coal-burning fluidized bed furnace, demonstrating the potential of the proposed methodology.

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“…Static simulations may misrepresent the real dynamic properties of the controlled structures-especially when the system is lightly damped, oscillating and underactuated. The use of methods based on a set of distributed step responses is rather suited for systems with non-oscillatory and slower dynamics, such as assessing temperature fields in metallurgical processes [48,49]. …”

Section: Static Loadingmentioning

confidence: 99%

Laboratory Demonstration Hardware for AVC

Takács

Rohal’-Ilkiv

2012

Model Predictive Vibration Control

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Engineering Methods and Software Support for Modeling and Design of Discrete-time Control of Distributed Parameter Systems

Cited by 13 publications

References 2 publications

Control of billet temperature field by the secondary cooling in continuous casting of steel

Control of billet temperature field by the secondary cooling in continuous casting of steel

Control of energy systems as distributed parameter systems with software support by virtual software environments

Laboratory Demonstration Hardware for AVC

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