Gabriel Hulkó

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

In this mini-tutorial some advanced engineering methods are presented for modeling and design of discrete-time control of distributed parameter systems described for control purposes by numerical structures over complex-shape 3D definition domains along with the software package Distributed Parameter Systems Blockset for MATLAB & Simulink - Third-Party MathWorks product, inspired by the boom of numerical analysis of dynamics of machines and processes within engineering practice. The techniques offered are completed by a user-friendly Web service tool, suitable for interactive solution of model control problems of distributed parameter systems via the Internet.

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

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.

Modelling and Control of Extruder Barrel Temperature Field

Abstract Modelling and control of the extruder temperature field is discussed, a process governed by a nonlinear partial differential equation. Utilizing finite element approximation a discretized input/output representation of the system is created with the inputs being powers of heaters and output the extruder temperature field. Local linearization is applied at the operating point and the controller is designed based on the lumped-input and distributed-parameter-output systems approach, using the time-space decoupling of systems dynamics.

Steel Billet Continuous Induction Heating – Numerical Model and Advanced Control

Nowadays, the achievement of proper steel billets temperature profile is not the only design priority of induction heaters for hot forming applications. Due to its high operating costs, its design is constantly improving in terms of electrical and thermal efficiency. Therefore the more efficient multi-coil design starts to be more used in industrial practice. Numerical model of mentioned heater based on partial differential equations were solved by finite element method in virtual software environment. Primary goal of computer modeling was to investigate the thermal dynamics of four-module heater working in steady-state operation regime. Obtained data were applied to design an advanced control circuit based on distributed parameter systems theory. This may open up the opportunity to make further progress in induction heaters design.

Control of groundwater remediation process as distributed parameter system

Pollution of groundwater requires appropriate technical solutions which can be deployed for remediation. For as much as the local groundwater contamination and its subsequent spread may result in contamination of drinking water resources or other disasters. This publication aims to design and demonstrate a model task of groundwater remediation process as distributed parameter system. The model problem consists of appropriately spaced soil region with water flow and transport of pollution which are modelled in the program MODFLOW and MT3DMS. Actuating quantities are generated by pumping wells. Control problem solution is supported by DPS Blockset for MATLAB & Simulink.

Modeling and Control of Temperature Field of the Secondary Cooling Zone in Continuous Casting of Steel as Distributed Parameter System

Abstract This paper presents some results on modeling and control of temperature field in a steel continuous casting, demonstrating the possibilities of using engineering methods for control of distributed parameter systems along with virtual engineering environments. A system to be controlled is considered as lumped-input and distributed-parameter-output system. Dynamics and control synthesis are decomposed into time and space components, respectively. Demonstrations of PID and adaptive model-based predictive control are being presented via cosimulation, utilizing virtual software environments for continuous casting simulation and distributed parameter control systems, respectively.

Robust Control of Distributed Parameter Systems with Demonstration in Casting Technology and MATLAB/Simulink/DPS Blockset Software Support

© 2012 Belavý et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Robust Control of Distributed Parameter Systems with Demonstration in Casting Technology and MATLAB/Simulink/DPS Blockset Software Support

Control of Distributed Parameter Systems - Engineering Methods and Software Support in the MATLAB & Simulink Programming Environment

Distributed parameter systems (DPS) are systems with state/output quantities X(x,t) /Y(x,t) – parameters which are defined as quantity fields or infinite dimensional quantities distributed through geometric space, where x – in general is a vector of the three dimensional Euclidean space. Thanks to the development of information technology and numerical methods, engineering practice is lately modelling a wide range of phenomena and processes in virtual software environments for numerical dynamical analysis purposes such as ANSYS www.ansys.com, FLUENT (ANSYS Polyflow) www.fluent.com , ProCAST www.esi-group.com/products/casting/, COMPUPLAST – www.compuplast.com, SYSWELD – www.esi-group.com/products/welding, COMSOL Multiphysics www.comsol.com, MODFLOW, MODPATH,... www.modflow.com , STAR-CD www.cd-adapco.com, MOLDFLOW www.moldflow.com, ... Based on the numerical solution of the underlying partial differential equations (PDE) these virtual software environments offer colorful, animated results in 3D. Numerical dynamic analysis problems are solved both for technical and non-technical disciplines given by numerical models defined in complex 3D shapes. From the viewpoint of systems and control theory these dynamical models represent DPS. A new challenge emerges for the control engineering practice, which is the objective to formulate control problems for dynamical systems defined as DPS through numerical structures over complex spatial structures in 3D. The main emphasis of this chapter is to present a philosophy of the engineering approach for the control of DPS given by numerical structures, which opens a wide space for novel applications of the toolboxes and blocksets of the MATLAB & Simulink software environment presented here.

PLC control of casting die preheating process as distributed parameter system

In the paper a problem of casting die preheating control is solved. Concept of the control is designed based on distributed parameters systems. The design of the control structures are created within simulation studies, afterwards are connected into co-simulation regime network to tune the control parameters and then applied to control of preheating using PLC.

Control of the preheating of a casting mould as a distributed parameter system by PLC

This paper deals with the control of the preheating process of a casting mould formulated as a distributed parameter system and using a programmable logic controller (PLC). The control process is first optimized by a simulation procedure using a distributed parameter control loop. The temporal part of the control synthesis is implemented on the PLC in order to control the preheating of the casting mould. A controlled preheating process is demonstrated on the experimental casting mould with embedded sensors and actuators.