In already installed fieldbus instrumentations, a configuration-free and manufacturer-independent access to field device information sets is still missing. Due to the lack of suitable and simple models, as well as of ...
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In already installed fieldbus instrumentations, a configuration-free and manufacturer-independent access to field device information sets is still missing. Due to the lack of suitable and simple models, as well as of interaction patterns of field devices in various information worlds of processcontrolengineering, the practical use made of device novelties is rather complex. This affects high investments for engineering this information in various applications of processcontrolengineering. Modern field devices contain embedded information sets that are ever-increasing, and provide great opportunity for innovative asset management functionality. In this paper we present an information server, serving as an additional information channel between field and plant level in process industry. We show that this so-called asset management box provides the configuration-free analysis of fieldbus segments, the open access to installed field devices and the automatically updated, structured and self-descriptive presentation of their embedded information sets. Its basic functionality fulfills one prerequisite of an efficient asset management system: the analysis of the actual state of a fieldbus installation, without prior knowledge about the installed field devices and enhanced investments in engineering. We discuss several asset management applications, implemented on the asset management box. To prevent excessive investments for engineering of asset management applications themselves, widest flexibility and extensibility of the management systems' functionality is required. The box's structure permits easy extensibility of plant and client-specific applications as well as the easy integration of specific fieldbus hardware, the box provides a universal fieldbus interface.
For predictive control in industry often very long horizons for control error and manipulated signal are used because of the slow processes which take place in the petrochemical industry. In order to reduce the comput...
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For predictive control in industry often very long horizons for control error and manipulated signal are used because of the slow processes which take place in the petrochemical industry. In order to reduce the computational effort some commercial predictive control program packages offer the ability to reduce the number of points in both horizons but do not recommend how to select the points which have to be considered in the horizon of the control error and manipulated variable. In this work the authors introduce an optimal choice not only of the horizon lengths itself but also for the strategy of reducing the number of points in the horizons. A genetic optimization algorithm was used both for the search for the optimal length of the horizons and for the best allocation of the points in the horizons. The results of the optimization process where used to deduct a simple rule.
Most industrial processes are nonlinear. In such a case only a nonlinear model valid for the whole working area can ensure a good controller design. The nonlinear process is approximated by a multi-model consisting of...
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Most industrial processes are nonlinear. In such a case only a nonlinear model valid for the whole working area can ensure a good controller design. The nonlinear process is approximated by a multi-model consisting of the intelligent combination of some linear sub-models. As a very practical way the following identification strategy was used: independent model parameter estimation in the different working points and the calculation of the global valid model output as the weighted sum of the sub-models. As a weighting function the Gaussian function is used. The parameters of the Gaussian function were chosen either without or with optimization of the identification cost function. The global valid nonlinear model was used for model based predictive control. A heat exchanger example illustrates the method.
The paper presents a control system design technique in delta domain for IMC (Internal Model control) structure. Also a generalised form for delta domain Dead-beat control algorithm is given, a hybrid implementation o...
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The paper presents a control system design technique in delta domain for IMC (Internal Model control) structure. Also a generalised form for delta domain Dead-beat control algorithm is given, a hybrid implementation of the controller (Z-domain combined with delta domain) is presented and its architecture is compared with the pure deltadomain implementation. The effect of placing the limitations in the IMC structure is also studied. The hybrid control structure is compared with a similar Dead-beat controller in Z-domain for second and third order plants (benchmarks). An illustrative sensitivity analysis between the hybrid control system and the Z-domain system has been performed.
The degree of machine automation is continuously increasing, because computer technology, control software, telecommunication technology and visualization systems are also continuously improved. This makes the use of ...
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To substantially improve operational control of complex industrial plants, it is important to be able to predict the influences which process variables have on quality-relevant target variables. The determination of i...
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This paper investigates the possibility of computing the actual position of a passenger car based on different independent internal signals available in a production car. A new front wheel based dead reckoning approac...
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For many practical applications, a combination of theoretical and experimental modelling appears feasible. Qualitative knowledge about the most significant effects are often known or easily accessible. This contributi...
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This paper presents the design of a model-based supervision and diagnosis system for hydraulic and future electro-hydraulic passenger car braking systems. A state space model of the braking system has been derived, wh...
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To substantially improve operational control of complex industrial plants, it is important to be able to predict the influences which process variables have on quality-relevant target variables. The determination of i...
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To substantially improve operational control of complex industrial plants, it is important to be able to predict the influences which process variables have on quality-relevant target variables. The determination of influencing relationship and also the delay-time behaviour of various industrial processes makes it difficult to synthesis a quality prediction system. A hybrid modeling method combined with a suitable classification of process characteristics ensures a widespread model synthesis for quality prediction.
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