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Fuzzy Model-Based Predictive control of a Heat Exchanger

IFAC Proceedings Volumes 1997年第25期30卷 409-414页

作者： Martin Fischer Oliver Nelles Rolf Isermann Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Systems Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Tel: +49/6151-165109 Fax: +49/6151-293445

In this contribution, fuzzy model-based predictive control is applied to temperature control of a cross-flow water/air heat exchanger. After a review of Takagi-Sugeno type fuzzy systems and their identification from measurement data the on-line adaptation of these models is discussed. If the nonlinearity of the process is assumed to keep its structure, the linear parameters in the rule consequents can be locally updated by a recursive least-squares algorithm. Since this algorithm is computationally inexpensive it can be utilized in nonlinear model predictive control (NMPC). If the process is influenced by unmeasurable or unmodeled disturbances, the fuzzy model is adapted to these quantities. It can be distinguished between disturbances and model deficiencies which require the adaptation of all linear parameters in the rule consequents and model offsets in the static mapping which can be canceled by local adaptation of one offset parameter per rule. The second case is particularly simple to implement because there is no need for persistent excitation. Moreover, this mode of operation copes with the challenges in control which are specific to the heat exchanger. The effectiveness of the adaptive nonlinear model predictive controller is proven by application to an industrial-scale pilot plant.

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Exploiting Prior Knowledge in Fuzzy Model Identification of a Heat Exchanger

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IFAC Proceedings Volumes 1997年第25期30卷 391-396页

作者： Martin Fischer Oliver Nelles Rolf Isermann Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Systems and Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Tel: +49/6151-165109 Fax: +49/6151-293445

Intelligent automation systems integrate a great variety of methods based on models of the controlled processes, e. g. model-based control or model-based supervision and fault diagnosis. Hence, there emerges a great demand for modeling techniques suitable for the identification of nonlinear dynamic systems. Besides classical approaches, neuro/fuzzy systems appear promising due to their approximation qualities. In this contribution, it is investigated how Takagi-Sugeno type fuzzy systems can be utilized for identification of a cross-flow water/air heat exchanger. On the one hand, these models provide transparency and interpretability which allows to incorporate qualitative and quantitative prior knowledge about the plant. On the other hand, Takagi-Sugeno models can also be identified from measurement data in order to compensate for incomplete system knowledge. Data gathering requires appropriate excitation of the system. It will be shown how elementary prior knowledge can be exploited for the design of identification signals. The concept of error bars is reviewed, and it is used to roughly evaluate the model quality depending on the properties of the excitation signal. For identification from training data the local linear model tree algorithm (LOLIMOT) is applied.

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automatic Model Selection in Local Linear Model Trees (LOLIMOT) for Nonlinear System Identification of a Transport Delay process

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IFAC Proceedings Volumes 1997年第11期30卷 699-704页

作者： Oliver Nelles Oliver Hecker Rolf Isermann Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +49/6151/16-4524 Fax: +49/6151/293445 th-darmstadt. de

In this paper a new method for nonlinear system identification is proposed. It is based on local linear models constructed by a tree algorithm in combination with a subset selection technique for determination of the local linear models’ structure. The local linear model tree can be interpreted as a Takagi-Sugeno fuzzy model, where the tree algorithm constructs the rule premises, the input membership functions and allows easy control of the model’s complexity (number of rules) while the subset selection technique determines the rule conclusions. The selection of the local linear model structure allows an automatic choice of different model orders and dead times in different operating regions. The capability of this approach to model a real world process with operating point dependent dead times and time constants is demonstrated.

关键词： identification nonlinear local linear models tree structure subset selection

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A neural network controls the galvannealing process

A neural network controls the galvannealing process

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： C. Schiefer F.X. Rubenzucker H.P. Jorgl H. Aberl Laboratory for Intelligent Control Methods for Process Technologies Institute for Machine and Process Automation Vienna University슠of슠Technology Vienna Austria Electrical Engineering and Process Control Research and DevelopmentDepartment TAW Voest-Alpine Industrieanlagenbau GmbH Linz Austria

High quality galvanized steel strip is a need of today's manufacturers of various products. In particular, in the top quality section steel strips for the automotive, building and consumer goods industries only those steel producers will be successful who are applying state-of-the-art process technologies. For this reason, VOEST-ALPINE Industrieanlagenbau mbH (VAI) and VOEST-ALPINE Stahl Linz developed a new galvannealing control system to optimize this metallurgical process. As the latest improvement of the galvannealing control strategy, a neural network controller has been developed by VAI in Cooperation with the Vienna University of Technology Christian Doppler laboratory for Intelligent control Methods for process Technologies. The paper describes the galvannealing process as far as it is necessary for the understanding of the controller functions, the controller structure and its essential functions. Furthermore, the neural network structure used and its integration into the controller system are explained. A discussion of simulation and practical operating results shows the improvements achieved by using a neural network controller in comparison to the conventional controller.

关键词： Neural networks process control Steel Strips Electrical equipment industry Manufacturing industries Metals industry control systems Galvanizing Automotive engineering

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Robustness Properties of Extended Horizon Nonlinear Predictive control Strategies Applied for the Hammerstein Model

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IFAC Proceedings Volumes 1997年第16期30卷 321-325页

作者： Robert Haber Ruth Bars Zoltán Czipríán Department of Process Engineering Laboratory of Process Control Cologne Institute of Technology D-50679 Köln Betzdorfer Str. 2 Germany Department of Automation Technical University of Budapest H-1111 Budapest Goldmann Gy. tér 3 Hungary

Predictive control algorithms are promising also in the case of nonlinear systems. Two versions of extended horizon predictive control algorithms are given here for the nonlinear simple Hammerstein model. A quadratic cost function is minimized, which considers the quadratic deviation of the reference signal and the output signal predicted in a future point and also the squares of the control increments. The system model is transformed to a predictive incremental form. Two versions of suboptimal extended horizon control algorithms are given with different assumptions for the control signal during the control horizon. Robustness properties of these algorithms are considered in case of plant-model mismatch through some simulation examples.

关键词： Predictive control nonlinear control control algorithms suboptimal control robustness Hammerstein model

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Robust Design of PID and IMC-Based controllers in the Time Domain

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IFAC Proceedings Volumes 1997年第16期30卷 365-370页

作者： Robert Haber Ruth Bars Department. of Plant and Process Engineering Laboratory for Process Control Cologne Institute of Technology (Fachhochschule Köln) D-50679 Köln Betzdorfer Str. 2 Germany Department of Automation Technical University of Budapest H-1111 Budapest Goldmann Gy. ter 3 Hungary

A third-order proportional process model with parameter uncertainty is controlled by different simple controllers. First the robustness of four different PID-control designs is investigated. Then internal model control (IMC) is designed in the time domain for the exact process model and for its approximating model with a first-order lag and a delay time. Finally, the PID-control of the nominal model is extended by an IMC-like feedback, which makes the control especially robust.

关键词： Robust control PID control internal model control controller design

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On the design and control of mechatronic systems - A survey

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 1996年第1期43卷 4-15页

作者： Isermann, R Institute of Automatic Control Laboratory for Control Engineering and Process Automation Technical University of Darmstadt Darmstadt Germany

The integration of mechanical systems and microelectronics opens new possibilities for mechanical design and automatic functions. After a discussion of the mechanical and electronic design the organization of information processing in different levels is described, Within this frame ''low-degree intelligent'' mechatronic systems can be developed which comprise adaptive control, supervision with fault diagnosis, and decisions with regard to further actions. This requires the realization of knowledge-based systems with learning abilities. Some aspects of the design of information processing including modeling and estimation, control, and supervision methods are considered. Finally as example an adaptive semiactive shock absorber for vehicle suspension systems is shown.

关键词： control systems Mechatronics Information processing automatic control Mechanical systems Microelectronics Adaptive control Fault diagnosis Knowledge based systems process design

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Fuzzy systems design based on a hybrid neural structure and application to the fault diagnosis of technical processes

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control engineering PRACTICE 1996年第1期4卷 35-42页

作者： Ayoubi, M Technical University of Darmstadt Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg 4 64283 Darmstadt Germany

A novel structure which models the fuzzy inference mechanism based on neural units is proposed, to combine both the adaptive feature of neural networks and the transparency of fuzzy systems. It is shown how a perceptron with a sigmoidal activity function can perform the aggregation of premise antecedents and can thus implement conjunction or disjunction operations depending on the neuron's threshold. Knowledge-base parameters such as relevance weights of antecedents and priority weights of rules are introduced and discussed. The network topology is extracted by means of a coincidence learning law, the so-called Hebbian rule, in order to limit the problem of high dimensionality known by local classifiers. Two real-world problems are reported: Monitoring of the state of a turbocharger on the basis of model-based symptoms, and the supervision of air pressure in vehicle wheels, based on physically extracted symptoms.

关键词： fault diagnosis rule extraction neuro-fuzzy structure Hebbian learning turbocharger vehicle wheels

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Information processing for mechatronic systems

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ROBOTICS AND AUTONOMOUS SYSTEMS 1996年第2期19卷 117-134页

作者： Isermann, R Institute of Automatic Control Laboratory for Control Engineering and Process Automation Technical University of Darmstadt Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany

The integration of mechanical systems and microelectronics opens many new possibilities for process design and automatic functions. After discussing the mutual interrelations between mechanical and electronic design the different ways of integration within mechatronic systems and the resulting properties are described. The information processing can be organized in multilevels, ranging from low-level control, through supervision to general process management. In connection with knowledge bases and inference mechanisms intelligent control systems result. The design of control systems for mechanical systems is described, from modeling, identification to adaptive control for nonlinear systems. This is followed by solving supervision tasks with fault diagnosis. Then design tools for mechatronic systems are considered and examples of applications are given, like intelligent control of an electromechanical throttle actuator and force and torque reconstruction for a robot.

关键词： mechatronic systems intelligent control nonlinear control adaptive control supervision fault diagnosis hardware-in-the-loop simulation intelligent automobile actuators torque reconstruction robot control

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Fault detection in milling, using parameter estimation and classification methods

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control engineering PRACTICE 1996年第11期4卷 1573-1578页

作者： Konrad, H Institute of Automatic Control Technical University of Darmstadt Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str.4 D-64283 Darmstadt Germany

In this paper a new method of fault detection in milling is reported. Based on measured cutting forces, model parameters are estimated for each insert of the milling cutter. Using a classifier, the patterns of these estimated parameters are processed further, and the state of the milling process is determined. The method was first tested with simulated data, and then verified with measurements on a machining center.

关键词： machine tools cutting failure detection neural networks

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