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Nonlinear Identification and control of a Thermal Plant Based on Multi-Layer Perceptron Networks with Locally Distributed Dynamics

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IFAC Proceedings Volumes 1997年第25期30卷 359-366页

作者： Mihiar Ayoubi Darmstadt University of Technology Lab. of Control Engineering and Process Automation Landgraf-Georg Str. 4 64283 Darmstadt Germany

This paper addresses the application of neural networks with distributed dynamics to the identification and control of nonlinear systems. The prime concern is to acquire a multi-input, nonlinear neural model which can be used to predict the water outlet temperature of a heat exchanger. The neural model is used to control of the outlet temperature based on the predictive control scheme. The heat exchanger possesses a significant static nonlinearity, minor dynamic nonlinearity and dead times depending on the operating point.

关键词： Dynamic Neural Networks Nonlinear Identification Predictive control Thermal Plant

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Nonlinear predictive control based on the extraction of step-response models from Takagi-Sugeno fuzzy systems

Nonlinear predictive control based on the extraction of step...

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American control Conference (ACC)

作者： M. Fischer M. Schmidt K.K. Kavsek-Biasizzo Institute of Automatic Control Laboratory of Control Systems and Process Automation Technical University of Darmstadt Darmstadt Germany Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia

This paper deals with nonlinear predictive control based on higher order Takagi-Sugeno fuzzy systems which can also be interpreted as generalized radial basis function networks. We investigate how the fuzzy models can be linked to a special type of model based predictive control algorithm, namely the dynamic matrix control (DMC). Previously, purely linear step response models were used for long-range prediction. Here, the method is extended to nonlinear processes. Therefore, various step responses for different operating points are extracted from the fuzzy model. For performance evaluation, a heat exchanger is identified by means of the local linear model tree algorithm and controlled by the modified DMC.

关键词： Predictive control Predictive models Takagi-Sugeno model Fuzzy systems Automatic control Laboratories Radial basis function networks Fuzzy control control system synthesis Nonlinear control systems

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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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Detection and Isolation of Sensor Faults for a Heat Exchanger Based on Local Linear Fuzzy Models

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IFAC Proceedings Volumes 1997年第25期30卷 525-530页

作者： Peter Ballé Oliver Nelles Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University of Darmstadt Landgraf-Georg-Str. 4 64283 Darmstadt Germany Phone: +49/6151/16-2314 Fax: +49/6151/293445

The development of a reliable fault detection and isolation (FDI) scheme for nonlinear processes is often laborious and difficult to achieve due to the complexity of the system. Neural networks and fuzzy models, able to approximate nonlinear dynamic functions offer a powerful tool to cope with this problem. In this paper, a multi-model approach for FDI of sensor faults on a highly nonlinear real world processes is introduced. The approach is based local linear fuzzy models and provides residuals which are structured in a way that detection of six and isolation of five different sensor faults over all ranges of operation becomes possible. The approach enables on-line supervision of the process. Furthermore, a sensitivity analysis of the residuals to different faults can be made, based on the parameters of the fuzzy models.

关键词： fault detection/isolation fuzzy models parallel/series-parallel model heat exchanger

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Identification with Dynamic Neural Networks - Architectures, Comparisons, Applications

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IFAC Proceedings Volumes 1997年第11期30卷 947-972页

作者： Rolf Isermann Susanne Ernst Oliver Nelles 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

An overview of presently known neural networks for the identification of nonlinear dynamic systems is given. The suitable networks are classified where two different approaches, the external and internal dynamic networks, have to be distinguished. One alternative is to apply standard static neural networks and incorporate the dynamics by providing the network with information about previous inputs and outputs created by external linear filters. Another possibility is to include dynamic elements within the neural network structure and therefore making the network itself a nonlinear dynamic system. The principles, advantages and drawbacks of both approaches are pointed out. Two special neural network architectures, one with external and one with internal dynamics, are considered in more detail. Their capability to identify complex nonlinear dynamic real-world processes is studied. Experimental results for two multi-variable processes are shown, a combustion engine turbocharger and an industrial scale tubular heat exchanger.

关键词： dynamic neural networks nonlinear systems dynamic systems identification

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Knowledge-Based Structures for Fault Diagnosis and Its Applications

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IFAC Proceedings Volumes 1997年第27期30卷 1-18页

作者： Rolf Isermann Institute of Automatic Control Laboratory of Control Engineering and Process Automation Darmstadt University of Technology Landgraf-Georg Str. 4 D-64283 Darmstadt Germany Phone: +49 (6151) 16-7412 FAX: +49 (6151)293604

The operation of technical processes requires increasingly advanced supervision and fault diagnosis to improve reliability, safety and economy. This paper describes structures for advanced methods of fault detection and diagnosis. It begins with a consideration of a knowledge-based procedure which is based on analytical and heuristic information. Then different methods of fault detection are considered, which extract features from measured signals and use process and signal models. These methods are based on parameter estimation, state estimation and parity equations. By comparison with the normal behaviour, analytic symptoms are generated. Human operators may be a further source of information, and support the generation of heuristic symptoms. For fault diagnosis, all symptoms have to be processed in order to determine possible faults. This can be performed by classification methods or approximate reasoning, using probabilistic or possibilistic (fuzzy) approaches based on if-then-rules. The application of these methods is shown for the fault detection and diagnosis of Dieselengines.

关键词： Fault detection supervision monitoring parameter estimation parity equations technical processes actuators sensors fault diagnosis diagnostic reasoning fuzzy reasoning wheel suspension electrical mechanical actuator

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Adaptive Predictive control Based on Local Linear Fuzzy Models

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IFAC Proceedings Volumes 1997年第11期30卷 819-824页

作者： Martin Fischer Oliver Nelles 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: +49/6151/16-5109 Fax: +49/6151/293445

In this paper, local linear fuzzy models are used for adaptive predictive control of nonlinear processes. First, an algorithm (LOLIMOT = local linear model tree algorithm) for off-line identification of Takagi-Sugeno type fuzzy models is briefly reviewed. In order to cope with time-variant nonlinear processes and also with the influence of non-measurable disturbances, an on-line adaptation of the model is performed. A recursive least-squares algorithm (RLS) is applied to update the linear parameters in the rule consequents locally. The local estimation results in low computational effort and avoids forgetting in non-active operating regimes. The effectiveness of the approach is demonstrated by simulations and by controlling the output temperature of a cross-flow heat exchanger.

关键词： nonlinear control predictive control adaptation fuzzy models heat exchangers

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Model and Signal Based Supervision of Components of Cars

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IFAC Proceedings Volumes 1997年第8期30卷 479-484页

作者： Thomas Weispfenning Institute of Automatic Control Laboratory of Control Engineering and Process Automation Darmstadt University of Technology Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +49(6151)16-7412 FAX: +49(6151)293604

In this paper methods for fault diagnosis of car components are presented. Model based methods like parameter estimation can be used for an estimation of the process coefficients. By dividing the nonlinear characteristic curve into several sections good estimation results can be achieved and a diagnosis of the shock absorber be performed. Signal spectra estimation for detection of tire pressure loss requires only an accelerometer to measure the vertical acceleration of the wheel. The presented results were obtained with measured data drawn from a test rig and a driving car. The proposed methods are suited for an on-line supervision of the car as well as for technical inspection e.g. in workshops.

关键词： fault detection supervision vehicle suspension signal analysis parameter estimation

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Orthonormal Basis Functions for Nonlinear System Identification with Local Linear Model Trees (LOLIMOT)

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IFAC Proceedings Volumes 1997年第11期30卷 639-644页

作者： Oliver Nelles 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

A new approach for identification of nonlinear dynamic systems is proposed. It is based on a combination of generalized orthonormal basis functions and local linear model trees (LOLIMOT). The main idea is to approximate an unknown function from data by the interpolation of many local linear models. The number of local linear models and their validity regions are determined by a tree construction algorithm that partitions the input space by axisorthogonal cuts. The parameters of the local linear models are estimated by minimizing a equation error criterion (ARX models). These local parametric models are replaced by linear parameterized output error models based on generalized orthonormal basis functions. Thus, stability of the nonlinear dynamic model can be proven and the low bias property of output error models can be exploited. Furthermore, a subset selection technique may be applied for choosing the complexity of each local linear model. Since different basis functions may be constructed for each local linear model, processes with operating condition dependent dynamic behavior can be modeled efficiently.

关键词： orthonormal basis functions nonlinear system identification local linear models

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Fault Detection of Vehicle Suspensions

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IFAC Proceedings Volumes 1997年第18期30卷 801-806页

作者： Thomas Weispfenning Rolf Isermann Institute of Automatic Control Laboratory of Control Engineering and Process Automation Darmstadt University of Technology Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +49(6151)16-7412 FAX:+49(6151)293604

In this paper methods for fault detection and diagnosis of vehicle suspensions are presented. With parameter estimation and parity equations symptoms on the current process status can be extracted. By means of parameter estimation detailed symptoms can be generated, allowing an automatic distinction of different faults. For the automatic classification, neural networks were trained. Parity equations are well suited for a detection of sensor faults, although they do not offer the possibility for a distinction of different faults. All the presented results were obtained with measured data drawn from a test rig and a driving car. The proposed methods are suited for an on-line supervision of the car as well as for technical inspection e.g. in workshops.

关键词： fault detection parameter estimation parity equation vehicle suspension tire pressure application results

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