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International Workshop on Advanced Motion control (AMC)

作者： M. Fischer M. Tomizuka Institute of Automatic Control Laboratory of Control Systems and Process Automation Technical University of Darmstadt Darmstadt Germany Department of Mechanical Engineering University of California Berkeley Berkeley CA USA

High-accuracy positioning is applied in a variety of modern computer-controlled machines. The achievable precision is not only determined by the mechanical properties of the systems but strongly depends on the utilized control algorithms and the quality of the sensor signals. The objective of this paper is to demonstrate how alternative position sensors influence the performance of a robust digital tracking controller consisting of a disturbance observer in the velocity loop, a feedback controller in the position loop, and a zero phase error tracking controller as feedforward controller. Two different sensor systems for an x-y positioning table are considered. While a digital encoder is attached to the actuating motor, a laser interferometer with a significantly higher resolution directly measures the position of the compliantly coupled table. The latter case represents a noncollocated system. After introducing the hardware setup, both the system identification and the controller design are briefly reviewed. The impact of the measurement device on the control performance and the optimal choice of the controller parameters are investigated in extensive experiments.

关键词： Mechanical sensors Velocity control control systems Sensor systems Tracking loops Optimal control Application software Mechanical factors Robust control Digital control

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Model-Based and Signal-Based Fault Detection of Diesel Engines with Turbocharger

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IFAC Proceedings Volumes 1996年第1期29卷 7832-7837页

作者： Matthias Schüler Steffen Leonhardt Christof Ludwig Mihiar Ayoubi Rolf Isermann Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University of Darmstadt Landgraf-Georg Str. 4 D-64283 Darmstadt Germany Phone: +49 (6151) 16-7412 FAX: +49 (6151) 293604

Rising demands in automotive development and strict emission standards enforce the application of modern conrrol and supervision strategies to combustion engines. This contribution shows the shaping and adaption of model based fault detection and direct signal analysis methods when applied to a turbocharged diesel engine. First a real lime supervision of fuel mass and injection angle based on dynamic cylinder pressure measurement is described. This is followed by a method for engine misfire detection using only a low resolution crankshaft speed signal. Then fault detection for a diesel engine turbocharger with nonlinear neural networks is proposed. Finally the results of a diagnosis of multiple faults with a neural network are presented. All methods have been implemented and tested experimentally on a dynamical engine test stand at the Technical University of Darmstadt.

关键词： diesel engine fault detection fault diagnosis neural networks state estimation supervision

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Model-Based Identification of a Vehicle Suspension Using Parameter Estimation and Neural Networks

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IFAC Proceedings Volumes 1996年第1期29卷 4510-4515页

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

A two-step scheme for identification of a vehicle suspension is presented which combines parameter estimation and neural networks for approximation. At first, the parameters of the discrete time transfer function are estimated using a RLS-algonthm. These parameters are nonlinear functions of the physical coefficients, but a direct calculation of these is often not possible or leads to large errors due to the nonlinear amplification of noise. Therefore, to approximate the coefficients, a nonlinear mapping using a RBF network is performed. For training of the network and to test generalization abilities, the coefficients of a vehicle suspension were varied. The study shows that an approximation of the physical coefficients by application of the presented scheme is possible. The method was tested by simulated data and measurements from a test rig at the Technical University of Darmstadt.

关键词： identification neural networks parameter estimation vehicle suspension supervision fault diagnosis

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A New Technique for Determination of Hidden Layer Parameters in RBF Networks

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IFAC Proceedings Volumes 1996年第1期29卷 4380-4385页

作者： Oliver Nelles Rolf Isermann Technical University of Darmstadt 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: +49/6/5//293445

Radial basis function (RBF) networks are often used for identification of nonlinear dynamic systems. The main reason why RBF networks are so successful is that the hidden layer parameters can be fixed in a very reasonable way and only the weights are optimized by a standard least squares technique. Thus for the case of Gaussian RBFs a good choice of the centers and standard deviations is crucial for good network performance. We show that Ihe most widely used clustering approach has many drawbacks. An alternative technique for center determination is presented, that is not completely unsupervised but exploits error information. It is based on a fusion of linear parameter estimation and the RBF network. First, a linear system is estimated from data. Then only the nonlinear part is approximated by an RBF network.

关键词： identification nonlinear dynamic systems radial basis functions clustering

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Extended Horizon Predictive control of Non-Linear Systems - Multi-Dimensional Optimisation and Suboptimal Solution

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IFAC Proceedings Volumes 1996年第8期29卷 57-62页

作者： Robert Haber Ruth Bars Department of Process Engineering Laboratory of 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. tér 3 Hungary

Predictive control strategies have been proved effective and robust in practice. These algorithms were developed mainly for linear processes. The main idea is to determine the control signal minimising the deviation between a reference signal and the predicted process output in a given prediction horizon. A simplified version is one-step-ahead predictive control, where prediction horizon is restricted to one future point. Extended horizon control proposed by Ydstie (1984) is enlengthened one-step-ahead control which lets more time for the settling process than the dead time. Extended horizon predictive control strategy is derived here for a class of nonlinear systems. Some simulation results show the effect of the tuning parameters for the reference signal tracking performance of the nonlinear system.

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AN OBSERVER AND SIGNAL-processING APPROACH TO FDI FOR THE INDUSTRIAL ACTUATOR BENCHMARK TEST

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control engineering PRACTICE 1995年第12期3卷 1741-1746页

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

In order to apply the best fault-detection and diagnosis scheme, it is required to investigate the process model profoundly and the kinds of faults to be detected. Especially, the process excitation and the effect of the fault being considered play an important role. This is the starting point for the choice of one of the various model-based fault-detection methods. According to this strategy, two different approaches, an observer-based and a signal-based approach, are selected for the two given faults of the benchmark task. It is shown that the use of adaptive thresholds can significantly improve the performance of the fault-detection scheme with respect to the false alarm rate and the delay in detection.

关键词： FAULT-DETECTION FAULT-ISOLATION ACTUATORS INDUSTRIAL control ADAPTIVE THRESHOLDS OBSERVERS BENCHMARK TESTING ANALYTICAL REDUNDANCY

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Dynamic neural units for nonlinear dynamic systems identification 3rd

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International Workshop on Artificial Neural Networks

作者： Ayoubi, M Schafer, M Sinsel, S Technical University of Darnstadt Inst. of Automatic Control Laboratoty of Control Engineering and Process Automation LandgraJ:Georg 4 Darmstadt 64283 Germany

ISBN: (纸本)3540594973

An attempt has been made to establish a time-discrete neuron model which ir applied to build Radial Basis Function and Multilayer Perceptron networks with distributed dynamics. The well known delta-rule is extended to the dynamic delta-rule in order to optimize network parameters. Both network types were used to identify empirical parametrical models of a turbocharger of a Diesel engine which comply with the demanded accuracy properties to a high degree. The performance of both network types is compared according to required number of parameter approximation accuracy and computational effort.

关键词： Diesel engines

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Identification and supervision of a thermal plant based on multi-layer perceptron networks with locally distributed dynamics

Identification and supervision of a thermal plant based on m...

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IEEE Conference on Decision and control

作者： M. Ayoubi R. Isermann J. Huber Institute of Automatic Control Laboratory of Control Engineering & Process Automation Technical University of Darmstadt Darmstadt Germany

This paper considers the application of neural networks with distributed dynamics to the identification of nonlinear systems. The primary objective is to establish a neural model bank which generates prediction errors. These estimation residuals can be treated as analytic symptoms to supervise the plant operating state. The practical approach applicability has been illustrated using a thermal plant. Here, two sensor faults of interest are localized by means of the so-called residual pattern.

关键词： Multilayer perceptrons Lab-on-a-chip Neural networks Neurons Nonlinear dynamical systems Fault detection Nonlinear filters Transversal filters Automatic control control engineering

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Identification of nonlinear dynamic systems classical methods versus radial basis function networks

Identification of nonlinear dynamic systems classical method...

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American control Conference (ACC)

作者： O. Nelles R. Isermann Institute of Automatic Control Laboratory of Control Engineering and Process Automation Darmstadt University of Technology Darmstadt Germany

This paper compares radial basis function networks for identification of nonlinear dynamic systems with classical methods derived from the Volterra series. The performance of these different approaches, such as Hammerstein, Wiener and NDE models, is analysed. Since the centres and variances of the Gaussian radial basis functions will be fixed before learning and only the weights are learned, a linear optimization problem arises. Therefore training the network and parameter estimation becomes comparable in computational effort. It is shown that the classical methods can compete or even perform better than the neural network, if the assumptions for the structure are valid. However, in practical applications when the structure is not known the radial basis function network performs much better than the classical methods.

关键词： Radial basis function networks Neural networks Nonlinear dynamical systems Parameter estimation Power system modeling Linear systems Nonlinear systems Automatic control Laboratories control engineering

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On training radial basis function networks as series-parallel and parallel models for identification of nonlinear dynamic systems

On training radial basis function networks as series-paralle...

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IEEE International Conference on Systems, Man and Cybernetics

作者： O. Nelles Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University of Darmstadt Darmstadt Germany

Identification of linear and nonlinear dynamic systems can be performed with a series-parallel or parallel model. In this paper both approaches are compared. While many applications require the model to run in parallel to the process, usually the identification procedure is carried out with a series-parallel model. This paper shows that optimization of a series-parallel model does not necessarily lead to a good parallel model. Furthermore a decrease of the error in one configuration may result in an increase in the other.

关键词： Radial basis function networks Predictive models Transfer functions Nonlinear equations Parameter estimation Stability Automatic control Laboratories control engineering automation

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