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

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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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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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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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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Automatic control of vehicle startup for emission tests on engine dynamometers

Automatic control of vehicle startup for emission tests on e...

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American control Conference (ACC)

作者： K. Pfeiffer R. Isermann Laboratory for Control Engineering and Process Automation Institute of Automatic Control Technical University of Darmstadt DarMstadt Germany

The dynamical engine test stand described with a real engine and a drive-line simulation is a capable tool for exhaust emission analysis. To simulate the dynamics of load changes occurring in a driving vehicle, a model of the car body including clutch, manual gear-box, drive-line, differential and wheels has been developed and implemented on the test stand. Exhaust emission tests may be performed automatically with a proper driver simulation used in the dynamical engine test stands. Starting vehicle from standstill is a critical operation, especially for vehicles with manual gearbox, that occurs frequently in exhaust emission test cycles. Model-based control strategies for the vehicle startup have been developed. These strategies are presented and discussed.

关键词： Automatic control Automatic testing Engines Vehicle dynamics Wheels Torque Aerodynamics Vehicles Vehicle driving Roads

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A Comparison Between RBF Networks and Classical Methods for Identification of Nonlinear Dynamic Systems

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IFAC Proceedings Volumes 1995年第13期28卷 233-238页

作者： Oliver Nelles Rolf Isermann Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str. 4 64283 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 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 become comparable in calculational effort. It will be shown, that the classical methods can compete or even perform better than the neural network, if the assumptions for the structure are valid. However if in practical applications the structure is not known the radial basis function network performs much better than the classical methods.

关键词： identification nonlinear systems parameter estimation neural networks radial basis function networks

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Identification and Adaptive Nonlinear control of Drives with Friction

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IFAC Proceedings Volumes 1995年第14期28卷 305-310页

作者： A. Abou-El-Ela Th. Pfeufer R. Isermann Technical University of Darmstadt Institute of Automatic Control Laboratory for Control Engineering and Process Automation Landgraf Georg Str.4 D-64283 Darmstadt Germany

In this contribution several approaches to nonlinear adaptive control of mechanical drives are proposed taking into account determined nonlinear actuator characteristics such as friction, hysteresis and other static nonlinearities. Improved control performance in terms of dynamics, high positioning accuracy and robustness is achieved by combining linear position control with nonlinear feed-forward and adaptive friction compensation methods. Based on a precise model of the drive dynamics recursive identification techniques are applied to obtain reliable estimates of the time varying and position dependent process parameters, the nonlinear friction characteristic in particular. As examples the nonlinear friction characteristics of robotic joint drives and pneumatic drives are determined and it is shown how the control can be improved considerably by model based nonlinear control in the presence of time varying nonlinear characteristics and hysteresis effects. Experimental results illustrate the efficiency of the developed nonlinear control schemes.

关键词： Nonlinear adaptive control parameter identification friction compensation robotic joint drives pneumatic drives

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Fault Detection of Flowrate and Temperature control Loops using Estimated Parity Equations

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IFAC Proceedings Volumes 1995年第12期28卷 193-198页

作者： Thomas Höfling Rüdiger Deibert Oliver Hecker Technical University of Darmstadt Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Phone: +49 6151 163114 Fax: +49 6151 293445

Many systems with nonlinear behaviour are difficult to model. Consequently model based approaches to fault detection can not be applied easily. Additional problems are introduced in closed loops by the controversity of controller design and fault detection. For a certain class of processes with mainly constant input signals a combination of parameter estimation techniques and the parity space approach allows on-line fault detection with small signal models. Only few knowledge of the process is required: An idea about the dynamic model order and the static nonlinear characteristic curve. Using the proposed method fault detection is performed for temperature control loops in a thermic plant.

关键词： Fault detection model based nonlinear parameter estimation parity space

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Nonlinear distance and cruise control for passenger cars

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IFAC Proceedings Volumes 1995年第1期28卷 209-214页

作者： St. Germann R. Isermann Technical University of Darmstadt Institute of Automatic Control Laboratory for Control Engineering and Process Automation Landgraf-Georg-Straße 4 D-64283 Darmstadt Germany Telephone : 0049-6151-162114 Telefax:0049-6151-293604

A methodology for designing an automated vehicle longitudinal velocity and distance controller is presented and applied to an automobile. Typically a vehicle is described by velocity depended dynamics and specific nonlinearities. Therefore the controller consists of a three layer structure. In the first layer a linearization of the nonlinearities is done in order to achieve a simplified structure for controller design purposes. With respect to changes in the typical vehicle parameters-mass and aerodynamical drag - an adaptive controller structure is used and these parameters are estimated by a recursive least squares algorithm. Based on classical controlling techniques a linear acceleration controller is developed in the middle layer. Up to now no mathematical equations are available describing the subjective demands of the driver and passengers during longitudinal motion. Therefore a fuzzy controller is applied in the upper layer. This controller based on the linguistic description of comfort demands. The complete structure is used in two different series fabricated vehicles and experimental results are shown for highway traffic as well as for stop-go traffic on highway congestions.

关键词： vehicle dynamics longitudinal motion intelligent vehicle highway systems fuzzy logic intelligent cruise control

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Nonlinear distance and cruise control for passenger cars

Nonlinear distance and cruise control for passenger cars

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American control Conference (ACC)

作者： S. Dermann R. Isermann Inst. of Autom. Control Tech. Univ. Darmstadt Germany Institute of Automatic Control Laboratory for Control Engineering and Process Automation Technical University of Darmstadt Darmstadt Germany

A methodology for designing an automated vehicle longitudinal velocity and distance controller is presented and applied to an automobile. nonlinearities. The controller consists of a three layer structure. In the first layer a linearization of the nonlinearities is done in order to achieve a simplified structure for controller design purposes. With respect to changes in the typical vehicle parameters-mass and aerodynamical drag-an adaptive controller structure is used and these parameters are estimated by a recursive least squares algorithm. Based on classical controlling techniques a linear acceleration controller is developed in the middle layer. A fuzzy controller is applied in the upper layer. This controller based on the linguistic description of comfort demands. Additionally a neural network is used in this layer instead of the fuzzy system. The complete structure is used in two different series fabricated vehicles and experimental results are shown for highway traffic and also for stop-go traffic on highway congestions. Additionally a neural network is trained by measurement data as a second approach to describe the comfort demands.

关键词： Automatic control Vehicles control nonlinearities Neural networks Road transportation Communication system traffic control Design methodology Velocity control Automobiles Programmable control

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