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H ∞ Parameter Identification for Inflight Detection of Aircraft Icing: The Time-Varying Case

IFAC Proceedings Volumes 2000年第15期33卷 289-294页

作者： James W. Melody Thomas Hillbrand Tamer Başar William R. Perkins Coordinated Science Laboratory University of Ilinois 1308 West Main Street Urbana IL 61801-2307/ USA Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Systems and Process Automation Landgraf-Georg-Strasse 4 D-64283 Darmstadt Germany

Inflight parameter identification of aircraft flight dynamics is considered in this paper in the context of an ice management system. In particular, an H ∞ parameter identification algorithm is evaluated in terms of detecting an aircraft icing event when only a noisy state measurement is available. While previous studies have addressed identification during a pilot-induced maneuver, taking advantage of the excitation due to input, this paper addresses identification during cruise, where excitation is provided only by unknown disturbances due to turbulence. Simulation results show that for moderate turbulence levels, the H ∞ algorithm provides a timely and unambiguous icing indication.

关键词： Aircraft control H ∞ Identification Time-Varying systems Robustness

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Linearization based and local model based controller design

Linearization based and local model based controller design

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1999 European control Conference, ECC 1999

作者： Fink, A. Nelles, O. Fischer, M. Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Systems and Process Automation Landgraf-Georg-Str. 4 DarmstadtD-64283 Germany

ISBN: (纸本)9783952417355

This paper deals with the comparison of two design methodologies for nonlinear fuzzy model based controllers. A straightforward approach to design nonlinear controllers is to linearize the nonlinear model at the current operating point and to design a linear controller. If the design of the linear controller, however, requires the optimization of parameters, this design procedure can become infeasible for real-time application because of the high computational effort. Local linear models, however, provide the possibility to design such controllers off-line for each submodel separately. During the control process, it is interpolated between the linear controllers according to the current operating point. These two design strategies are discussed and the control performance is compared by a simulation example. Finally, both strategies are utilized to design a predictive controller for temperature control of a heat exchanger. © 1999 EUCA.

关键词： Design

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Supervision of nonlinear adaptive controllers based on fuzzy models

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IFAC Proceedings Volumes 1999年第2期32卷 8602-8607页

作者： Alexander Fink Martin Fischer Oliver Nelles Darmstadt University of Technology Institute of Automatic Control Laboratory of Control Systems and Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Tel: +49/6151-164524 Fax: +49/6151-293445

A novel approach for the supervision of fuzzy model on-line adaptation is proposed. A nonlinear predictive controller is designed based on a Takagi-Sugeno fuzzy model. By adapting the fuzzy model on-line, high control performance can be achieved even with time-variant process behavior and changing unmodeled disturbances. A local weighted recursive least-squares algorithm exploits the local linearity of Takagi-Sugeno fuzzy models. In order to cope with problems resulting from insufficient excitation a supervisory level is introduced. It comprises a variable forgetting factor and an additional adaptation model which makes the on-line adaptation robust and reliable. The effectiveness and real-world applicability of the proposed approach are demonstrated by application to temperature control of an industrial-scale heat exchanger.

关键词： Adaptive control fuzzy model heat exchanger rls supervision

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Fast Neural Networks for Diesel Engine control Design

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IFAC Proceedings Volumes 1999年第2期32卷 8154-8159页

作者： M. Hafner M. Schüler R. 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: +49/6151-162514 Fax: +49/6151-293445

Advanced engine control systems require accurate process models. This paper presents neural net models for combustion engines. After briefly introducing a special local linear RBF network (LOLIMOT) two applications are described. Different methods for developing exhaust gas models are compared and a dynamic model for the charging pressure dynamics of a turbocharger is presented. Finally, an exhaust vs. consumption optimization is presented for optimizing the injection angle dependent on given weighting factors for specific emissions, the fuel consumtion and the current driving situation.

关键词： Engine control modeling exhaust optimization neural networks

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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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Fuzzy Model-Based Predictive control of a Heat Exchanger

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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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Robust hybrid control based on inverse fuzzy process models

Robust hybrid control based on inverse fuzzy process models

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： M. Fischer R. Isermann Institute of Automatic Control Laboratory of Control Systems and Process Automation Technical University of Darmstadt Darmstadt Germany

Takagi-Sugeno type fuzzy models are universal approximators for nonlinear dynamic processes. If they are trained to represent the inverse plant characteristics they can be used as feedforward controllers. The achievable control performance strongly depends on the model quality, and the simple inverse model controller does not cope with disturbances and process uncertainty. As a consequence, a hybrid control scheme is proposed which considerably improves the robustness properties. This paper reviews the identification of both forward and inverse fuzzy models. The difficulties accompanying the latter task are discussed. The control scheme based on the idea of disturbance observation is introduced and thoroughly analyzed. Finally, the controller is applied to a cooling blast with nonlinear behavior and variant dynamics.

关键词： Robust control Fuzzy control Inverse problems Fuzzy systems Radial basis function networks Humans System identification Partitioning algorithms Least squares methods Iterative algorithms

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Application and comparison of alternative position sensors in high-accuracy control of an X-Y table

Application and comparison of alternative position sensors i...

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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 Fault Diagnosis and Supervision of the Main and Feed Drives of a Flexible Milling Center

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IFAC Proceedings Volumes 1991年第6期24卷 185-190页

作者： P. Wanke T. Reiß Institut of Automatic Control Laboratory of Control Systems and Process Automation Technical University Darmstadt Landgraf-Georg-Str. 4 D-6100 Darmstadt Germany

The early detection and localization of faults in machines and drives is of primary interest to make further improvement of the reliability and saftey. This paper presents a new approach in fault diagnosis and supervision of main and feed drives with elastic components. The supervision of drives with additional sensors for temperature, pressures or vibrations is usually expensive. Currently, internal process faults are only detected partially and at a rather late stage, by generating alarms if certain limits of the measured signals are exceeded or limit switches stop the machine tool. A process model based approach for fault diagnosis was developed. Dynamic models of each drive are tried and together with easily measurable signals of the drive, like current and speed, physical parameters are estimated. Changes of those process parameters are the symptoms for a diag-nostic inference mechanism. Experimental results with least squares parameter estimation in the form of disrete square root filters are shown.

关键词： Fault detection feed drive machine tool main drive modeling parameter estimation process monitoring

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