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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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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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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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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 based on adaptive parity equations and single-parameter tracking

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control engineering PRACTICE 1996年第10期4卷 1361-1369页

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

Faults which appear in technical processes can often be described as additive or multiplicative faults with respect to the process model. To perform fast detection of these faults, continuous-time parity equations are used. Parameter deviations are estimated directly from residuals, providing information about their size. The combined method enables one to distinguish between additive and parametric faults. In case of time-variant processes with slow parameter changes, the coefficients of the parity equations can also be adapted with respect to the tracked parameters. The problem of persistent process excitation for estimation is by-passed. The fault-detection scheme is demonstrated on a permanently excited d.c. motor on a laboratory rig. Its properties, and the experimental results, are discussed.

关键词： fault detection adaptive parameter estimation parity space continuous-time dcmotors

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Local linear model trees for on-line identification of time-variant nonlinear dynamic systems

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1st International Conference on Artificial Neural Networks, ICANN 1996

作者： Nelles, Oliver Technical University of Darmstadt Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str. 4 DarmstadtD-64283 Germany

ISBN: (纸本)3540615105

This paper discusses on-line identification of time-variant nonlinear dynamic systems. A neural network (LOLIMOT, [1]) based on local linear models weighted by basis functions and constructed by a tree algorithm is introduced. Training of this network can be divided into a structure and a parameter optimization part. Since the network is linear in its parameters a recursive least-squares algorithm can be applied for on-line identification. Other advantages of the proposed local approach are robustness and high training and generalisation speed. The simplest recursive version of the algorithm requires only slightly more computations than a recursive linear model identification. The locality of LOLIMOT enables on-line learning in one operating region without forgetting in the others. A drawback of this approach is that systems with large structural changes over time cannot be properly identified, since the model structure is fixed. © Springer-Verlag Berlin Heidelberg 1996.

关键词： Forestry

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Identification of a high efficiency boiler based on neural networks with locally distributed dynamics

Identification of a high efficiency boiler based on neural n...

引用

IEEE International Symposium on Intelligent control (ISIC)

作者： J. Ohl M. Ayoubi M. Kurth Laboratory of Control Engineering & Process Automation Institute of Automatic Control Technical University of Darmstadt Darmstadt Germany

This paper considers the application of neural network with distributed dynamics to the identification of nonlinear systems. The main intention is to provide a simulation tool to the development engineer. The identification of the thermal plant is accomplished without any a priori knowledge about the nonlinear structure or the dynamics of the plant. Identification results of two different neural nets are shown and compared. The application area is boilers for building energy management systems.

关键词： Boilers Neural networks Aerodynamics Nonlinear dynamical systems Autoregressive processes Temperature control Heating Neurons Optimal control control systems

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