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Identification with Dynamic Neural Networks - Architectures, Comparisons, Applications

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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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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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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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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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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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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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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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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