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2009 10th European control Conference, ECC 2009

作者： Csercsik, David Szederkenyi, Gabor Hangos, Katalin M. Farkas, Imre Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute Budapest Hungary Department of Endocrine Neurobiology Institute of Experimental Medicine HAS Budapest Hungary

ISBN: (纸本)9783952417393

GnRH neurons are key elements of the reproductive neuroendocrine system and play important central regulating role in the dynamics of the hormonal cycle. A conductance-based Hodgkin-Huxley model structure is proposed in this paper in the form of nonlinear ordinary differential equations that is able to take into account up-to-date biological literature data related to ion channels. Measurement data were available for parameter estimation, which are originated from laboratory experiments done in the institute of Experimental Medicine of the Hungarian Academy of Sciences in the form of whole cell patch-clamp recordings. The proposed neuron model is highly nonlinear in parameters and the evaluation of the objective function is computationally expensive, therefore the asynchronous parallel pattern search (APPS) procedure has been used for identification which is a gradient-free optimization method that can handle linear equality and inequality constraints and has advantageous convergence properties. The model with high number of estimated parameters provides a qualitatively good fit of both voltage clamp and current clamp traces. © 2009 EUCA.

关键词： Parameter estimation

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Identification of Nonlinear Static processes with Local Polynomial Regression and Subset Selection

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IFAC Proceedings Volumes 2009年第10期42卷 138-143页

作者： Heiko Sequenz Alexander Schreiber Rolf Isermann Institute of Automatic Control Laboratory of Control Engineering and Process Automation Technical University Darmstadt Germany (Tel: +49-6151-16 3914

The presented method for nonlinear system identification is based on the LOLIMOT algorithm introduced by Nelles and Isermann [1996]. The LOLIMOT algorithm divides the input space by a tree-construction algorithm and interpolates the local linear models by local membership functions. Instead of assuming local linear models, the presented algorithm utilizes general local nonlinear functions, which make the algorithm more flexible. These are approximated by a multidimensional Taylor series. Since the amount of regressors grows fast with the number of inputs and the expansion order, a subset selection procedure is introduced. It reveals significant regressors and gives information about the local functional behavior. The local subset selection is implemented as a stepwise regression with replacement of regressors. Mallows’ C p -statistic is used for the subset selection algorithm and is also implemented for final model selection. The benefit of the extended algorithm lies in the higher flexibility in the local models, which results in less partitions of the input space by a similar approximation quality.

关键词： Local Regression Subset Selection LOLIMOT Identification Algorithm Multivariate Polynomials Least-Squares Estimation Automotive Emissions

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Dynamical Modeling and Identification of a GnRH neuron *

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IFAC Proceedings Volumes 2009年第12期42卷 425-430页

作者： Dávid Csercsik Gábor Szederkényi Katalin M. Hangos Imre Farkas Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute HAS Budapest Hungary Department of Endocrine Neurobiology Institute of Experimental Medicine HAS Budapest Hungary

GnRH neurons, as key elements of the reproductive neuroendocrine system, have important central regulating role in the dynamics of the hormonal cycle. A Hodgkin-Huxley type neural model is proposed in this paper, that takes into account up-to-date biological literature data related to ion channels. The proposed neuron model is highly nonlinear in parameters and the evaluation of the objective function is computationally expensive, therefore the asynchronous parallel pattern search (APPS) procedure has been used for identification. The model with high number of estimated parameters provides a qualitatively good fit of both voltage clamp and current clamp traces.

关键词： Neural dynamics Dynamic modelling Identification Nonlinear systems

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Fault Detection of Networked control systems Subject to Access Constraints and Random Packet Dropout

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自动化学报 2009年第9期35卷 1230-1234页

作者： WANG Yong-Qiang YE Hao DING Steven WANG Gui-Zeng Department of Automation Tsinghua National Laboratory for Information Science and Technology (TNList) Tsinghua University Beijing 100084 P. R. China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg 47057 Germany

In this paper, the problem of fault detection is addressed for networked control systems (NCSs) subject to both access constraints and random packet dropout, which to the best of our knowledge has not been considered before. Both residual generation and residual evaluation as well as false alarm computation of the designated threshold are given. First, based on a deterministic formulation, residual generation is carried out in the periodic system framework. Then, residual evaluation is achieved by making full use of the stochastic properties of the random packet dropout. Finally, performance evaluation of the designated threshold, i.e., the computation of false alarm rate, is fulfilled with the assistance of Chebyshev s inequality. Simulation results are given to illustrate effectiveness of the proposed method.

关键词：故障检测网路控制接入约束残差

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An adaptive weight PSO for rolling schedules multi-objective optimization of tandem cold rolling

An adaptive weight PSO for rolling schedules multi-objective...

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IEEE International Conference on automation and Logistics

作者： Yu Wang Yong Li Jianchang Liu Key Laboratory of Integrated Automation of Process Industry Ministry of Education Northeastern University Shenyang Liaoning China Department of Automatic Control Shenyang Institute of Aeronautical Engineering Shenyang Liaoning China

In this paper, an adaptive weight particle swarm optimization is proposed for multi-objective optimization. And it is applied to rolling schedules multi-objective optimization of tandem cold rolling. According to the demands of actual rolling schedules designing, power distribution, rolling energy consumption and slip rate are selected as objective functions. Then the multi-objective model of rolling schedules is established. The proposed algorithm is applied to rolling schedules optimization of tandem cold rolling. The result shows that the proposed method decreases the values of three objective functions simultaneously compared to actual rolling schedules. Furthermore the proposed algorithm has faster convergence speed than the adaptive weight approach GA.

关键词： Job shop scheduling Particle swarm optimization Optimization methods Power distribution Scheduling algorithm automation Energy consumption Logistics Laboratories Convergence

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Fault Detection of Networked control systems Subject to Access Constraints and Random Packet Dropout

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Acta automatica Sinica 2009年第9期35卷 1230-1234页

作者： Yong-Qiang WANG Hao YE Steven DING Gui-Zeng WANG Department of Automation Tsinghua National Laboratory for Information Science and Technology (TNList) Tsinghua University Beijing 100084 P. R. China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg 47057 Germany

关键词： Fault detection (FD) residual generation residual evaluation networked control system (NCS) packet dropout

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Model Adjustment and Multi-Model Based Fault Diagnosis for Hydraulic Servo Axis

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IFAC Proceedings Volumes 2008年第2期41卷 13809-13814页

作者： Marco Muenchhof Mark Beck Technische Universitaet Darmstadt Institute of Automatic Control Laboratory for Control Systems and Process Automation Darmstadt Germany (Tel: +49-6151-16-3114 Technische Universitaet Darmstadt Institute of Automatic Control Laboratory for Control Systems and Process Automation Darmstadt Germany (Tel: +49-6151-16-2314

This paper presents a model adjustment and a multi-model based fault diagnosis approach. Both methods are based on the same idea. A physical model of the process is altered such that it mimics the behavior of the process in the presence of certain faults. A number of these modified models, each governing a different fault condition, are evaluated and the model with the smallest output error is determined. As this model is assumed to best govern the current process dynamics, it can be used to diagnose the actual state of the process. Two variations of this idea are presented, tailored specifically to online and offline operation respectively. For online applications, multiple models with fixed parameters are evaluated in parallel, whereas for offline application, an optimization approach is employed. Here, one model with several fault size parameters is regarded and the optimal fault size parameters are determined by means of an interval halving technique. Both techniques have been evaluated at a testbed and have shown very good fault detection and diagnosis capabilities.

关键词： Hydraulic Actuators Fault Diagnosis Sensor Failures Physical Models Optimization

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Displacement Sensor Fault Tolerance for Hydraulic Servo Axis

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IFAC Proceedings Volumes 2008年第2期41卷 13803-13808页

作者： Marco Muenchhof Technische Universitaet Darmstadt Institute of Automatic Control Laboratory for Control Systems and Process Automation Darmstadt Germany (Tel: +49-6151-16-3114

In this paper, a fault management system for a hydraulic servo axis is described. This system is capable of sustaining faults in the piston displacement sensor of the position-controlled hydraulic servo axis. By means of a parity equation based fault detection stage, faults in the displacement sensor as well as other sensors and components of the servo axis can be detected and subsequently be diagnosed by means of a fuzzy-logic based reasoning system. Upon the diagnosis of a piston displacement sensor fault, the system switches to a “model-sensor” which provides an estimate for the piston position. To ensure the utmost model fidelity of the model sensor, the model parameters are constantly updated by means of parameter estimation during the fault-free operation of the servo axis. Experiments at a hydraulic servo axis conclude this paper and show the high quality of the reconstructed piston displacement sensor signal.

关键词： Hydraulic Actuators Fault Diagnosis Fault Tolerance Sensor Failures Physical Models Parameter Estimation

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A unified approach to fuzzy modelling and robust synchronization of different hyperchaotic systems

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Chinese Physics B 2008年第11期17卷 4056-4066页

作者： Zhang, Hua-Guang Zhao, Yan Yu, Wen Yang, Dong-Sheng School of Information Science and Engineering Northeastern University Shenyang 110004 China Key Laboratory of Integrated Automation of Process Industry Ministry of Education Northeastern University Shenyang 110004 China Department of Automatic Control Engineering Shenyang Institute of Engineering Shenyang 110136 China Departamento de Control Automatico CINVESTAV-IPN Av. IPN 2508 México D. F. 07360 Mexico

In this paper, a Takagi-Sugeno (T-S) fuzzy model-based method is proposed to deal with the problem of synchronization of two identical or different hyperchaotic systems. The T-S fuzzy models with a small number of fuzzy IF-THEN rules are employed to represent many typical hyperchaotic systems exactly. The benefit of employing the T-S fuzzy models lies in mathematical simplicity of analysis. Based on the T-S fuzzy hyperchaotic models, two fuzzy controllers are designed via parallel distributed compensation (PDC) and exact linearization (EL) techniques to synchronize two identical hyperchaotic systems with uncertain parameters and two different hyperchaotic systems, respectively. The sufficient conditions for the robust synchronization of two identical hyperchaotic systems with uncertain parameters and the asymptotic synchronization of two different hyperchaotic systems are derived by applying the Lyapunov stability theory. This method is a universal one of synchronizing two identical or different hyperchaotic systems. Numerical examples are given to demonstrate the validity of the proposed fuzzy model and hyperchaotic synchronization scheme. © 2008 Chin. Phys. Soc. and IOP Publishing Ltd.

关键词： Synchronization

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Identification of the primary circuit dynamics in a pressurized water nuclear power plant

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IFAC Proceedings Volumes 2008年第2期41卷 10640-10645页

作者： Csaba Fazekas Gábor Szederkényi Katalin M. Hangos Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences P.O. Box 63 H-1518 Budapest Hungary

This paper presents the results of the parameter estimation procedure for the primary circuit dynamics of a VVER-type nuclear power plant. The model structure is a low dimensional lumped nonlinear model published previously in Fazekas et al. [2007a]. The parameter estimation method uses the modular decomposition of the system model for obtaining physically meaningful initial parameter estimates. The final parameter estimates are computed using the integrated model.

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