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NONLINEAR NETWORK STRUCTURES FOR FEEDBACK control

Asian Journal of control 2008年第4期1卷

作者： F.L. Lewis Dept. of Electical Engineering The University of Texas at Arlington U.S.A. F. L. Lewis was born in Wärzburg. Germany subsequently studyning in Chile and Goruonstoun School in Scotland. He obtained the Bachelor's Degree in Physics/Electrical Engineering and the Master's of Electrical Engineering Degree at Rice University in 1971. He spent six years in the U.S. navy serving as Navigator aboard the frigate USS Trippe (FF-1075) and Executive Officer and Acting Commanding Officer aboard USS Salinan (ATF-161). In 1977 he received the Master's of Science in Aeronautical Engineering from the University of West Florida. In 1981 he obtained the Ph.D. degree at The Georgia Institute of Technology in Atlanta where he was employed as a professor from 1981 to 1990 and is currently an Adjunct Professor. He is a Professor of Electrical Engineering at The University of Texas at Arlington where he was awarded the Moncrief-O'Donnell Endowed Chair in 1990 at the Automation and Robotics Research Institute. Dr. Lewis has studied the geometric analytic and structural properties of dynamical systems and feedback control automation. His current interests include robotics intelligent control neural and fuzzy systes nonlinear systems and manufacturing process control. He is the author/co-author of 2 U.S. patents 124 journal papers 20 chapters and encyclopedia articles 210 refereed conference papers seven books: Optimal Control Optimal Estimation Applied Optimal Control and Estimation Aircraft Control and Simulation Control of Robot Manipulators Neural Network Control High-Level Feedback Control with Neural Networks and the IEEE reprint volume Robot Control. Dr. Lewis is a registered Professional Engineer in the State of Texas and was selected to the Editorial Boards of International Journal of Control Neural Computing and Applications and Int. J. Intelligent Control Systems. He is the recipient of an NSF Research Initiation Grant and has been continuously funded by NSF since 1982. Since 1991 he has received $1.8 m

A framework is given for controller design using Nonlinear Network Structures, which include both neural networks and fuzzy logic systems. These structures possess a universal approximation property that allows them to be used in feedback control of unknown systems without requirements for linearity in the system parameters or finding a regression matrix. Nonlinear nets can be linear or nonlinear in the tunable weight parameters. In the latter case weight tuning algorithms are not straightforward to obtain. Feedback control topologies and weight tuning algorithms are given here that guarantee closed-loop stability and bounded weights. Extensions are discussed to force control, backstepping control, and output feedback control, where dynamic nonlinear nets are required.

关键词： Intelligent feedback control neural networks neural network structures basis function networks fuzzy control fuzzy-neural networks universal approximation adaptive nonparametric control learning control systems

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LOCATING OBJECT USING HIERARCHICL HAUSDORFF DISTANCE MATCHING

LOCATING OBJECT USING HIERARCHICL HAUSDORFF DISTANCE MATCHIN...

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2006 8th International Conference on Signal Processing

作者： WEI,Y.F. JIANG,D.L. PENG,S.L. School of Electronic Information & Control Engineering Beijing University of Technology Institute of Automation Chinese Academy of Science

ISBN: (纸本)0780397371

In order to locate all the objects of a model in a large image,we proposed an effective Hausdorff distance(HD) matching algorithm that is based on coarse-to-fine hierarchical *** constructing the hierarchical pyramidal structure, the decimation mode of decomposition and the number of hierarchical levels are determined adaptively by the edge model. Thus the proposed algorithm can decrease the number of losing objects,at the same time sharply speed up the search when locating objects in an *** integrate circuit's(IC) micro images,which are mainly composed of vertical and horizontal linear edges,the algorithm shows better *** experiments also illustrate good matching results with other images.

关键词： hausdorff distance hierarchical structure decimation mod

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Fault diagnosis of networked control systems

Fault diagnosis of networked control systems

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作者： Fang, Huajing Ye, Hao Zhong, Maiying Dept. of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Dept. of Automation Tsinghua University Beijing 100084 China School of Control Science and Engineering Shandong University Jinan 250061 China

ISBN: (纸本)9783902661142

Networked control systems (NCS) are systems with at least one loop closed through data networks. NCS have many advantages compared with traditional systems, however the network-induced delay and other peculiarities of communication channel will degrade the performance of closed-loop systems. Supported by National Nature science Foundation of China, we studied the fault diagnosis and fault-tolerant control theory for NCS in recent years. This plenary talk will summarize the main ideas and results on fault diagnosis of NCS, including fault detection for NCS with information scheduling, for NCS with network induced time delay and for NCS with random time delay based on quasi Takagi-Sugeno fuzzy models. Copyright © 2006 IFAC.

关键词： Networked control systems

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Global exponential stability in Lagrange sense of continuous-time recurrent neural networks

Global exponential stability in Lagrange sense of continuous...

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Liao, Xiaoxin Zeng, Zhigang Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei 430074 China School of Automation Wuhan University of Technology Wuhan Hubei 430070 China

ISBN: (纸本)354034439X

In this paper, global exponential stability in Lagrange sense is further studied for continuous recurrent neural network with three different activation functions. According to the parameters of the system itself, detailed estimation of global exponential attractive set, and positive invariant set is presented without any hypothesis on existence. It is also verified that outside the global exponential attracting set;i.e., within the global attraction domain, there is no equilibrium point, periodic solution, almost periodic solution, and chaos attractor of the neural network. These theoretical analysis narrowed the search field of optimization computation and associative memories, provided convenience for application. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Recurrent neural networks

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Robust stability and stabilization of a class of nonlinear switched systems

Robust stability and stabilization of a class of nonlinear s...

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25th IASTED International Conference on Modelling, Identification, and control, MIC 2006

作者： Ji, Zhijian Wang, Long School of Automation Engineering Qingdao University Qingdao Shandong 266071 China Center for Systems and Control Department of Mechanics and Engineering Science Peking University Beijing 100871 China

ISBN: (纸本)0889865515

This paper addresses the problem of robust stability and stabilization of a class of nonlinear switched systems when each subsystem is not stabilized by a designed single static output feedback. Both the condition on robust stability only via switching and the condition on switched static output feedback controllers are expressed in terms of matrix inequalities. These conditions are derived by multiple Lyapunov functions technique and the switching rules adopted are state-dependent. All these results can be regarded as an extension of some existing results for non-switched systems.

关键词： Stabilization

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A target differentiation algorithm of multi-neural-network based on rough set for mobile robot

A target differentiation algorithm of multi-neural-network b...

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6th World Congress on Intelligent control and automation, WCICA 2006

作者： Xin, Ma. Liu, Wei Li, Yibin Chen, Weidong Xi, Yugeng Centre for Robotics School of Control Science and Engineering Shandong University Jinan Shandong Province China Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)1424403324

Aiming at the uncertainty of sonar data in the problem of sonar-based target differentiation for mobile robot, the paper firstly presents a hierarchical reduction approach to reduce a sonar data set based on rough set theory. Then on the basis of the reduction, a multi-neural network based target differentiation algorithm is designed on the fact that the effective combination of multiple neural networks can increase the ability to pattern classification and generalization of the whole system. Parallel network architecture is adopted locally to increase the differentiation accuracy. And the average weighed strategy is applied to fuse the outputs of the parallel neural network classifiers to improve the robustness of the differentiation system to noise, physically plausible failure and missing sonar data situation. The simulation experiments prove that the new method combining with the traditional target differentiation algorithm can increase the accuracy of sonar-based target differentiation for mobile robot in indoor environments. © 2006 IEEE.

关键词： Mobile robots

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Surface reconstruction based on radial basis functions network

Surface reconstruction based on radial basis functions netwo...

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Liu, Han-Bo Wang, Xin Wu, Xiao-Jun Qiang, Wen-Yi Department of Control and Mechatronics Engineering Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)3540344829

A new method for arbitrary 3d-object reconstruction in unknown environment is proposed in this paper. The implicit surface is reconstructed based on radial basis functions network from range scattered data. For the property of locality of radial basis function, the method is fast and robust with respect to large data. Furthermore, an adapted K-Means algorithm is used to reduce RBF centers for reconstruction. Experiment results show that the presented approach is helpful in speed improvement and is a good solution for large data reconstruction. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Radial basis function networks

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An algorithm for recovering FECG signal

An algorithm for recovering FECG signal

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2006 International Conference on Communications, Circuits and Systems, ICCCAS

作者： Yunxia, Li Changyuan, Fan Jianglin, Zhang School of Automation Engineering University of Electronic Science Technology of China Chengdu Sichuan Province China Department of Control Engineering Chengdu University of Information Technology Chengdu Sichuan Province China

ISBN: (纸本)0780395840

The extraction of fetal electrocardiogram (FECG) from the composite maternal ECG signal is discussed. This problem can be modelled from the perspective of blind source extraction. Using the objective function presented by Barros, this paper proposes a new algorithm to extract the FECG with higher speed. Simulation results confirm the validity of the proposed algorithm. © 2006 IEEE.

关键词： Electrocardiography

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Discrete time system multi-model adaptive control based on localization method

Discrete time system multi-model adaptive control based on l...

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6th World Congress on Intelligent control and automation, WCICA 2006

作者： Xiaoli, Li Weicun, Zhang Wei, Wang Department of Automation Information and Engineering School University of Science and Technology Beijing Beijing 100083 China Research Center of Information and Control Dalian University of Technology Dalian 116024 China

ISBN: (纸本)1424403324

By using 'localization' method, an improved algorithm of discrete time system multi-model adaptive control (MMAC) is given in this paper. The system to be controlled can be a deterministic system (noise free) or a stochastic system with bounded random disturbances, and the parameters of the system are unknown. Multiple models of the system are set up to cover the uncertainties of the system dynamics. Every sample time, by using localization method, only a few of models which are closer to the 'true' model of the system are selected, and a multi-model adaptive controller is formed based on the selected models, so the big computation burden of the multiple model algorithm is greatly reduced. It is proved that the closed loop system is stable when multi-model controller is used to a linear time-invariant system, and simulation results show the effectiveness of the proposed method. © 2006 IEEE.

关键词： Mathematical models

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Nonlinear system adaptive control by using multiple neural network models

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Li, Xiao-Li Kang, Yun-Feng Wang, Wei Department of Automation Information and Engineering School University of Science and Technology Beijing Beijing 100083 China Research Center of Information and Control Dalian University of Technology Dalian 116024 China

ISBN: (纸本)3540344373

Multiple radial based function (RBF)neural network models are used to cover the uncertainty of time variant nonlinear system, and multiple element controllers are set up based on the multiple RBF models. At every sample time, the closest model is selected by an index function which is formed by the integration of model output error. The element controller based on this model will be switched as the controller of the controlled system. This kind of multiple model adaptive controller (MMAC)is an extension of the MMAC in linear system, and it can improve the transient response and performance of the controlled system greatly. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Adaptive control systems

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