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T-S fuzzy observer for nonlinear magnetic levitation system with uncertain variable friction coefficient

Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (science and technology) 2011年第SUPPL. 1期42卷 114-118页

作者： Zhou, Li-Jie Guo, Shu-Li Gao, Zhe School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex system Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing 100081 China

An adaptive fuzzy observer for nonlinear magnetic levitation system with uncertain friction coefficient is proposed. First, a T-S fuzzy model of the nonlinear magnetic levitation system is proposed. An observer is given by LMI method and the Lyapunov method. Through adding an auxiliary variable, it relaxes the constraint of the design of observer. The proposed approach is a more relaxed condition than others. Simulation results show the effectiveness of this approach. The results show that the position of magnetic levitation system is estimated effectively with unknown friction coefficient. There is some reference value for a kind of nonlinear systems with unknown parameter.

关键词： Lyapunov methods

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Mobile robot's electronic compass calibration based on modified Fourier Neural Network

Mobile robot's electronic compass calibration based on modif...

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IEEE Conference on Robotics, Automation and Mechatronics

作者： Gong Kun Deng Fang Ma Tao Gong Kun is with School of Automation Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Deng Fang is with School of Automation Beijing Institute of Technology and Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Ma Tao is with School of Automation Beijing Institute of Technology and Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China

In order to improve the precision of the azimuth measured by mobile robot's electronic compass, this paper proposes a new calibration method based on Fourier Neural Network trained by Modified Particle Swarm Optimization (MPSO-FNN). This method makes use of Fourier Neural Network (FNN) to establish the error compensation model of electronic compass's azimuth, and introduces Modified Particle Swarm Optimization (MPSO) algorithm to optimize the weights of neural network. Thus the comparatively accurate error model of azimuth is obtained to compensate the output of electronic compass. This method not only has strong nonlinear approximation capability, but also overcomes the neural networks' shortcomings which are too slow convergence speed, oscillation, and easy to fall into local optimum and sensitive to the initial values. Experimental results demonstrate that after calibrated by this method, the range of azimuth error reduces to -0.35°~0.70° from -3.4°~25.2°, and the average value of absolute error is only 0.30°.

关键词： Compass Azimuth Biological neural networks Particle swarm optimization Training Accuracy Automation

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The application and issuse of linear wireless sensor networks

The application and issuse of linear wireless sensor network...

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International Conference on system science, Engineering Design and Manufacturing Informatization (ICSEM)

作者： Zhengjie Wang Xiaoguang Zhao Xu Qian School of Mechanical Electronic & Info. Engineering China University of Mining & Technology (Beijing) Beijing China College of Information and Electrical Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Intelligent Control and Management of Complex Systems Chinese Academy of Sciences Beijing China

Wireless sensor networks consist of a large number of sensor nodes that have low power and limited transmission range and can be used in various scenario. The nodes can be deployed in the long and narrow region, such as road, bridge, tunnel and pipeline, to get some interesting information. The linear topology of these network application is different other application and have special feature, such as multi-hop, long delay, long distance and low reliability. This paper introduces the concept of linear wireless sensor networks and discusses the classification of topology and key issue of this network. The application of the linear wireless sensor network, such as road, bridge, tunnel and pipeline is presented. The research challenges are discussed at last in this paper.

关键词： Monitoring Reliability Collaboration Spread spectrum communication Hoses Topology Routing protocols

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A novel type of trigonometric neural network trained by Extended Kalman Filter

A novel type of trigonometric neural network trained by Exte...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： M. Norouzi M. Mansouri M. Teshnehlab M. Aliyari Shoorehdeli Faculty of Engineering Department of Computer Engineering Science and Research Branch Islamic Azad University Iran Intelligent System Laboratory (ISLAB) Faculty of Electrical Engineering Control Department K.N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Department of Mechatronics Engineering Advanced Process Automation &Control Laboratory (APAC) K.N. Toosi University of Technology Tehran Iran

In this study, a new type of trigonometric neural network is presented by adding frequency and phase to trigonometric activation functions. The proposed trigonometric neural network has more flexibility in comparison with conventional trigonometric neural networks and even other types of neural networks. Due to the low convergence rate and high posibility of trapping in a local minimum of backpropagation algorithm, Extended Kalman Filter algorithm is used to train the neural network's parameters which they appear in a nonlinear form. The Simulation of the suggested neural network based on the prediction of Mackey-Glass time series and identification of a nonlinear dynamic ystem reveals the efficiency of the proposed network. To show the efficiency of this method, the results are compared with the results of the others.

关键词： Biological neural networks Kalman filters Equations Neurons Vectors Covariance matrix Mathematical model

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Object tracking based on multi-bandwidth mean shift with convergence acceleration

Object tracking based on multi-bandwidth mean shift with con...

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2nd International Conference on Image Analysis and Signal Processing, IASP'2010

作者： Bin, Zhou Jun-Zheng, Wang Wei, Shen Key Laboratory of Complex System Intelligent Control and Decision School of Automatic Control Beijing Institute of Technology Beijing China

ISBN: (纸本)9781424455553

A multi-bandwidth based tracking algorithm was proposed to search for the global kernel mode when the probability density has multiple peak modes. Firstly, a monotonically decreasing sequence of bandwidths was fixed according to the target scale. At each bandwidth, using mean shift to And out the maximum probability, and starting the next iteration at the previous convergence location. Finally, the best optimal mode could be obtained at the last bandwidth. To accelerate the convergence, over-relaxed strategy was introduced to enlarge the step size. Under the convergence rule, the learning rate was adaptively adjusted by Bhattacharyya coefficients of consecutive iteration convergence. The experimental results show that the proposed multi-bandwidth mean shift tracker is robust in high-speed object tracking, and perform well in occlusions. The adaptive over-relaxed strategy is effective to lower the convergence iterations by enlarging the step size. © 2010 IEEE.

关键词： Bandwidth

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An adaptive hybrid optimizer based on particle swarm and differential evolution for global optimization

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science China(Information sciences) 2010年第5期53卷 980-989页

作者： XIN Bin 1,2 ? , CHEN Jie 1,2* , PENG ZhiHong 1,2 & PAN Feng 1,2 1 School of Automation, Beijing Institute of technology, Beijing 100081, China 2 Key Laboratory of complex system intelligent control and Decision, Ministry of Education, Beijing 100081, China 1. School of Automation Beijing Institute of Technology Beijing 100081 China2. Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China

This paper presents extensive experiments on a hybrid optimization algorithm (DEPSO) we recently developed by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). The hybrid optimizer achieves on-the-fly adaptation of evolution methods for individuals in a statistical learning way. Two primary parameters for the novel algorithm including its learning period and population size are empirically analyzed. The dynamics of the hybrid optimizer is revealed by tracking and analyzing the relative success ratio of PSO versus DE in the optimization of several typical problems. The comparison between the proposed DEPSO and its competitors involved in our previous research is enriched by using multiple rotated functions. Benchmark tests involving scalability test validate that the DEPSO is competent for the global optimization of numerical functions due to its high optimization quality and wide applicability.

关键词： global optimization statistical learning differential evolution particle swarm optimization hybridization adaptation rotated function

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Robust filtering for networked systems with random transmission delays and packet dropouts

Robust filtering for networked systems with random transmiss...

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作者： Cai, Yunze Wang, Hua O. Tanaka, Kazuo Xu, Xiaoming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing 800 Dongchuan Rd. Shanghai 200240 China Department of Mechanical Engineering Boston University Boston MA 02215 United States Department of Mechanical Systems and Intelligent Systems University of Electro-Communications Tokyo 182-8585 Japan Department of Automation Shanghai Jiao Tong University 800 Dongchuan Rd. Shanghai 200240 China University of Shanghai for Science and Technology 16 Jun Gong Rd. Shanghai 200093 China

ISBN: (纸本)9781457700804

This paper addresses a robust H∞ filtering problem for networked systems that are subject to both random transmission delays and packet dropouts. To start with, a data transmission model is established by employing random series with Bernoulli distributions. A sufficient condition for robust stability with H∞ constraints is derived for the filtering error system. The robust filter is designed in terms of the feasibility of a linear matrix inequality (LMI). The numerical examples are provided to show the effectiveness of the data transmission model and the proposed filtering method. © 2011 AACC American Automatic control Council.

关键词： Linear matrix inequalities

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Rules-based autonomous obstacle avoidance for mobile robots

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Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (science and technology) 2011年第SUPPL. 1期42卷 493-497页

作者： Wang, Qi-Zhi Xu, De School of Computer Science and Information Technology Beijing Jiaotong University Beijing 100044 China Key Laboratory of Intelligent Control of Complex Systems Institute of Automation Chinese Acad. of Sci. Beijing 100080 China

A real time and autonomous obstacle avoidance method based on rules for mobile robots was presented. Wall- along algorithm was dynamically implemented in unknown environment without collision. The results show that th... 详细信息

关键词： Mobile robots

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Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation

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电子学报(英文版) 2010年第3期19卷 451-456页

作者： LI Bo LIU Wenju DOU Lihua School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision (Beijing Institute of Technology) Ministry of Education Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

Density estimation via Gaussian mixture modeling has been successfully applied to image segmentation, speech processing and other fields relevant to clustering analysis and Probability density function (PDF) modeling. Finite Gaussian mixture model is usually used in practice and the selection of number of mixture components is a significant problem in its application. For example, in image segmentation, it is the donation of the number of segmentation regions. The determination of the optimal model order therefore is a problem that achieves widely attention. This paper proposes a degenerating model algorithm that could simultaneously select the optimal number of mixture components and estimate the parameters for Gaussian mixture model. Unlike traditional model order selection method, it does not need to select the optimal number of components from a set of candidate models. Based on the investigation on the property of the elliptically contoured distributions of generalized multivariate analysis, it select the correct model order in a different way that needs less operation times and less sensitive to the initial value of EM. The experimental results show the effectiveness of the algorithm.

关键词：高斯混合模型图像分割无监督学习应用概率密度函数椭球等高分布模型估计高斯密度

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Identification of interval models for a class of uncertain systems via linear programming

Identification of interval models for a class of uncertain s...

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作者： Zhang, Guozhu Chen, Jie Li, Zhiping Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology School of Automation Beijing 100081 China

ISBN: (纸本)9781424474264

The interval models of uncertain plants are frequently used in the field of robust control. In this paper, a novel interval model identification method based on linear programming is proposed. By certain prepossessing on the measured data of the plant, the interval model identification problem is transformed into several linear programming problems, which can be easily solved through various algorithms. We have proved that the resultant interval model has a known error bound, provided that certain conditions on the measured data are satisfied. To demonstrate its effectiveness, the proposed method is applied to identify the interval model of a servo system with variant load and inertia. © 2010 AACC.

关键词： Linear programming

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