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第25届中国控制与决策会议

作者： Xin Xu Wenjie Chen Mingjun Jiang School of Automation Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision (Beijing Institute of Technology) Ministry of Education China North Vehicle Research Institute

ISBN: (纸本)9781467355339

An algorithm namely the TDOA-Camberra is proposed for the multi-target passive location in this paper. The algorithm uses the Camberra distance to associate the target data and do the optimal search, then uses the time difference of arrival (TDOA) to locate multiple targets passively once data from the same target is determined. At the same time, as the computing time of associating data using Camberra distance is long, the improved sequential similarity detection algorithm (SSDA) is added to the calculation in order to shorten the time of the data association, thus to increase the speed of the multi-target passive location. The theoretical analysis and simulation experiment proves that the multi-target passive location can be implemented by the proposed algorithm accurately and quickly.

关键词： passive location, data association, Camberra distance, SSDA

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Pyramid histogram of oriented gradient and particles swarm optimization based SVM for vehicle detection

Pyramid histogram of oriented gradient and particles swarm o...

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2013 7th International Conference on Image and Graphics, ICIG 2013

作者： Wang, Hailuo Bo, Wang Sun, Li College of Automation Beijing Institute of Technology Beijing China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China

ISBN: (纸本)9780769550503

Vehicle Detection is an important part in intelligent transportation system (ITS) and driver assistance system. Considering vehicles have strong edges and lines in different orientation and scales, in this paper, we presents a method for detecting vehicles based on a feature named Pyramid Histogram of Oriented Gradient. This feature provides spatial distribution information of edges which was often ignored by other features. Specifically, we extract PHOG features from a traffic image and a vector is obtained as representation of this image. In order to speed up the process of calculation, Principle Component Analysis (PCA) algorithm is applied to these vectors to reduce their dimensionality. Tests show the efficiency improved significantly. Theses representative vectors are then used to train SVM classifier. To ensure the final classification accuracy, we adopt Particles Swarm Optimization (PSO) method to gain the parameters used in SVM classification. Optimal parameters correspond with optimal result. Experiments demonstrate the superiority of the proposed approach which has achieved an average accuracy of 95% on test images. © 2013 IEEE.

关键词： Vehicles

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Sensor Fault Detection with Online Sparse Least Squares Support Vector Machine

Sensor Fault Detection with Online Sparse Least Squares Supp...

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第三十二届中国控制会议

作者： Guo Su Deng Fang Sun Jian Li Fengmei School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex

ISBN: (纸本)9781479900305

In this paper,we present the theory of online sparse least squares support vector machine(OS-LSSVM) for prediction and propose a predictor with OS-LSSVM to detect sensor *** principle of the predictor and its online algorithm are *** with the traditional least squares support vector machine(LSSVM),OS-LSSVM has an advantage on training speed owing to the online training algorithm based on the base vector *** real-time output data of sensor is employed as the training vector to establish the regression *** method is compared with the LSSVM predictor in the *** typical faults of sensors are investigated and the simulation result indicates that the OS-LSSVM predictor can diagnose sensor fault accurately and rapidly,thus it is especially suitable for online sensor fault detection.

关键词： sensor fault detection OS-LSSVM LSSVM

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Design and optimization of a high-bandwidth low-speed high-torque permanent magnetic synchronous motor

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Diangong Jishu Xuebao/Transactions of China Electrotechnical Society 2014年 29卷 108-114页

作者： Chen, Hao Liu, Xiangdong Zhao, Jing Key Laboratory for Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Beijing100081 China

In order to get a better dynamic response for actuator in condition of low-speed, a permanent magnet synchronous motor (PMSM) in direct-drive application with the characteristics of high-bandwidth, low-speed and high-torque was designed in this paper. Based on the requirement of high-bandwidth, the motor's inertia should be kept low to acquire a large acceleration. The limitation of space size makes motor work in non-linear region, which will increase the electrical capacity. This paper took the relationship between inertia, physical size and electrical capacity into consideration and an optimal result was selected by the method of cyclic iteration. According to the characteristics of large-torque motors, the reason of low power factor was further analyzed. In order to improve the motor performance, optimal design was taken on modified Taguchi method based on time-stepping Finite Element Method (FEM) in this paper. First, five structural parameters (pole-arc coefficient, etc.) were taken as optimization variables and four motor performance indexes (power factor, etc.) as optimization objectives. Then, the multi-objective multivariable optimization was adopted. Finally, The results of FEM and prototype experiment verified the advantage and effectiveness of the modified Taguchi method. ©, 2014, Diangong Jishu Xuebao/Transactions of China Electrotechnical Society. All right reserved.

关键词： Finite element method

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Decentralized Filtering a Multi-Agent system with Local Parametric Couplings Based On Kalman Filter

Decentralized Filtering a Multi-Agent System with Local Para...

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第25届中国控制与决策会议

作者： Yini Lv Hongbin Ma Mengyin Fu Chenguang Yang Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institution of Technology School of Computing and Mathematics Plymouth University

ISBN: (纸本)9781467355339

In this paper, decentralized filtering of multiagent systems with coupling uncertainties is proposed and investigated. The considered multi-agent system is composed of many agents, each of which evolves with a discrete-time stochastic linear time-varying dynamics, and every agent can be locally influenced by its neighbor agents. Therefore the states evolution of each agent is not only related with its previous states but also related with its neighbors' previous states in the linear dynamic system. Communication limitations existing in the considered multi-agent system restrict that each agent can only observe its own measurements (outputs) and its neighbor agents' outputs while the states are invisible to any agent. Because of communication limitations and information constraints, without knowing the coupling gains of the local interactions, it is not easy for each agent to estimate its states by traditional kalman filter or other state observers, which were extensively discussed in the literature. In this preliminary study, for the considered coupled linear discrete-time multiagent system with uncertain linear local couplings, based on the key idea of state augmentation and the certainty-equivalence principle borrowed from the area of adaptive control, we propose an efficient decentralized kalman filtering scheme, for each agent, to simultaneously estimate the unknown states and coupling parameters, and extensive simulations are conducted, which have clearly verified the effectiveness of the proposed decentralized filtering scheme.

关键词： multi-agent system, kalman filter, parametric couplings, parameter estimation, states estimation, decentralized filtering

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Hand-eye servo and flexible control of an anthropomorphic arm

Hand-eye servo and flexible control of an anthropomorphic ar...

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2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013

作者： Ma, Gan Huang, Qiang Yu, Zhangguo Chen, Xuechao Meng, Libo Sultan, Malik Saad Zhang, Wen Liu, Yan IRI School of Mechatronical Engineering Beijing Institute of Technology Beijing China Key Laboratory of Biomimetic Robots and Systems Ministry of Education China Key Laboratory of Intelligent Control and Decision of Complex System China

ISBN: (纸本)9781479927449

One key technology for a humanoid robot is to sense the environment accurately and control the movement of the robot in realtime. This paper addresses this problem and focuses on the visual servoing control of an anthropomorphic robotic arm. A high precision, image-based visual servo algorithm for an eye-in-hand visual system is proposed to control the movement of the anthropomorphic arm. The control objective of the controller is to lock all the feature points at fixed points on the image plane. Then, impedance control law is implemented to keep the flexible of the arm. Finally, the effectiveness of the proposed methods are verified by experiments on our humanoid robotic arm platform. © 2013 IEEE.

关键词： Anthropomorphic robots

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Sensor fault detection with online sparse least squares support vector machine

Sensor fault detection with online sparse least squares supp...

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32nd Chinese control Conference, CCC 2013

作者： Su, Guo Fang, Deng Jian, Sun Li, Fengmei School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 China

ISBN: (纸本)9789881563835

In this paper, we present the theory of online sparse least squares support vector machine (OS-LSSVM) for prediction and propose a predictor with OS-LSSVM to detect sensor fault. The principle of the predictor and its online algorithm are introduced. Compared with the traditional least squares support vector machine (LSSVM), OS-LSSVM has an advantage on training speed owing to the online training algorithm based on the base vector set. The real-time output data of sensor is employed as the training vector to establish the regression model. This method is compared with the LSSVM predictor in the experiment. Three typical faults of sensors are investigated and the simulation result indicates that the OS-LSSVM predictor can diagnose sensor fault accurately and rapidly, thus it is especially suitable for online sensor fault detection. © 2013 TCCT, CAA.

关键词： Vectors

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Extension of SGMF Using Gaussian Sum Approximation for Nonlinear/Non-Gaussian Model and Its Application in Multipath Estimation

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Acta Automatica Sinica 2013年第1期39卷 1-10页

作者： Jie CHEN Lan CHENG Ming-Gang GAN Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing 100081 China

The multipath estimation of global navigation satellite system (GNSS) signal is actually the state estimation of nonlinear/non-Gaussian systems. The extension of sliced Gaussian mixture filter (ESGMF) based on Gaussian sum approximation is proposed for the state estimation of nonlinear/non-Gaussian state space, and the probability density function (PDF) expression of states is derived recursively for a time varying system. Resampling is applied to the prediction PDF to reduce the complexity of Bayesian inference. The simulation result of multipath estimation with ESGMF shows that the ESGMF algorithm performs better in accuracy than the algorithms based on particle filter (PF) and extended Kalman filter (EKF).

关键词： Non-Gaussian noise Gaussian sum probability density function (PDF) sliced Gaussian mixture filter (SGMF) multipath estimation

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Comparison of several filtering methods for linear multi-agent systems with local unknown parametric couplings

Comparison of several filtering methods for linear multi-age...

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3rd IFAC Conference on intelligent control and Automation Science, ICONS 2013

作者： Lv, Yini Ma, Hongbin Yang, Chenguang Fu, Mengyin Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 China School of Computing and Mathematics Plymouth University United Kingdom

ISBN: (纸本)9783902823458

In this paper, several filtering methods for a class of discrete-time stochastic linear time-varying multi-agent systems with local coupling uncertainties have been investigated. Every agent can only observe its own measurements (outputs) and its neighbor agents' outputs while the states are invisible to any agent because of communication limitations existing in the considered multi-agent system. Because of the information constraints, without knowing the coupling gains of the local interactions, it is not easy for each agent to estimate its states by traditional Kalman filter or other state observers. Noting of the existence of coupling uncertainties in many practical applications, this paper introduces one general framework of decentralized filtering problem of multi-agent systems. For the considered system, based on the key idea of state augmentation and the certainty-equivalence principle borrowed from principles in adaptive control, we introduce several filtering methods to resolve the fundamental problem considered in this paper. By conducting extensive simulations, the consuming time and estimation errors of every method are compared for one typical example, which suggests which method is more precise and faster. Copyright © 2013 IFAC.

关键词： Kalman filters

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New method of sparse visual saliency feature extraction and application in unmanned vehicle environment sensing

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Information Technology Journal 2013年第20期12卷 5914-5921页

作者： Du, Ming-Fang Wang, Jun-Zheng Li, Jing Cao, Hai-Qing Key Laboratory of Complex System Intelligent Control Decision Beijing Institute of Technology Ministry of Education 100081 Beijing China

A new method based on Hessian matrix threshold of finding local low-level saliency features is proposed in this study after the standard local invariant feature extraction algorithm SRUF (Speeded Up Robust Features) is analyzed. In this method, the number of saliency feature points can change with the change of Hessian threshold. The saliency feature points will become sparser when Hessian threshold becomes larger. When a certain extreme threshold which is defined as Hessian Threshold Node is reached, the retained discriminative feature points are remarkable stability characteristics, also make up the best sparse saliency features set. This method is applied in the unmanned vehicle environment sensing system to help extract the before going vehicle's saliency feature and realize object tracking and obstacle detection. Experiment results show that this is an quick and robust method to determine saliency feature points quantitatively and is very suitable for the occasion which has strong demand on real-time. © 2013 Asian Network for Scientific Information.

关键词： Object detection

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