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Chinese control Conference (CCC)

作者： Xinmei Wang Zhenzhu Liu Leimin Wang Feng Liu Wei Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

In this paper, a new unscented Kalman filter (Unscented Kalman filter, UKF) for nonlinear system with both one-step randomly delayed measurements and colored measurement noises is proposed. Firstly, the first-order Markov sequence model is used to whiten colored noise, at the same time, an independent and identically distributed Bernoulli variable is used to model the delay of measurement data transmission, then the model of nonlinear one-step randomly time delay system with colored noise whitening is established. Secondly, filter recursion formula of UKF under the above model is proposed through unscented transformation (Unscented transformation, UT) to calculate the posterior mean and covariance of the nonlinear state based on the Bayesian filter framework. The proposed new UKF method can effectively deal with the issue that traditional UKF is failure under the condition of one-step randomly delayed measurements and colored measurement noises. The efficiency and superiority of the proposed method are illustrated in a numerical example for a target tracking problem.

关键词： Noise measurement Colored noise State estimation Delays Nonlinear systems Target tracking

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Image Jacobian Matrix Estimation based on Fuzzy Adaptive Robust Kalman Filter

Image Jacobian Matrix Estimation based on Fuzzy Adaptive Rob...

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Chinese control Conference

作者： Zhenzhu Liu Xinmei Wang Zhenjiang Feng Zhongzhao Xie Shuai Ke Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

Aiming at the problem of image Jacobian matrix estimation, this paper proposes a method to get the motion state estimation of the object feature point at the current time by using the combination of robust Kalman filter and fuzzy adaptive method from the image feature space, and the estimation of the image Jacobian matrix can be obtained. Firstly, an adaptive robust decorrelation Kalman filler algorithm with colored measurement noise is proposed by reconstructing process equation and measurement equation and combining the mathematical characteristics of the standard Kalman filter noise. Secondly, by monitoring if the ratio between theoretical residual and actual residual is near 1, the fuzzy inference system constantly adjust the weighted measurement noise covariance and recursively correct the measurement noise covariance of the adaptive Kalman filter, and thus be able to estimate the position and velocity of the object feature point at the current time in the image space more accurately, then the estimation of image Jacobian matrix can be achieved accurately under unknown dynamic environment. The feasibility and superiority of the proposed method can be verified by the simulation and experimental results.

关键词： Jacobian matrices Noise measurement Kalman filters Mathematical model Estimation Current measurement Robots

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An optimization method for coupling matrix elements of microwave filter

An optimization method for coupling matrix elements of micro...

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第37届中国控制会议

作者： Xiao-Long Zhuang Wei-Hua Cao Yan Yuan Can Liu Yu-Sheng Guo School of Automation China University of Geosciences China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper proposes a coupling matrix element optimization method for microwave filters. The traditional method is more complex and does not directly optimize the filter coupling matrix elements. The firefly algorithm optimization method used in this paper directly optimizes the elements of the N + 2 order coupling matrix. Compared with the traditional method, it has better speed and optimization effect. First, the elements of the specific coupling matrix are introduced into the optimization algorithm to be executed, then the matrix is iterated through the set objective function. Finally, when the optimized data is within the allowable range, the optimization of the elements of the coupling matrix is stopped and optimization is performed. The resulting coupling matrix outputs the response. To prove the effectiveness of the proposed method, three methods were used to optimize the coupling matrix elements of the fourth-order filter and compare the final optimization results.

关键词： computer aided tuning coupling matrix ploynomial model extraction optimization

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Synchronization control of star network based on quorum sensing mechanism 8

Synchronization control of star network based on quorum sens...

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8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Liu, Feng Mateus, Felix Mei, Qicheng Wu, Xianyong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China College of Electrical Engineering and New Energy China Three Gorges University Yichang443002 China

In this paper, we combined the quorum sensing mechanism of bacteria with the Repressilator oscillator to describe the rhythmicity of bacteria. It is found that the transmission of signal molecules between cells produces a star network, and the dynamical equation of the star network is given. Then, the error projection synchronization of star networks is defined and analyzed. A Lyapunov function is constructed to prove that the error system will be close to zero within a certain time under the control of the controller. Finally, a four node star network is used for simulation. The results show that the controller can achieve the desired results. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Synchronization

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Research and Implementation of Target Tracking Algorithm Based on Convolution Neural Network

Research and Implementation of Target Tracking Algorithm Bas...

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Chinese control Conference

作者： Li Jun Zhang Chen Wang Xing Jin School of Automation China University of Geosciences Wuhan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Video target tracking is an important research topic in computer vision, and has been widely used in video surveillance, robot, human-computer interaction and so on. The emergence of large data age and the emergence of in-depth learning methods provide a new opportunity for the study of video target tracking. This paper first analyzes the research problems of video target tracking at present, analyzes the characteristics and trends of video target tracking in the new period, introduces the emerging recursive neural network frame structure, combined with Kalman filterAnd the experimental results show that the accuracy and robustness of the target tracking based on the convolution neural network algorithm are all good.

关键词： Target tracking Convolutional neural networks Feature extraction Convolution Training Kalman filters Image color analysis

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Convolutional Neural Networks for Facial Expression Recognition with Few Training Samples

Convolutional Neural Networks for Facial Expression Recognit...

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Chinese control Conference

作者： Zhongzhao Xie Yongbo Li Xinmei Wang Wendi Cai Jing Rao Zhenzhu Liu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

Facial expression recognition (FER) plays an important role in human-machine interaction. An assistant robot having a close interaction with human being should be able to recognize human facial expression. FER is a non-trivial problem because each individual has his own way to reveal his emotion and the facial expressions of two different persons may not be totally identical. Hence, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing steps. The experiments employed to evaluate our technique were carried out using two largely used public databases(CK+, JAFFE). A study of the impact of each image pre-processing operation in the accuracy rate is presented. The proposed method: achieves competitive results when compared with other facial expression recognition methods-97.85% of accuracy in the CK+ database-it is fast to train, and it allows for real time facial expression recognition with standard computers.

关键词： Training Face recognition Convolutional neural networks Neurons Convolution Feature extraction

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Synchronous Detector for GMI Magnetic Sensor Based on Lock-in Amplifier

Synchronous Detector for GMI Magnetic Sensor Based on Lock-i...

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Chinese control Conference (CCC)

作者： Jinchao Wang Fang Jin Lei Zhu Zhi Zhao Hengchang Rao Junlei Song Kaifeng Dong M Wenqin Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

A new concept of a synchronous detector for Giant Magneto-Impedance (GMI) sensors is presented. This concept combines a lock-in amplifier, with outstanding capabilities, high speed and a feedback approach that ensures the amplitude detection with easily adjustable gain. The synchronous detector is capable of measuring high-frequency and very low amplitude signals without the use of diode-based active rectifiers or analog switches. In comparison with most of the commercially available diode-based peak detectors, the linearity of the synchronous detector is generally better, especially for low level amplitudes. The synchronous detector has been used for the amplitude measurement of single frequency sine signal and for the demodulation of amplitude-modulated signal. It has also been successfully integrated in a GMI sensor prototype. Magnetic field measurements in open- and closed-loop of this sensor have been conducted. The measured sensitivity was about 1.02 V/Oe in open-loop while it was 0.12V/Oe in close-loop. The above research provides technical accumulation for the design of sensor nodes based on GMI sensor wireless sensor networks.

关键词： Detectors Magnetic sensors Sensitivity Magnetic cores Voltage measurement Linearity

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Flocking of Second-Order Multiagent systems with Connectivity Preservation Based on Algebraic Connectivity Estimation

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IEEE Transactions on Cybernetics 2017年第4期47卷 1067-1077页

作者： Fang, Hao Wei, Yue Chen, Jie Xin, Bin School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

The problem of flocking of second-order multiagent systems with connectivity preservation is investigated in this paper. First, for estimating the algebraic connectivity as well as the corresponding eigenvector, a new decentralized inverse power iteration scheme is formulated. Then, based on the estimation of the algebraic connectivity, a set of distributed gradient-based flocking control protocols is built with a new class of generalized hybrid potential fields which could guarantee collision avoidance, desired distance stabilization, and the connectivity of the underlying communication network simultaneously. What is important is that the proposed control scheme allows the existing edges to be broken without violation of connectivity constraints, and thus yields more flexibility of motions and reduces the communication cost for the multiagent system. In the end, nontrivial comparative simulations and experimental results are performed to demonstrate the effectiveness of the theoretical results and highlight the advantages of the proposed estimation scheme and control algorithm. © 2017 IEEE.

关键词： Multi agent systems

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Fault Tolerant control for a Class of Nonlinear System based on Active Disturbance Rejection control and RBF Neural Networks 36

Fault Tolerant Control for a Class of Nonlinear System based...

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第36届中国控制会议

作者： Lushan Zhou Liling Ma Junzheng Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

ISBN: (纸本)9781538629185

In this paper,a fault tolerant control method based on active disturbance rejection control(ADRC) and radial basis function neural network(RBFNN) is proposed for a class of multi-input-multi-output nonlinear system with actuator faults,components faults and sensor *** proposed method does not rely on the plant *** regarding the faults and plant uncertainties as the disturbance,through the observation of extended state observer and the compensation of feedback control signal,this method achieves the fault tolerance control of the plant with component fault and actuator *** sensor faults,in this work,radial basis function neural network is applied to estimate the real output of the *** this output estimation is utilized by active disturbance rejection control to achieve the fault tolerance of ***,the effectiveness of the proposed method is validated by the simulation results of the three-tank system.

关键词： Fault tolerant control active disturbance rejection control radial basis function neural network

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Event-Triggered Nonlinear Model Predictive control with Bounded Disturbances and State-dependent Uncertainties

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IFAC-PapersOnLine 2017年第1期50卷 9308-9314页

作者： Wang M. Sun J. Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 China

In this paper, two event-triggered nonlinear model predictive control(NMPC) strategies based on Lyapunov function method for discrete-time nonlinear systems with bounded disturbances and state-dependent uncertainties are proposed. The corresponding event-triggered control law is designed and the feasibility is analyzed. Stability of the event-triggered NMPC systems is studied and sufficient stability conditions are obtained. Finally, simulation results show advantages of the two event-triggered strategies on performance and computational burden. © 2017

关键词： event-triggered control,nonlinear model predictive control, Lyapunov function

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