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arXiv 2019年

作者： Li, Yuzhe Shi, Dawei Chen, Tongwen State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110004 State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada

In this paper, we first consider a pinning node selection and control gain co-design problem for complex networks. A necessary and sufficient condition for the synchronization of the pinning controlled networks at a homogeneous state is provided. A quantitative model is built to describe the pinning costs and to formulate the pinning node selection and control gain design problem for different scenarios into the corresponding optimization problems. Algorithms to solve these problems efficiently are presented. Based on the developed results, we take the existence of a malicious attacker into consideration and a resource allocation model for the defender and the malicious attacker is described. We set up a leader-follower Stackelberg game framework to study the behaviour of both sides and the equilibrium of this security game is investigated. Numerical examples and simulations are presented to demonstrate the main results. Copyright © 2019, The Authors. All rights reserved.

关键词： complex networks

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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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Approach and landing guidance design for reusable launch vehicle using multiple sliding surfaces technique

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Chinese Journal of Aeronautics 2017年第4期30卷 1582-1591页

作者： Xiangdong LIU Fengdi ZHANG Zhen LI Yao ZHAO School of Automation Beijing Institute of Technology Beijing 100081 China National Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing100081 China China Academy of Launch Vehicle Technology Beijing 100076 China

An autonomous approach and landing（A＆L） guidance law is presented in this paper for landing an unpowered reusable launch vehicle（RLV） at the designated runway touchdown. Considering the full nonlinear point-mass dynamics, a guidance scheme is developed in threedimensional space. In order to guarantee a successful A＆L movement, the multiple sliding surfaces guidance（MSSG） technique is applied to derive the closed-loop guidance law, which stems from higher order sliding mode control theory and has advantage in the finite time reaching *** global stability of the proposed guidance approach is proved by the Lyapunov-based *** designed guidance law can generate new trajectories on-line without any specific requirement on off-line analysis except for the information on the boundary conditions of the A＆L phase and instantaneous states of the RLV. Therefore, the designed guidance law is flexible enough to target different touchdown points on the runway and is capable of dealing with large initial condition errors resulted from the previous flight phase. Finally, simulation results show the effectiveness of the proposed guidance law in different scenarios.

关键词： Finite time control Landing guidance Lyapunov stability Reusable launch vehicle Sliding mode control

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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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Distributed dimensionality reduction of industrial data based on clustering

Distributed dimensionality reduction of industrial data base...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yongyan Zhang Guo Xie Wenqing Wang Xiaofan Wang Fucai Qian Xulong Du Jinhua Du Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing China

Large amounts of data are produced in system operation, and how to extract effective information from these data has become an important research topic in the industrial application. Dimensionality reduction is a way to refine the data. Because of the low efficiency of the existing methods, these methods can't discover the internal structure of the data. Regarding these problems, a distributed method of dimensionality reduction based on clustering is proposed, which includes the following steps:(1) Clustering the data into some small classes according to the similarity between the data variables; (2) reducing the dimension of data in a small class after being clustered respectively; (3) merging the data after being reduced dimension; (4) classifying the data after being merged by support vector machine (SVM). The data in the simulation is the test data, and the results show that the methods proposed in this paper are better than the existing dimensionality reduction methods.

关键词： Dimensionality reduction Support vector machines Clustering algorithms Distributed databases Data mining Principal component analysis Classification algorithms

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Small-scale intersection scan model for UGV in urban environment

Small-scale intersection scan model for UGV in urban environ...

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2017 American control Conference, ACC 2017

作者： Yang, Yi Hao, Li Zhu, Hao Ningyi, Lyu Wenjie, Song School of Automation State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781509059928

Intersection detection is a critical capacity for an Unmanned Ground Vehicle (UGV) to drive safely in structured urban environment. Large-scale intersections stamped on maps have plenty of features for detection while some unmapped small-scale urban intersections are hard to be identified. In this paper, we propose a novel intersection detection method conducted on the basis of Hidden Markov Model (HMM). This method is based on the intersection scan model to obtain the traversable directions to classify the intersection. The scan model is effective in dealing with both LIDAR and visual data. Combination of the scan model and the HMM can accurately estimate the traversable directions in consideration of both real-time and historic data. Results from simulations and real-world experiments have shown the functionality of the presented approach. © 2017 American Automatic control Council (AACC).

关键词： Hidden Markov models

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UAV-aided Large-scale Map Building and Road Extraction for UGV 7

UAV-aided Large-scale Map Building and Road Extraction for U...

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7th IEEE Annual International Conference on CYBER Technology in Automation, control, and intelligent systems, CYBER 2017

作者： Meiling, Wang Huachao, Yu Guoqiang, Feng Yi, Yang Yafeng, Li Tong, Liu School of Automation Beijing Institute of Technology State Key Laboratory of Complex System Intelligent Control and Decision Beijing100081 China

ISBN: (纸本)9781538604892

Due to the limited visual field of UGV, it is difficult to detect the accessible zone in a wide range of area in real time. This paper proposes a UAV-aided autonomous road network construction method where roads are previously unknown for UGV navigation. First, SIFT and RANSAC algorithms are applied to obtain the homography of UAV sequential images, through which these images are stitched to a large-scale map. Next, least square fitting algorithm and RDP algorithm are applied to extract initial road area and then obtain road network suitable for UGV navigation by topological processing and skeleton extraction. The experiments show that this UAV-aided method is effective in generating a large-scale road network map for UGV driving through unknown environment without human intervention. © 2017 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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An Adaptive Sliding Mode controller with The Exponential and Power Reaching Law for Discrete systems

An Adaptive Sliding Mode Controller with The Exponential and...

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第37届中国控制会议

作者： Xiaofei Zhang Hongbin Ma Zhuang Li Wenchao Zao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

With the development of aeronautics and astronautics, the response speed of servo system need be faster. First, In order to improve the dynamic quality of servo system, the exponential and power reaching law, which combines the advantages of the exponential reaching law and the power reaching law, is introduced. Second, the chattering of the sliding mode controller（SMC） with the exponential and power reaching law for discrete systems is investigated. Finally, the adaptive sliding mode controller（ASMC） with the exponential and power reaching law is introduced. The stability of the ASMC with the exponential and power reaching law for discrete systems is analyzed, and the simulation of this approach on one joint of a six degrees of freedom robot is carried out. The experimental results indicate that the ASMC with the exponential and power reaching law is effective in reducing the time of arriving the sliding mode surface. The experimental results also indicate that the ASMC with the exponential and power reaching law may make output error reach zero in a shorter time.

关键词： adaptive sliding mode controller reaching law

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Convolutional Neural Network Features Comparison Between Back-Propagation and Extreme Learning Machine

Convolutional Neural Network Features Comparison Between Bac...

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第37届中国控制会议

作者： Atmane Khellal Hongbin Ma Qing Fei School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Recently deep learning based architectures have been widely deployed in many problems of artificial *** deep learning models, Convolutional Neural Networks（CNN） have been reported in numerous successful applications such as object recognition, and natural language processing. The convolutional neural networks are trained by back-propagating the classification error using the Back-Propagation（BP） algorithm, which requires a large amount of data and slows the training process. To overcome these difficulties, a new fast and accurate approach based on Extreme Learning Machine（ELM） to train any convolutional neural network has been proposed. The developed framework（ELM-CNN） is based on the concept of autoencoding to learn the convolutional filters with biases, by reconstructing the normalized input and the intercept term. In this paper, systematic comparison with traditional back-propagation based training method（BP-CNN） has been made with respect to two aspects qualitative and quantitative. The experimental results on the popular MNIST dataset show that the ELM-CNN algorithm achieves competitive results in terms of generalization performance and up to 16 times faster than the back-propagation based training of CNN.

关键词： Convolutional Neural Network Extreme Learning Machine Features Comparison Visualization

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