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第37届中国控制会议

作者： Yeyun Cai Fang Deng Jiachen Zhao Huangbin Qiu Xinyu Fan Zelang Liang School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems

Wearable devices have appeared in every aspect of our lives, from small items such as wristbands and eyeglasses to finished garments as shoes and clothing. With the promoting of scientific development, intelligent technology is also combined with wearable devices to form a unique network that depends on the human body. The increasing demand for equipment has made smart wearable devices a research hotspot. Such equipment is usually small, light-weight, good flexibility, and can be adapted to the complex use of the environment. But these advantages just become the limitations of power supply for ***, the fixed charger can not be used to charge the device during use, and the volume of such a special battery can not be increased in order to increase the storage capacity. Therefore, how to solve the problem of power suppling for the wearable device effectively is a big challenge. Based on this, the paper proposes a distributed system of smart wearable energy harvesting, which considers the human body as the core and uses a variety of ubiquitous energy sources such as solar energy, thermal energy and mechanical energy. The system makes a combination of wearable and distributed design to form a wireless body area network,and provides a stable power output for smart wearable devices.

关键词： wearable energy harvesting distributed system human body

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Off-Policy Q-Learning for Infinite Horizon LQR Problem with Unknown Dynamics

Off-Policy Q-Learning for Infinite Horizon LQR Problem with ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Xinxing Li Zhihong Peng Li Liang Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

In this paper, a novel online Q-Iearning approach is proposed to solve the Infinite Horizon Linear Quadratic Regulator (IHLQR) problem for continuous-time (CT) linear time-invariant (LMI) systems. The proposed Q-Iearning algorithm employing off-policy reinforcement learning (RL) technology improves the exploration ability of Q-Iearning to the state space. During the learning process, the Q-Iearning algorithm can be implemented just using the data sets which just contains the information of the behavior policy and the corresponding system state, thus is data- driven. Moreover, the data sets can be used repeatedly, which is computationally efficient. A mild condition on probing noise is established to ensure the converge of the proposed Q-Iearning algorithm. Simulation results demonstrate the effectiveness of the developed algorithm.

关键词： Silicon Optimal control Convergence Approximation algorithms Manganese Regulators Adaptation models

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3D Deep Learning for 3D Printing of Tooth Model

3D Deep Learning for 3D Printing of Tooth Model

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yiwen Liu Xiuqin Shang Zhen Shen Bin Hu Zhihai Wang Gang Xiong Faculty of Information The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing University of Technology Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Faculty of Information Beijing University of Technology Beijing China

Recently, with the development of both 3D sensors and 3D virtual network that bring the needs of interaction with the real world, many 3D applications burst out. However, it is difficult to understanding these three-dimensional scenes with a fixed program. Then, a data-driven method is required to process these 3D data, which brings a strong demand of 3D Deep Learning in 3D data. Towards this goal, with an end-to-end deep learning, the experiment is based on PointNet++, a well proposed method for feature extraction. The experiment optimizes the network structure and parameters to improve the classification results. Finally, the network is applied to tooth model for classification and identification so that the dental model can be found from different perspectives.

关键词： Three-dimensional displays Deep learning Transforms Cameras Object segmentation Image segmentation Sensors

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Stochastic stability of evolutionary prisoner's dilemma

Stochastic stability of evolutionary prisoner's dilemma

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2018 Australian and New Zealand control Conference, ANZCC 2018

作者： Liang, Haili Zhou, Zhao Zhang, Fan Peng, Chen Wang, Yu-Long Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Mathematics Southeast University Nanjing China

ISBN: (纸本)9781538666173

In this paper, we study two-player evolutionary prisoner's dilemma on regular graphs and identify the stochastically stable equilibria for infinite populations. We consider four different update rules: Birth-death(BD), death-birth(DB), imitation(IM) and pairwise comparison(PC). With the same values of cost and benefit of cooperation, we show that there is a unique stochastically stable equilibrium for evolutionary prisoner's dilemma on regular graphs. If the benefit-to-cost ratio is larger than k + 2 (k is the degree of a regular graph), the networked game has a higher fraction of cooperators than that for a well-mixed population. Under certain conditions, the lower graph connectivity can lead to the emergence of more cooperators. Besides theoretical analysis, we demonstrate our results through numerical computations and simulations as well. © 2018 IEEE.

关键词： Stochastic 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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Multi-robot Path Planning and Trajectory Smoothing

Multi-robot Path Planning and Trajectory Smoothing

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Hub Ali Gang Xiong Huaiyu Wu Bin Hu Zhen Shen Hongxing Bai School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences China Hubei Jointown Technology Co. Ltd Artificial Intelligence Research Institute Wuhan China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

In this paper we consider a problem in task execution for multi-robot trajectory planning with collision avoidance in a shared working environment. Consider two or more robots generating trajectories towards their respective goal positions. The collision may occur if their trajectory coordinates are intersecting at a point or follow the same path segment simultaneously. The central planner is introduced to control robot motion in the collision state and to reduce the complexity of the multi-robot path planning system. The global path for every robot is generated by the $\mathrm{A}^{*}$ algorithm in a grid-based environment. The path has presented a sequence of optimal grid numbers and later transformed into Cartesian coordinates for smooth trajectory generation. The central planner takes an optimal grid sequence for every robot to analyze the collision state according to its cost value. It regenerates the trajectories to minimize the complexity cost value and replaces the previous trajectory based on minimum cost value. In the collision state, the central planner allows one robot at a time to pass along the conflict path segment and hold others in queue at a safety offset distance until the previous robot passes safely. The algorithm has been applied to robots working in a shared environment in complex maps and the simulations is performed with MATLAB to calculate the efficiency of this approach for handling collision states in a multi-robot path planning system.

关键词： Robot kinematics Trajectory Collision avoidance Task analysis Resource management

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Design of distributed guidance laws for multi-UAV cooperative attacking a moving target based on reducing surrounding area

arXiv

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arXiv 2019年

作者： Wei, Xiaoqian Yang, Jianying Fan, Xiangru Peking University Beijing 100871 China Science and Technology on Aerospace Intelligent Control Laboratory State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Clarkson University PotsdamNY United States

In this paper, two cooperative guidance laws based on the area around the target of multi- attackers are designed to deal with the problem of cooperative encirclement or simultaneous attack in the case of known target acceleration and unknown target acceleration. Multi-attacker commu- nication network only needs to contain a directed spanning tree, and does not require all attackers to observe the target information, where at least one can observe the target. The components along the attacker-target line of sight in the novel guidance laws can reduce the relative remaining distance between the attacker and the target at the same speed, thus completing simultaneous attack and avoiding the calculation of the remaining time. The components of the guidance laws perpendicular to the attacker-target line of sight can make the normal overload of relative motion zero, so that the trajectory will not be distorted and the collision problem within the attacker group can be avoided. The simulation results verify the practicability of the novel guidance laws. Copyright © 2019, The Authors. All rights reserved.

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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Algebraic Connectivity Estimation Based on Decentralized Inverse Power Iteration

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Asian Journal of control 2017年第2期19卷 805-812页

作者： Wei, Yue Fang, Hao Chen, Jie Xin, Bin School of Automation Beijing Institute of Technology Beijing 100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 China

In this work, we propose a new scheme to estimate the algebraic connectivity of the graph describing the network topology of a multi-agent system. We consider network topologies modeled by undirected graphs. The main idea is to propose a new decentralized conjugate gradient algorithm and a decentralized compound inverse power iteration scheme. The matrix inversion computation in this scheme is replaced by solving the non-homogeneous linear equations relying on the proposed decentralized conjugate gradient algorithm. With this scheme, we can achieve a fast convergence rate in estimating the algebraic connectivity by setting the parameter μ properly. Simulation results demonstrate the effectiveness of the proposed scheme. © 2016 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Network topology

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