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Beijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing 2012年第1期34卷 96-101页

作者： Peng, Zhi-Hong Sun, Lin Chen, Jie School of Automation Beijing Institute of Technology Beijing 100081 China Key Lab. of the Ministry of Education of China for Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing 100081 China

An improved differential evolution algorithm was proposed for solving the online path planning problem of unmanned aerial vehicle (UAV) low-altitude penetration in partially known hostile environments. The algorithm adopts von Neumann topology and improves its structure to maintain the diversity of the population, prevent the population from falling into local optima in the early evolution and speed up the convergence rate in the later evolution as well. The mutation operator of differential evolution is improved to speed up the convergence rate of the algorithm, so that the optimal solution of the multi-objective optimization problem can be found quickly;the coding method combined the absolute Cartesian coordinates with the relative polar coordinates is used to improve the searching efficiency. The simulation experiment of online path planning for UAV low-altitude penetration shows that the proposed algorithm has a better performance than the unimproved differential evolution algorithm.

关键词： Antennas

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Evolution of Cooperation in Snowdrift Game with Multi-Memory Mechanism

Evolution of Cooperation in Snowdrift Game with Multi-Memory...

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Chinese control Conference (CCC)

作者： Yaojun Liu Xingwen Liu Nyima Tashi Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission College of Electrical Engineering Southwest Minzu University Chengdu Sichuan China Engineering Research Center for Tibetan Information Processing School of Information Science and Technology Tibet University Lhasa China

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavior, memory-based research on evolutionary games has proliferated in recent years. In the present paper, inspired by the well-known “Stanford marshmallow experiment” (SME), we proposed a multi-memory mechanism in Snowdrift game (SDG) on spatial lattice networks whose core lies in what follows: Players are divided into two groups according to individual self-control; different memory lengths are set by group and by time in a single realization. The simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary game. More specifically, as memory length increases to an intermediate length, the cooperation level increases as well. For mechanism with short memory length, the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative behavior. These findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation.

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Resonant beam communications with echo interference elimination

arXiv

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arXiv 2021年

作者： Xiong, Mingliang Liu, Qingwen Wang, Gang Giannakis, Georgios B. Zhang, Sihai Zhu, Jinkang Huang, Chuan College of Electronics and Information Engineering Tongji University Shanghai201804 China State Key Lab 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 Minnesota MinneapolisMN55455 United States Key Lab of Wireless-Optical Communications University of Science and Technology of China Hefei Anhui230026 China School of Science and Engineering Chinese University of Hong Kong Shenzhen Guangdong518172 Hong Kong

Resonant beam communications (RBCom) is capable of providing wide bandwidth when using light as the carrier. Besides, the RBCom system possesses the characteristics of mobility, high signal-to-noise ratio (SNR), and multiplexing. Nevertheless, the channel of the RBCom system is distinct from other light communication technologies due to the echo interference issue. In this paper, we reveal the mechanism of the echo interference and propose the method to eliminate the interference. Moreover, we present an exemplary design based on frequency shifting and optical filtering, along with its mathematic model and performance analysis. The numerical evaluation shows that the channel capacity is greater than 15 bit/s/Hz. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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Fuzzy Rate Analysis of Operators and its Applications in Linear Spaces

Research Square

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Research Square 2021年

作者： Zhang, Yijin Li, Honggang Jin, Maoming Lin, Zongbing Nanjing Changjiang Waterway Engineering Bureau China Key Lab of Intelligent Analysis and Decision on Complex Systems Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Pass College Chongqing Technology and Business University Chongqing401520 China Institute of Nonlinear Analysis Research Changjiang Normal University Chongqing400803 China School of Mathematics and Computer Science Panzhihua University Panzhihua617000 China

In this paper, a new concept, the fuzzy rate of an operator in linear spaces is proposed for the very first time. Some properties and basic principles of it are studied. Fuzzy rate of an operator B which is specific i... 详细信息

关键词： Membership functions

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Robust Output Synchronization of Discrete-Time Linear-Time-Invariant Multi-Agent systems Using Phase Tool

Robust Output Synchronization of Discrete-Time Linear-Time-I...

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European control Conference (ECC)

作者： Xin Mao Dan Wang Wei Chen Li Qiu School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Southern University of Science and Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong S.A.R. China

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, the output synchronization in large-scale discrete-time networks is examined by utilizing the novel phase tool, where the agent dynamics are allowed to be significantly heterogeneous. The synchronization synthesis problem is formulated and thoroughly investigated, with the goal of characterizing the allowable heterogeneity among the agents to ensure synchronization under a uniform controller. The solvability condition is provided in terms of the phases of the residue matrices of the agents at the persistent modes. When the condition is satisfied, a design procedure is given, producing a low-gain synchronizing controller. Numerical examples are given to illustrate the results.

关键词： Sufficient conditions Europe Heterogeneous networks Synchronization Multi-agent systems

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General formation control for multi-agent systems with double-integrator dynamics

arXiv

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arXiv 2018年

作者： Wang, Chen Xia, Weiguo Sun, Jinan Fan, Ruifeng Xie, Guangming State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing100871 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China

We study the general formation problem for a group of mobile agents in a plane, in which the agents are required to maintain a distribution pattern, as well as to rotate around or remain static relative to a static/moving target. The prescribed distribution pattern is a class of general formations that the distances between neighboring agents or the distances from each agent to the target do not need to be equal. Each agent is modeled as a double integrator and can merely perceive the relative information of the target and its neighbors. A distributed control law is designed using the limit-cycle based idea to solve the problem. One merit of the controller is that it can be implemented by each agent in its Frenet-Serret frame so that only local information is utilized without knowing global information. Theoretical analysis is provided of the equilibrium of the N-agent system and of the convergence of its converging part. Numerical simulations are given to show the effectiveness and performance of the proposed controller. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile agents

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Almost equitable partitions and controllability of leader–follower multi-agent systems

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Automatica 2021年 131卷

作者： Xiufeng Zhang Jian Sun Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 China Beijing Institute of Technology Chongqing Innovation Center Chonqing 401120 China

In this paper, the controllability of leader–follower multi-agent systems is studied from the perspective of graph theory. It is known that, for a given graph, the number of cells of an almost equitable partition (AEP) provides an upper bound on the controllability index . In this context, the main contribution here is an efficient algorithm to construct the maximal AEP for a given graph with a set of leaders. Specifically, we first establish the relationship between an AEP and the distance partition (DP) for the single-leader setting. It is shown that the cells of an AEP are subsets of cells of the DP. We extend the results to the multiple-leader case and develop a convergent algorithm to efficiently compute the maximal AEP. Finally, numerical examples are provided to show the effectiveness of the proposed method.

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Distributed stochastic proximal algorithm with random reshuffling for non-smooth finite-sum optimization

arXiv

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arXiv 2021年

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Xie, Lihua Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China School of Electronic and Information Engineering Tongji University Shanghai200082 China Ministry of Education Beijing100081 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

The non-smooth finite-sum minimization is a fundamental problem in machine learning. This paper develops a distributed stochastic proximal-gradient algorithm with random reshuffling to solve the finite-sum minimization over time-varying multi-agent networks. The objective function is a sum of differentiable convex functions and non-smooth regularization. Each agent in the network updates local variables by local information exchange and cooperates to seek an optimal solution. We prove that local variable estimates generated by the proposed algorithm achieve consensus and are attracted to a neighborhood of the optimal solution with an (formula presented) convergence rate, where T is the total number of iterations. Finally, some comparative simulations are provided to verify the convergence performance of the proposed algorithm. © 2021, CC BY.

关键词： Optimal systems

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Hierarchical Task Impedance control of a Serial Manipulator for Minimally Invasive Surgery

Hierarchical Task Impedance Control of a Serial Manipulator ...

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IEEE International Conference on Human-Machine systems

作者： Hang Su Chenguang Yang Jiehao Li Yiming Jiang Giancarlo Ferrigno Elena De Momi Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Bristol Robotics Laboratory University of the West of England Bristol United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

ISBN: (数字)9781728158716

ISBN: (纸本)9781728158723

Flexibility and robustness have become key points in the development of surgical robot controller for physical interactions. However, the conventional impedance control schemes unaware of the actual surgical scenario, including complex physical interaction on the robot arm, lead to the loss of accuracy. In this paper, a hierarchical task impedance control scheme is proposed for Minimally Invasive Surgery (MIS) based on an operational space formulation of a 7 DoFs redundant robot. Its redundancy is exploited to guarantee a remote center of motion (RCM) constraint and to provide a flexible workspace for the medical staff to assist physicians. In addition to the achievement of the classical whole-body impedance control, the issue of uncertain disturbances will be addressed by a decoupled adaptive approximation based on a radial basis function neural network (RBFNN) within the control framework. Task performances under the hierarchical task impedance controller were validated and compared with previous work in the literature. Experimental results showed its improved performance in terms of positional error and RCM constraint, regardless of the existing uncertain physical interaction.

关键词： Task analysis Impedance Manipulators Tools Surgery Jacobian matrices

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