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第三十八届中国控制会议

作者： Juan Li Bin Xin School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology

The decomposition strategy and the ε-constraint method are two important strategies in the field of multi-objective optimization. DMOEA-εC first attempts to incorporate the ε-constraint method into the decomposition strategy and solve a multi-objective optimization problem(MOP) via optimizing a series of scalar constrained subproblems collaboratively with the help of information from neighboring subproblems. However, given the inefficiency of applying DMOEA-εC to deal with many-objective optimization problems(MaOPs), a two-stage upper bound vectors generation procedure is proposed to generate widely spread upper bound vectors in a high-dimensional space. Besides, a boundary points maintenance mechanism is put forward to remedy the diversity loss of a population in DMOEA-εC. Based on the above, DMOEA-εC with the two-stage upper bound vectors generation procedure and the boundary points maintenance mechanism, named as IDMOEA-εC, is presented for MaOPs. IDMOEA-εC is compared with four state-of-the-art many-objective evolutionary algorithms, including HypE, NSGA-Ⅲ, MOEADD, and Two Arch2. Experimental studies demonstrate that IDMOEA-εC outperforms or performs competitively against these algorithms on the majority of sixteen DTLZ test instances with up to 10 objectives.

关键词： Many-objective optimization decomposition ε-constraint method two-stage upper bound vectors generation procedure boundary points maintenance mechanism

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An Efficient Restart-Enhanced Genetic Algorithm for the Coalition Formation Problem 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Guo, Miao Xin, Bin Chen, Jie Wang, Yipeng School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

In multi-agent system (MAS), the coalition formation (CF) is an important problem focusing on allocating agents to different tasks. In this paper, the single-task single-coalition (STSC) formation problem is considered. The mathematical model of the STSC problem is built with the objective of minimizing the total cost with the ability constraint. Besides, an efficient restart-enhanced genetic algorithm (REGA) is designed to solve the STSC problem. Furthermore, this paper constructs a comparison experiment, employing a random sampling method, an estimation of distribution algorithm and a genetic algorithm without restart strategy as competitors. The results of statistical analysis by the Wilcoxon’s rank-sum test demonstrate that the designed REGA performs better than its competitors in solving the STSC cases of different scales. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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A Guide for Human Walking Model and control — Insights from Mechanical Property Analysis of Human Walking

A Guide for Human Walking Model and Control — Insights from...

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Chinese control and decision Conference, CCDC

作者： Guangrong Chen Bowen Hou Sheng Guo Junzheng Wang Robotics Research Center Beijing Jiaotong University Beijing P.R. China Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention Beijing P.R. China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P.R. China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This paper aims to establish the simplest human walking model and provide a guide for controlling biped robots. Firstly, a human motion capture system was utilized to sample the motion data of human walking and a representative subject was selected. Secondly, one of the simplest human walking model was set up. The mechanical property analysis of human walking was done based on the established walking model via fitting. Thirdly, the feasibility and effectiveness of proposed walking model were validated by numerical simulations. Finally, the intrinsical relationships among human walking, biped walking model and control were discussed to provide a guide and insight for controlling biped robots.

关键词： Legged locomotion Numerical models Force Analytical models Numerical simulation

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Exponential input-to-state stability of impulsive stochastic fuzzy Cohen-Grossberg neural networks with distributed infinite transmission delays

Exponential input-to-state stability of impulsive stochastic...

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第三十八届中国控制会议

作者： Weisong Zhou Dan Shi Wei Zhu Key Lab of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications

In this paper, we study a class of impulsive stochastic fuzzy delayed Cohen-Grossberg neural networks with distributed infinite transmission delays. By Razumikhin technique and differential inequalities, some sufficient conditions ensuring the mean-square exponential input-to-state stability of our considered Cohen-Grossberg neural networks are obtained. These results can include constant delay and bounded distributed delay as its special cases, and generalize and improve some earlier publications. A numerical example is demonstrated to verify the theoretical results.

关键词： Impulsive Stochastic Infinite delays Cohen-Grossberg neural networks Exponential input-to-state stability

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Robust human-computer interaction for unstable camera systems

Robust human-computer interaction for unstable camera system...

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作者： Zhu, Hao You, Qing Chen, Wenjie School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

A lot of vision systems have been embedded in devices around us, like mobile phones, vehicles and UAVs. Many of them still need interactive operations of human users. However, specifying accurate object information could be a challenging task due to video jitters caused by camera shakes and target motions. In this paper, we first collect practical hand drawn bounding boxes on real-life videos which are captured by hand-held cameras and UAV-based cameras. We give a deep look into human-computer interactive operations on unstable images. The collected data shows that human input suffers heavy deviations which are harmful to interaction accuracy. To achieve robust interactions on unstable platforms, we propose a target-focused video stabilization method which utilizes a proposal-based object detector and a tracking-based motion estimation component. This method starts with a single manual click and outputs stabilized video stream in which the specified target stays almost stationary. Our method removes not only camera jitters but also target motions simultaneously, therefore offering an comfortable environment for users to do further interactive operations. The experiments demonstrate that the proposed method effectively eliminates image vibrations and significantly increases human input accuracy. © Copyright 2018 The Institute of Electronics Information and Communication Engineers.

关键词： Cameras

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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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A statistical learning approach to reactive power control in distribution systems

arXiv

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arXiv 2019年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian 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

Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the 'optimal' reactive power control with only several matrix-vector multiplications. The merits of this novel statistical learning approach are computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real data corroborate these practical merits. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Two-timescale voltage control in distribution grids using deep reinforcement learning

arXiv

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arXiv 2019年

作者： Yang, Qiuling Wang, Gang Sadeghi, Alireza Giannakis, Georgios B. Sun, Jian 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

Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of electric vehicles and renewable generators. Existing approaches to maintaining bus voltage magnitudes within the desired region can cope with either traditional utility-owned devices (e.g., shunt capacitors), or contemporary smart inverters that come with distributed generation units (e.g., photovoltaic plants). The discrete on-off commitment of capacitor units is often configured on an hourly or daily basis, yet smart inverters can be controlled within milliseconds, thus challenging joint control of these two types of assets. In this context, a novel two-timescale voltage regulation scheme is developed for distribution grids by judiciously coupling data-driven with physicsbased optimization. On a faster timescale, say every second, the optimal setpoints of smart inverters are obtained by minimizing instantaneous bus voltage deviations from their nominal values, based on either the exact alternating current power flow model or a linear approximant of it;whereas, on the slower timescale (e.g., every hour), shunt capacitors are configured to minimize the longterm discounted voltage deviations using a deep reinforcement learning algorithm. Extensive numerical tests on a real-world 47-bus distribution network as well as the IEEE 123-bus test feeder using real data corroborate the effectiveness of the novel scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Fuzzy rate analysis of operators and its applications in linear spaces

arXiv

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arXiv 2020年

作者： Zhang, Yijin Li, Honggang Jin, Maoming Lin, Zongbing 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 of 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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Shaping Effect of Phase-locked Loop on Impedance Characteristics of PMSG Wind Turbines Connected to Weak-grid

Shaping Effect of Phase-locked Loop on Impedance Characteris...

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IEEE General Meeting Power& Energy Society

作者： Feiya Wan Zhen Li Bin Liu Guanghui Li Xiaoliang Dong Xi Chen Samson S. Yu Amanullah M. T. Oo Zhenyu Shan Xiangdong Liu School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing China China Electric Power Research Institute Beijing China State Grid Corporation of China and North China Branch of State Grid Corporation of China Beijing China GEIRI North America USA School of Engineering Deakin University 75 Pigdon Rd Waurn Ponds Victoria Australia School of Electrical and Control Engineering North China University of Technology Beijing China

ISBN: (数字)9781728155081

ISBN: (纸本)9781728155098

With the booming installed capacity of permanent magnet synchronous generator (PMSG) for wind energy generation, the grid becomes weaker and the oscillation events are frequently observed in weak-grid-tied large-scale grid-connected PMSG wind farms. Although the impedance-based method is effective to analyze the stability of grid-connected systems, the mathematical relationship between external impedance characteristics and the internal controllers has not been quantitatively analyzed in depth, which is beneficial to the controller design. This paper studies the influence of phase-locked loop (PLL) on the impedance characteristics based on the analytical sequence impedance model of PMSG with typical topology and control structure. The shaping effect of PLL on the impedance characteristics is described so as to clearly describe the relationship between the external impedance characteristics of the wind turbines and the internal controller. Finally, the stability analysis of the grid-connected PMSG wind turbine system is provided in Matlab and simulation results are given to verify the oscillating mechanism caused by PLL.

关键词： Synchronous generators Stability analysis Wind turbines Topology Impedance Phase locked loops Oscillators

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