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arXiv 2019年

作者： Liang, Shu Zeng, Xianlin Chen, Guanpu Hong, Yiguang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed optimization for resource allocation problems is investigated and a sub-optimal continuous-time algorithm is proposed. Our algorithm has lower order dynamics than others to reduce burdens of computation and communication, and is applicable to weight-balanced graphs. Moreover, it can deal with both local constraints and coupled inequality constraints, and remove the requirement of twice differentiability of the cost function in comparison with the existing sub-optimal algorithm. However, this algorithm is not easy to be analyzed since it involves singular perturbation type dynamics with projected non-differentiable righthand side. We overcome the encountered difficulties and obtain results including the existence of an equilibrium of the algorithm, the sub-optimality in the sense that the solution approaches to the optimal solution as an adjustable parameter tends to zero, and the convergence of the algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Resource allocation

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Parallel intelligence： toward lifelong and eternal developmental AI and learning in cyber-physical-social spaces

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Frontiers of Computer Science 2018年第3期12卷 401-405页

作者： Fei-Yue WANG Jun Jason ZHANG Xiao WANG The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Electrical Engineering Wuhan University Wuhan 430072 China Qingdao Academy of Intelligent Industries Qingdao 266109 China

Dear editor, Fundamentally, there are only two main approaches so far in artificial intelligence （AI）： reasoning-oriented formal logic approach and function-oriented computational intelligence approach, so called Neats vs. Scuruffies, which is a reflection of the historical fight between two schools of thought for formalism and empiricism respectively in the field of AI that is continuing even today.

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Dynamically Optimized Sensor Deployment Based on Game Theory

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Journal of systems Science and complexity 2018年第1期31卷 276-286页

作者： Zhang Fan Zheng Zhi Jiao Lei School of Automation Beijing Institute of Technology Beijing 100081 Decision of Complex Systems State Key Laboratory of Intelligent Control Beijing 100081

Sensor network deployment is the key for sensors to play an important performance. Based on game theory, first, the authors propose a multi-type sensor target allocation method for the autonomous deployment of sensors, considering exploration cost, target detection value, exploration ability and other factors. Then, aiming at the unfavorable environment, e.g., obstacles and enemy interference, the authors design a method to maintain the connectivity of sensor network, under the conditions of effective detection of the targets. Simulation result shows that the proposed deployment strategy can achieve the dynamic optimization deployment under complex conditions.

关键词： Connectivity dynamic deployment game theory target allocation

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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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Comparative Study of Four Speed controllers of Brushless DC Motors for Industrial Applications

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IFAC-PapersOnLine 2020年第5期53卷 59-64页

作者： Tariku Sinshaw Tamir Gang Xiong Zhen Shen Xiaoyan Gong Sheng Liu Ehtisham Lodhi Li Wan Xisong Dong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan 523808 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Ten Dimensions (Guangdong) Technology Co. Ltd. Foshan 528225 China

Direct current (DC) motors are one of the most important kind of motors and are widely used in robotic and industrial applications. Recently, there have been significant efforts to develop direct current (DC) motors in an attempt to control speed of motors. However, conventional controlling approaches perform undesirably in terms of stability and quick response. Therefore, this paper presents a hybrid intelligent controller configuration for optimized speed control of brushless direct current (BLDC) motors in a factory supervisory control data acquisition (SCADA) system. We compare this hybrid intelligent controller with a conventional PID controller, fuzzy logic controller (FLC), and artificial neural network model reference controller (ANNMRC) in MATLAB , and the results show that the hybrid (neuro-fuzzy) controller performs superior in terms of stability, speed trajectory tracking capability, fast response, and simplicity for implementation.

关键词： BLDC motor fuzzy logic controller (FLC) artificial neural network model reference controller (ANNMRC) hybrid (neuro-fuzzy) control

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Multi-robot distributed cooperative monitoring of mobile targets 3rd

Multi-robot distributed cooperative monitoring of mobile tar...

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3rd International Conference on Intelligence Science, ICIS 2018

作者： Jiang, J.Q. Xin, B. Dou, L.H. Ding, Y.L. School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9783030013127

Cooperative monitoring targets of mobile robots is of great importance in military, civil, and medical applications. In order to achieve multi-robot coordinated monitoring, this paper proposes a new distributed path planning algorithm. In this algorithm, a method of dynamic sequential decision making is used firstly to determine which robots should be moved to the neighbourhoods of the target path. A distributed cooperative path planning algorithm is then proposed in which the robots can only utilize their local information to make the joint monitoring areas of robots cover the target path. To reduce the chance of detection and ensure enough time to implement monitoring handover, our distributed algorithm can guarantee enough overlapped monitoring areas between adjacent robots. The effectiveness of the algorithm was verified by simulation experiments and a comparison experiment in typical scenarios. © IFIP International Federation for Information Processing 2018.

关键词： Monitoring

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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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Distributed Cooperative Attitude Tracking for Multiple Flexible Spacecraft under a Directed Graph

Distributed Cooperative Attitude Tracking for Multiple Flexi...

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第三十八届中国控制会议

作者： Lei Wang Zhuoyue Song Housheng Su Yiqun Zou Xiafei Tang State Key Laboratory of Intelligent Control and Decision of Complex Systems and School of Automation Beijing Institute of Technology School of Automation Huazhong University of Science and Technology School of Automation Central South University School of Electrical and Information Engineering Changsha University of Science and Technology

This paper is concerned with the problem of cooperative attitude tracking and vibration reduction for multiple flexible spacecraft without modal variable measurement under a directed communication topology. Firstly, a distributed attitude synchronization control law for MRPs representation is proposed in the absence of external disturbances, where the modal information is obtained using a modal variable estimator. To deal with a more practical case that the spacecraft are affected by external disturbances, the control law is further modified by integrating a disturbance observer and a feedforward compensating scheme,in which the lumped disturbances including the external disturbances and errors of the estimated modal information are observed and compensated in finite time. Under this enhanced control scheme, the attitude tracking errors and vibration variables will converge to zero even in the presence of external disturbances, and the controller is continuous and chattering-free. Besides,compared with the existing results, the communication graph in this paper is assumed to be directed and contain a spanning tree with the virtual leader as the root node. A numerical example is illustrated to demonstrate the effectiveness of the proposed results.

关键词： Attitude tracking Multiple flexible spacecraft Directed graph External disturbance

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Market-based task assignment for multi-point dynamic aggregation tasks 48

Market-based task assignment for multi-point dynamic aggrega...

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48th International Conference on Computers and Industrial Engineering, CIE 2018

作者： Du, Xin Zhang, Jia Xin, Bin Ding, Yulong Peng, Zhihong Dou, Lihua School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

Multi-agent task assignment problem exists in collaborative target tracking, collaborative rescue, regional search, etc. Most researchers regarding multi-agent task assignment only consider static tasks. However, in complex scenarios, the states of tasks (e.g., Monitoring of hazardous environments) are time-varying. This type of the task can be modeled as a multi-point dynamic aggregation (MPDA) process. This paper proposes a distributed task assignment method for the MPDA task. This method firstly adopts an auction algorithm to obtain a pre-allocation planning that can ensure every task can be completed. Then, the agents who complete the pre-allocation planning will evaluate the uncompleted tasks and assist other agents to complete these tasks, so that tasks can be completed as soon as possible. Compared with the optimal solution, simulations show that the task allocation method can obtain satisfied allocation solutions with a lower computational cost. The gap from the optimal solution is only 8% on average. Especially, for problems including more than 100 tasks, the proposed method can obtain planning solutions within one second, and the makespan of the MPDA task is more than 50% better than the best one of the 1000 random sample solutions. © 2018, Curran Associates Inc. All rights reserved.

关键词： Optimal systems

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Adaptive robust control for four-motor driving servo system with uncertain nonlinearities

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control Theory and Technology 2017年第1期15卷 45-57页

作者： Wei ZHAO Xuemei REN Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing 100081 China

A novel adaptive robust control （ARC） is presented for the four-motor driving servo systems with the uncertain nonlinearities and actuation failures, such that the load tracking control is achieved with the proximate optimal-time. By applying the proposed scheme, several control objectives are achieved. First, the nonlinear synchronization algorithm is presented to maintain the velocity synchronization of each motor, which provides fast convergence without chatting. Moreover, the time-varying bias torque is applied to eliminate the effect of backlash and reduce the waste of energy. Then, the ARC is designed to achieve the proximate optimal-time output tracking with the transient performance in L2 norm, where the friction and actuation failures are addressed by the adaptive scheme based on the norm estimation of unknown parameter vector. Finally, the extensive simulated and experimental results validate the effectiveness of the proposed method.

关键词： Four-motor driving servo systems adaptive robust control velocity synchronization control friction backlash,actuation failures

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