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第33届中国控制与决策会议

作者： Ya Zeng Yan Zhao Tieyan Zhang Yuri Wang Qianzhi Shao Yuanzhuo Du School of Electric Power Shenyang Institute of Engineering School of Renewable Energy Shenyang Institute of Engineering Key Laboratory of Regional Multi-energy System Integration and Control Liaoning Province School of Automation and Electrical Engineering Shenyang Ligong University Industrial Branch State Grid Liaoning Electric Power Supply Co.Ltd. Fengman Training Center State Grid Xinyuan Company

The continuous development of science and technology has led to great changes in the demand for loads in the power system,placing higher demands on the grid for frequency *** disadvantages of traditional FM generators such as slow response speed and dead zone in frequency modulation are becoming more and more ***,the energy storage system has timely tracking,reversible charging and discharging,and rapid *** auxiliary grid frequency modulation through the energy storage system helps to maintain the active power balance of the power system,improve and increase *** frequency adjustment capability of the system provides reliable support for the safe and efficient operation of the power *** paper first analyses the mechanism of secondary frequency regulation of energy storage assisted power grid and establishes an equivalent model of secondary frequency regulation of thermal power units assisted by energy storage *** addition,an energy storage system-assisted grid secondary frequency regulation control strategy based on double-layer fuzzy control is designed to address the shortcomings of the existing energy storage assisted grid frequency regulation control ***,the simulation analysis of the frequency regulation effect is carried out to verify the rationality and effectiveness of the proposed control strategy.

关键词： Fuzzy control Energy Storage System Secondary Frequency Modulation

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Obstacle Avoidance of UAV Based on Neural Networks and Interfered Fluid Dynamical System

Obstacle Avoidance of UAV Based on Neural Networks and Inter...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Yanxiang Wang Honglun Wang Jiayun Wen Yuebin Lun Jianfa Wu School of Automation Science and Electrical Engineering & Science and Technology on Aircraft Control Laboratory Beihang University Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

Obstacle avoidance is the prerequisite guarantee for the unmanned aerial vehicle (UAV) to fly safely in the three-dimensional dynamic complex environment. In this paper, a three-dimensional real-time obstacle avoidance method is proposed by combining neural network and the Interfered Fluid Dynamical System (IFDS) for the first time. First, in order to solve the problem of insufficient samples, sample data are generated based on the sparrow search algorithm (SSA) and receding horizon control (RHC). Second, training neural network offline, the relative position between UAV, destination and obstacle from sample data as input of neural network, and the IFDS parameters are used as the feature extraction of the output terminal of the neural network. Third, the trained neural network is used to adjust the coefficients of the IFDS according to environment in real time. Finally, the simulations demonstrate effectiveness of the proposed method.

关键词： electrical engineering automation Aerospace control Unmanned aerial vehicles Neural networks Heuristic algorithms Fluid dynamics

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The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

SSRN

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SSRN 2022年

作者： Li, Juanjuan Li, Junqing Wang, Xiao Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Mathematics Renmin University of China Beijing100872 China Macao Institute of Systems Engineering Macau University of Science and Technology 999078 China

Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recording and trading are conducted separately in the Data Blockchain (DChain) and the Value Blockchain (VChain). Based on these blockchains, two-layer data trading markets are established to manage the initial data trading in the primary market and the data resale in the secondary market. Pricing mechanisms are proposed to protect the formulated data trading markets against strategic trading behaviors and balance the payoffs of both suppliers and users. In the regular data trading on VChain-S2D, two auction models are used to deal with users' strategic bidding in terms of the demand scale, where the incentive-compatible Vickrey-Clarke-Groves (VCG) model is dedicated to the low-demand trading scenario, while the nearly incentive-compatible Monopolistic Price (MP) model is for the high-demand trading scenario. In the temporary data trading on VChain-D2S, a reverse auction mechanism named Two-Stage Obscure Selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Moreover, experiments are carried out to validate our theoretical analysis. Our work is expected to enhance the data security and improve the trading efficiency. © 2022, The Authors. All rights reserved.

关键词： Blockchain

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Reinforcement Learning Based Approach for Flip Attack Detection

Reinforcement Learning Based Approach for Flip Attack Detect...

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IEEE Conference on Decision and control

作者： Hanxiao Liu Yuchao Li Jonas Mårtensson Lihua Xie Karl Henrik Johansson School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Division of Decision and Control Systems the School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

This paper addresses the detection problem of flip attacks to sensor network systems where the attacker flips the distribution of manipulated sensor measurements of a binary state. The detector decides to continue taking observations or to stop based on the sensor measurements, and the goal is to have the flip attack recognized as fast as possible while trying to avoid terminating the measurements when no attack is present. The detection problem can be modeled as a partially observable Markov decision process (POMDP) by assuming an attack probability, with the dynamics of the hidden states of the POMDP characterized by a stochastic shortest path (SSP) problem. The optimal policy of the SSP solely depends on the transition costs and is independent of the assumed attack possibility. By using a fixed-length window and suitable feature function of the measurements, a Markov decision process (MDP) is used to approximate the behavior of the POMDP. The optimal solution of the approximated MDP can then be solved by any standard reinforcement learning methods. Numerical evaluations demonstrates the effectiveness of the method.

关键词： Detectors Markov processes Reinforcement learning Cost function Aerospace electronics Velocity measurement Time measurement

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Classification on Point-cloud of Shoe-last Curvature using Weight-updated Boosting based Ensemble Learning

Classification on Point-cloud of Shoe-last Curvature using W...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Zairan Li Dan Wang Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University PR China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Optimizing the curvature shape of shoe last is an important research content and a promising direction in current shoe-last design. The emergence of plantar pressure imaging method provided a new technical mean for shoe-last design. The point-cloud dataset was acquired from the plantar pressure experiments which may be used for generating comfort shoe-last curvature, and the key zones on last curvature may be classified and has potential benefit to the designers to produce comfort shoe. In this paper ensemble learning technologies are applied for classifying those points through the proposed weights-updated algorithms. The proposed weights-updated algorithm with boosting operation performed high effectiveness by comparing to k-NN, random forest (RF) and Bayers (NB) in classification accuracy. The proposed boosting based ensemble learning method will be potential applied in interactive design of industry and be beneficial to produce comfort shoes.

关键词： Training Machine learning algorithms Footwear Boosting Classification algorithms Statistics Unsupervised learning

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Driver Behavior Modeling via Inverse Reinforcement Learning Based on Particle Swarm Optimization

Driver Behavior Modeling via Inverse Reinforcement Learning ...

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Chinese automation Congress (CAC)

作者： Zeng-Jie Liu Huai-Ning Wu The Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In this paper, an inverse reinforcement learning method based on particle swarm optimization (PSO) is proposed to model driver's steering behavior. Initially, the vehicle dynamics is represented by a Takagi-Sugeno (T-S) fuzzy model which provides a method of approximating Q-function. Then the driver behavior model is described as an optimal control policy with decision-making model which illustrates the driving style. Subsequently, the Q-function is approximated by a quadratic polynomial-in-memberships form and the PSO algorithm is used to obtain the decision-making model from the driving data. And the corresponding optimal control policy is obtained by using the Q-learning policy iteration method. Finally, a numerical simulation is carried to show the effectiveness of the proposed method.

关键词： Vehicles Cost function Vehicle dynamics Optimal control Mathematical model TV Safety

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False Data Injection Cyberattacks to Human Brain Implants’ Power Source

False Data Injection Cyberattacks to Human Brain Implants’ ...

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electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Samaneh Pazouki Abdullah Aydeger S. Mahdi Kazemi-Razi School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale Illinois USA School of Computing Southern Illinois University Carbondale Illinois USA Electrical Power and Control Systems esearch Group Northumbria University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781665442329

Brain-computer Interfaces (BCIs) are bidirectional communication implantable systems between the brain and in/external computing devices such as laptops and smartphones. BCIs are Cyber-Physical Systems (CPSs) capable of computation, communication, and decision-making process. This advanced technology is used for the treatment of diseases such as Parkinson, Alzheimer, and Depression. BCIs also have futuristic applications such as brains’ interconnection network and connection of the brain to the Internet. Despite the outstanding broad applications of BCIs, researchers have not considered the vulnerabilities of these systems against cyberattacks. In this paper, we present a threat model where the attacker manipulates the BCIs’ power source by injecting false data, which may lead to battery depletion or load shedding. Specifically, three different False Data Injection (FDI) cyberattacks are designed and developed: depleting brain implants’ battery, load shedding, and combination of these attacks simultaneously. Moreover, based on the BCI structure vulnerabilities and investigated FDI cyberattacks, a cybersecurity layer is proposed to reduce the cyberattacks’ impacts. We demonstrate that FDI cyberattacks can cause a BCI battery malfunction and create significant consequences for the human that the BCI is implanted.

关键词： Lead acid batteries Portable computers Multiprocessor interconnection Load shedding Depression Internet computer crime

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Fault Diagnosis of Wind Turbine Gearbox Based on SSDAE-ELM

Fault Diagnosis of Wind Turbine Gearbox Based on SSDAE-ELM

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第33届中国控制与决策会议

作者： Jianhua Zhang Haibo Dong Rui Shan Guolian Hou Congzhi Huang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University School of Control and Computer Engineering North China Electric Power University

In recent years,the installed capacity of wind turbine is increasing year by year,and the influence of wind turbine faults on wind farm operation is also *** this paper,a novel fault diagnosis method is proposed to diagnose the fault of wind turbine *** method is based on stacked sparse denoising auto-encoders(SSDAE)neural network which combines stacked sparse auto-encoders(SSAE) neural network and stacked denoising auto-encoders(SDAE) neural network to extract features from high-dimensional signal data *** addition,extreme learning machine(ELM) classifier is used to finish the fault diagnosis according to the features extracted from high-dimensional signal data by the ***,the method is tested based on the simulation experiment,the simulation results demonstrate that the accuracy of this fault diagnosis method is higher than that of other methods when the same parameters are used.

关键词： Wind Turbine Gearbox Deep Learning Stacked Sparse Denoising Auto-encoders Fault Diagnosis

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Gas Source Localization using Improved Multi-Agent Reinforcement Learning

Gas Source Localization using Improved Multi-Agent Reinforce...

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Chinese automation Congress (CAC)

作者： Zhi-Pu Wang Huai-Ning Wu The Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In this paper, an improved multi-agent reinforcement learning (MARL) algorithm is proposed to solve the localization problem of gas source with disturbance sources. Firstly, based on Gaussian dispersion model, multi-point sources are estimated at the initial position of sensor network. Secondly, the multi-agent system is pre-trained in the synthetic environment derived from dispersion model and the estimated source term. Then, the improved MARL algorithm is used to guide the mobile sensors to localize the actual target source. Finally, numerical simulations are given to verify the efficiency of this method.

关键词： Sensors Dispersion Estimation Cost function Location awareness Genetic algorithms Reinforcement learning

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A Zeroth-Order Momentum Method for Risk-Averse Online Convex Games

arXiv

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arXiv 2022年

作者： Wang, Zifan Shen, Yi Bell, Zachary I. Nivison, Scott Zavlanos, Michael M. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Enginnering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States The Air Force Research Laboratory Eglin AFB FL United States

We consider risk-averse learning in repeated unknown games where the goal of the agents is to minimize their individual risk of incurring significantly high cost. Specifically, the agents use the conditional value at risk (CVaR) as a risk measure and rely on bandit feedback in the form of the cost values of the selected actions at every episode to estimate their CVaR values and update their actions. A major challenge in using bandit feedback to estimate CVaR is that the agents can only access their own cost values, which, however, depend on the actions of all agents. To address this challenge, we propose a new risk-averse learning algorithm with momentum that utilizes the full historical information on the cost values. We show that this algorithm achieves sub-linear regret and matches the best known algorithms in the literature. We provide numerical experiments for a Cournot game that show that our method outperforms existing methods. © 2022, CC BY.

关键词： Learning algorithms

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