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Electrical Engineering and Green Energy (CEEGE), International Conference on

作者： Hankun Liu Lizheng Chen Xiaohan Shi School of Information and Electrical Engineering Shandong Jianzhu University China Key Laboratore of Power System Intelligent Dispatch and Control Shandong University China

Power load forecasting has an important impact on the safe dispatch of the electric grid and the operation of the national economy. A method based on the back propagation neural network was proposed and considered the key meteorological factor to improve the accuracy of electricity load forecasting. Firstly, this paper analyzed the influence of different meteorological factors on electricity load. Secondly, key meteorological factors and historical electricity loads were used as input vectors to the temporal convolutional network for electricity load forecasting. The results showed that considering meteorological factor could effectively improve the accuracy of load forecasting.

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Review of Research on New Energy Ramp Events

Review of Research on New Energy Ramp Events

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IEEE/IAS Industrial and Commercial Power system Asia (I&CPS Asia)

作者： Chao Wang Hong Lin Ming Yang Xiaoling Fu School of Electrical Engineering Xinjiang University Urumqi China School of Energy and Control Engineering Changji University Changji China Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China

With the massive access of new energy represented by wind power and photovoltaic to the power system, the new energy ramp events on the secure and stable operation of the power grid have brought huge challenges. The ramp events are reviewed from four aspects: the causes, definitions, prediction methods and evaluation indicators of ramp events. Firstly, the causes of wind power ramp events(WPRE) and solar power ramp events(SPRE) are analyzed, and the common definitions, advantages and disadvantages of ramp events are compared. Then, according to the form of prediction results, the methods of deterministic ramping prediction and uncertain ramp prediction are summarized. On this basis, the commonly used ramp evaluation indexes are summarized. Finally, the prediction of new energy ramp events is prospected.

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A Deep Reinforcement Learning Based control Strategy for Combined Wind Energy Storage system

A Deep Reinforcement Learning Based Control Strategy for Com...

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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Xiang, Guangwei Yang, Ming Huang, Shiying Yu, Guangyuan Yin, Aihui Liu, Xiao State Grid Corporation of China Heilongjiang Electric Power Company Harbin China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China

ISBN: (纸本)9781665414395

For renewable energy producers, the uncertainties of power generation and time-varying electricity prices may lead to economic losses. This paper proposes a deep reinforcement learning (DRL) based control model combined energy storage system (ESS) to maximize the income of the wind power producer. It is able to adapt the uncertainties by itself that can avoid the errors caused by modeling the uncertainties. It can also learn and improve the strategy based on feedbacks got by the mechanism of interacting with the environment. Moreover, Rainbow, an improved DRL algorithm, is introduced to formulate the optimal model. Finally, the effectiveness of the proposed model is verified based on the wind farm located in Jiangsu, China. © 2021 IEEE.

关键词： Wind power

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Design of the Slave Hand Structure and Interference Detection for the Master-Slave Craniotomy Surgical Robot 2

Design of the Slave Hand Structure and Interference Detectio...

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2nd International Conference on intelligent Equipment and Special Robots, ICIESR 2023

作者： Zhao, Lei Zhang, Guobin Li, Zehua Liu, Zhenzhong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (纸本)9781643685144

Existing neurosurgical robots are prone to interfering with each other in a narrow operating space due to the overlapping of several arms, which poses a great safety risk during surgery. In this paper, a 7-DOF dual-arm robot is designed to cooperate with the surgeon to complete craniotomy by manipulating the master hand mechanism. Aiming at the pain point that two real-time dynamic slave hands are prone to interference, this paper establishes a simplified model of the robot envelope, and transforms the interference detection problem between the robots into the interference checking of geometric primitives. If the interference between the two arms is detected by the algorithm, the surgical path of the current robotic arm is immediately terminated to ensure the safety of the surgery. Finally, the algorithm is simulated and verified with the robot designed in this paper. The results show that the algorithm is feasible and has high computational efficiency. © 2024 The Authors.

关键词： Structural design

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Simulation Research on Generalized Proportional Integral Observer Based Active Disturbance Rejection control for Coarse Pointing Assembly 8

Simulation Research on Generalized Proportional Integral Obs...

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8th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2024

作者： Ma, Jijun Guo, Yulong Wang, YuNing Shi, Yongkang Beijing Research Institute of Telemetry Beijing100074 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China Research Institute of Telemetry Beijing China

ISBN: (纸本)9798331506230

The coarse pointing assembly (CPA), as the outer loop of the laser terminal, its tracking stability is the basis for ensuring laser communication. This paper presents the model of the CPA. Aiming at the disturbance factors in the low-speed operation process of the CPA, an active disturbance rejection control (ADRC) method for the position loop of the CPA is designed based on the generalized proportional integral observer (GPIO) to achieve disturbance compensation control. Finally, simulations are carried out on the system model. The simulation results show that compared with the PID control strategy adopted, the proposed ADRC strategy based on GPIO of the CPA can effectively improve the tracking accuracy. © 2024 IEEE.

关键词： Disturbance rejection

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Iteration framework for solving mixed lubrication computation problems

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Frontiers of Mechanical Engineering 2021年第3期16卷 635-648页

作者： Shi CHEN Nian YIN Zhinan ZHANG Xiaojiang CAI State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Aerospace Control Technology Institute Shanghai 201109China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai 201109China

The general discrete scheme of time-varying Reynolds equation loses the information of the previous step,which makes it unreasonable.A discretization formula of the Reynolds equation,which is based on the Crank-Nicolson method,is proposed considering the physical message of the previous ***-Seidel relaxation and distribution relaxation are adopted for the linear operators of pressure during the numerical solution *** addition to the convergent criteria of pressure distribution and load,an estimation framework is developed to investigate the relative error of the most important term in the Reynolds *** surface with frill contacts and mixed elastohydrodynamic lubrication is tested for *** asperity contact and sinusoidal wavy surface are examined by the proposed discrete *** show the precipitous decline in the boundary of the contact *** relative error suggests that the pressure distribution is reliable and reflects the accuracy and effectiveness of the developed method.

关键词： mixed lubrication discretization formula relative error Reynolds equation asperity

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Graph-based Selection-Activation Reinforcement Learning for Heterogenous Multi-agent Collaboration 43

Graph-based Selection-Activation Reinforcement Learning for ...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Hao-Xiang Zhang, Xi-Wen Shen, Jun-Nan Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401135 China

ISBN: (纸本)9789887581581

In practical applications of multi-agent systems, agents are often heterogeneous, and each type of them typically has different task objectives. For heterogeneous multi-agent reinforcement learning (HMARL), the diversity of agent types and the unbalanced agent number of each type can lead to the curse of dimensionality and non-stationary. Moreover, the increase in the number of heterogeneous agents may result in slow convergence during training. This paper proposes a graph-based selection-activation reinforcement learning (GSARL) method for training heterogenous multi-agent collaboration strategies. It first constructs agents based on their types, then extracts the global adjacency matrices and the ally adjacency matrices from the agents' observations, and calculates the global feature matrices. Afterwards, GSARL utilizes hierarchical graph convolutional network to sequentially convolve the global information and ally information, obtaining action logits based on agent types. By using the neural topology graph and the selection-activation method, the optimal multi-agent collaboration configuration is obtained through combinatorial optimization. Experiments are conducted in an adversarial combat simulation environment involving collaborative Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). Simulation results show that the proposed method can accelerate convergence while allowing that each type of heterogeneous agents can leverage its unique advantages. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Unmanned aerial vehicles (UAV)

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Research on Rolling Bearing Fault Diagnosis Based on Classification Generative Adversarial Network

Research on Rolling Bearing Fault Diagnosis Based on Classif...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Kang, Ma Hongjun, Wang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science and Technology Cooperation Base for Intelligent Sensing and Control of High-End Equipment Beijing100192 China Beijing Key Laboratory of Measurement and Control of Electromechanical System Beijing100192 China

ISBN: (纸本)9783031494123

In practical engineering, it is usually difficult to label monitoring data, and the fault diagnosis accuracy is not high in strong noise environment. To solve the above problems, this paper uses Cwt-CatGAN method to process signals and diagnose faults. In this method, CWT is first used to convert one-dimensional original vibration signals into two-dimensional time–frequency graphs as the input of classification generation admixture network. Then, the CatGAN model is obtained through the adversarial training process, and fake samples similar to the extraction distribution of CWT are generated. Finally, the input samples are clustered into certain categories. In addition, the performance of the Cwt-CatGAN method is verified by using the classical rotating machine data set, the experiment is compared with the unsupervised fault diagnosis model CatAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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Forming Circle Formations of Multi-agent systems Under Actuator Faults 5th

Forming Circle Formations of Multi-agent Systems Under Actu...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Liu, Jiajun Ru, Xinfeng Xia, Weiguo Wang, Chen China National Aeronautical Radio Electronic Research Institute and National Key Laboratory of Science and Technology on Avionics System Integration Shanghai China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian China National Engineering Research Center of Software Engineering and the State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

ISBN: (纸本)9789811939976

A distributed fault-tolerant formation control law is designed in this paper for multi-agent systems under external disturbances and actuator faults including time-varying loss of effectiveness faults. The initial position of each agent is already on a circle and the control purpose is to form a desired formation on this circle. Based on this local information, a distributed adaptive formation control law is designed by employing the congelation of variables method to deal with time-varying actuator faults. Finally, a simulation example is given to verify the effectiveness of the adaptive fault-tolerant control design method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Multi-Agent Reinforcement Learning with Region Division and Leader-Follower Communication

Multi-Agent Reinforcement Learning with Region Division and ...

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Chinese Automation Congress (CAC)

作者： Chao Wang Xiang Xu Shenzhen Key Laboratory of Control Theory Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The integration of multi-agent reinforcement learning (MARL) into complex systems has paved new ways for collaborative problem-solving. However, traditional approaches to MARL frequently encounter the challenge of achieving ef-ficient communication among agents, essential for coordinated action. This paper introduces a region division and leader-follower(RDLF) communication algrithm with the MARL frame-work. RDLF divides the environment into several regions, each managed by a leader agent that coordinates the actions of fol-lower agents and handling inter-region communication. This hier-archical structure reduces unnecessary communication, enhancing learning efficiency. Experimental results in multi-particle en-vironments demonstrate RDLF's superiority over existing MARL algorithms, especially with increasing agent numbers. RDLF effectively addresses scalability and communication challenges in large-scale multi-agent systems, providing a robust foundation for its application in complex and dynamic environment.

关键词： Automation Scalability Heuristic algorithms Collaboration Reinforcement learning Problem-solving Complex systems Multi-agent systems

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