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Equivalent input disturbance-based load frequency control for smart grid with air conditioning loads

Science China(Information Sciences) 2022年第2期65卷 106-123页

作者： Li JIN Yong HE Chuan-Ke ZHANG Xing-Chen SHANGGUAN Lin JIANG Min WU School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical Engineering and Electronics University of Liverpool Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Integrating intermittent wind power into power systems results in low or zero inertia, threatening their frequency stability. To accommodate intermittent generations, the demand response(DR) is introduced, and air conditioning loads(ACs) account for an increasing proportion of all loads. The replacement of traditional generators with wind turbines and the ACs user uncertainties produce parameter uncertainties. This paper aims to construct an equivalent input disturbance(EID)-based load frequency control(LFC) strategy for the power system by considering wind power and ACs. First, an open-loop model is constructed for the LFC scheme with parameter uncertainties. Then, the parameter uncertainties and external disturbance are lumped into a fictitious disturbance, which is estimated using an EID *** incorporating the estimation of disturbance into the control input, the disturbance-rejection performance is achieved. Next, the Lyapunov theory is used to derive the two linear-matrix-inequality-based asymptotic stability criteria. A design algorithm is developed for the EID-based LFC scheme by exploiting an overall performance evaluation index. Finally, simulation results for the single-area and two-area LFC schemes show that, compared with the existing approaches, the method presented realizes the better disturbance rejection and higher robustness against parameter uncertainties, wind power fluctuation, and tie-line power ***, its robustness to time delays is verified.

关键词： equivalent input disturbance load frequency control parameter uncertainties wind power air conditioning loads

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Quick Routing Response to Link Failure in Low-Earth Orbit Satellite Networks 8

Quick Routing Response to Link Failure in Low-Earth Orbit Sa...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Lu, Zhiping Zhi, Ruxin Ma, Weiguo School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing China Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781665450515

Low earth orbit satellite communication systems are attractive for provisioning global, high-speed and low latency Internet access services. In order to achieve global continuous multiple coverage, the number of satellites is tremendous. But Low earth orbit satellite networks are prone to instability due to unpredictable link failures and frequent topology changes. Therefore, in this paper, a novel routing schemes with quick response to link failures is first proposed. Based on Dijkstra algorithm, a scalable routing algorithm is proposed to minimize the end-to-end transmission delay. It takes advantage of the predictable constellation trajectory, and changes dynamically according to the changes of network topology. It provides the shortest routing path and an alternative path simultaneously. When a link failure is detected by one satellite, notification packets will be sent to its neighbors for adjustment to the alternative routing path. Notification packets also will be sent to the head node for the recalculation of routing table. Finally, extensive simulations have been conducted, and the results show that the proposed scheme is able to produce desired performance. © 2022 IEEE.

关键词： Routing algorithms

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A Swing-Up and Stable control Strategy for Pendubot Based on Nonlinear Model Predictive Method

A Swing-Up and Stable Control Strategy for Pendubot Based on...

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2023 China Automation Congress, CAC 2023

作者： Wang, Lejun Liu, Yiqin Zhang, Ruihuan Huang, Zixin Cai, Zhen School of Automation School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing400065 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China School of Information Engineering Wuhan City Polytechnic Wuhan430064 China

ISBN: (纸本)9798350303759

The control objective of Pendubot is to stabilise the system in a vertically upward position by swinging up from a vertically downward position. Facing various physical constraints in practical applications, this paper proposes a swing-up stabil-isation control strategy based on a nonlinear model prediction method to accomplish the above control objective. On the basis of the system dynamics model, the forward Euler method is used to construct a nonlinear prediction model of the system. According to the control objectives and physical constraints of the system, the performance index function is designed, and then the control inputs of each sampling cycle are obtained through rolling optimisation and feedback correction, so that the system state gradually converges to the target trajectory, thus achieving the motion control objective. Based on the simulation results, it can be seen that the strategy proposed in this paper is efficient and robust under external disturbances. © 2023 IEEE.

关键词： Model predictive control

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Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks ⁎

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IFAC-PapersOnLine 2024年第10期58卷 176-181页

作者： Yi Li Shuangshuang Han Yongqiang Bai Fei-Yue Wang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Key Laboratory of Ocean Observation Technology MNR Tianjin China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Beijing Huairou Academy of Parallel Sensing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

关键词： VANETs Social relation strength Image data transmission MMSE detection Intelligent transportation Social networking

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A Multi-Channel Adaptive Cancellation Algorithm for Active Suppression of Motion-Induced Noise in Magnetic Antennas

A Multi-Channel Adaptive Cancellation Algorithm for Active S...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Mingyi Hu Junlei Song Yi Yan Ying Liu Xiaoxue Li Kaifeng Dong Fang Jin Wenqin Mo Yajuan Hui School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

During super low frequency (SLF) magnetic induction communication, the sensitive antenna carried by autonomous underwater vehicles (AUVs) is highly susceptible to vibration, resulting in a large amount of noise in communication signals, which seriously affects communication performance. The type of motion-induced noise typically exhibits randomness and nonlinearity. Based on the strong correlation between the motion-induced noise and the antenna's vibration signal, we propose a multi-channel normalized least mean square (MCNLMS) algorithm. The algorithm utilizes the six degrees of freedom vibration acceleration information from both ends of the magnetic antenna as the reference signal input for the canceller, providing more comprehensive information related to motion-induced noise than the single-channel adaptive noise cancellation algorithm. Experimental results indicate that the MCNLMS algorithm demonstrates effective suppression of the motion-induced noise, achieving an average reduction of approximately 7 dB within the 160–170 Hz communication band. Additionally, it exhibits improved stability and faster convergence.

关键词： Vibrations Industrial electronics Correlation Adaptive arrays Magnetic noise Approximation algorithms Noise cancellation Antennas Magnetic susceptibility Convergence

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Data-Driven Distributed MPC for Load Frequency control of Networked Nonlinear Power systems

Data-Driven Distributed MPC for Load Frequency Control of Ne...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Yubin Jia Jun Zhou Panxiao Yong Jun Guo School of Automation Southeast University Nanjing Jiangsu China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing P. R. China

ISBN: (纸本)9781665476881

In this paper we proposed a data-driven distributed model predictive control (MPC) for the load frequency control of the multi-area interconnection nonlinear power system. The data-driven Koopman operator method is used to deal with the nonlinear wind power generation system in each area. The distributed Koopman operator algorithm is proposed to deal with the huge amount of data in the large-scale distributed system. Based on the distributed linear model obtained by the distributed Koopman operator algorithm, the distributed MPC is applied to realize the load frequency control (LFC) of the multi-area power system. The cooperation-based cost function is optimized by the MPC controller in each area, and the global optimal solution can be achieved by the proposed method. The effectiveness and practicability of the proposed algorithm are demonstrated by the simulation.

关键词： Distributed databases Wind power generation Prediction algorithms Cost function Power systems Nonlinear systems Frequency control

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A Predictive Tracking control Algorithm for Initial Angle of Brushless DC Motor Based on Reinforcement Learning

A Predictive Tracking Control Algorithm for Initial Angle of...

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Asian control Conference

作者： Yu Zhu Haodong Chen Xihong Fei Kang Wang Jing Wang School of Electrical and Information Engineering Anhui University of Technology Ma'anshan China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’anshan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The initial angle detection and tracking control of brushless direct current motor (BLDC) is widely used in military, aviation, industrial, automotive, civil control systems, and household appliances. The motor is a nonlinear system, which is easily affected by external factors, so it is difficult to maintain the stability of the system with fixed control parameters. Therefore, this paper proposes a control method for initial angle tracking of BLDC based on reinforcement learning (RL). In order to determine the initial angle of the surface-mounted direct current (DC) motor, in the traditional Field-Oriented control (FOC) algorithm, the pulsating high-frequency injection algorithm is introduced and combined with the RL theory. According to the given PI parameters, the appropriate rewards are obtained through the RL controller. Then the proportional integral (PI) parameters are dynamically adjusted to make the estimated angle closer to the true angle value. This method avoids the error of angle estimation caused by fixed PI parameters in different situations, and obtains the best results by dynamically adjusting the parameters. Finally, the simulation experiment is carried out to prove the feasibility of the proposed scheme.

关键词： Home appliances Brushless DC motors Heuristic algorithms Estimation Reinforcement learning Prediction algorithms control systems

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Predicting microbe-disease associations via attention guided graph convolutional networks 4

Predicting microbe-disease associations via attention guided...

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4th International Conference on Biomedicine and Bioinformatics engineering, ICBBE 2024

作者： Gong, Shiyun Zhu, Yuan School of Automation China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Lumo Road Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Lumo Road Hongshan District Hubei Wuhan430074 China School of Mathematics and Physics China University of Geosciences Lumo Road Wuhan430074 China

ISBN: (纸本)9781510682443

It is important to predict microbe-disease associations, as it helps to understand the cause of diseases episodes, the prevention of diseases, among other roles. Traditionally, the study of microbe-disease association is mainly based on laboratory purified culture for research and observation, which is highly scientific but has some limitations, such as some microbes needing to be cultured in a laboratory environment and costs heavily. This study proposes an attention guided graph convolutional network d approach (AGGCN) to predict possible potential associations between microbes and diseases. In the first place, AGGCN constructs four different similarity networks of microbes and diseases, which are combined with association networks to form a microbe-disease heterogeneous network. Subsequently, it is used as an input to the graph convolutional neural network to obtain each node embedding of this heterogeneous network. In this study, a three-layer network is used, so three different types of node embeddings can be obtained, and these three different types of node embedding representations are integrated via the multichannel graph attention mechanism to obtain the final node embeddings. Finally, the node embeddings obtained above are used for association prediction of microbes-diseases in heterogeneous networks. Experimentally verified, the AUC of AGGCN is 0.8972, and the AUPR is 0.5387. Comparative experimental results indicated that AGGCN shows better than the other five mainstream methods. It provides a new insight to explore network potential feature representation and predict associations between microbe and disease. © 2024 SPIE.

关键词： Graph neural networks

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Towards Intelligent and Trustworthy Crowdsourcing systems with Cyber-physical Social Intelligence via Retrieval-augmented Generation

Towards Intelligent and Trustworthy Crowdsourcing Systems wi...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jing Yang Xingxia Wang Qinghua Ni Yuhang Liu Jing Wang Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

In recent years, crowdsourcing systems that tackle complex tasks through the collective efforts of many individuals have garnered substantial attention. However, the existing crowd-sourcing systems face some challenges such as overreliance on human experience and effort, information overload, and risks of privacy breaches. To overcome these challenges, this paper proposes an intelligent and trustworthy crowdsourcing frame-work based on blockchain and retrieval-augmented generation. The framework centers around foundational models to integrate the three distinct and sequentially interdependent phases: task decomposition, task allocation or recommendation, and task execution, enabling the automatic provision of a personalized, comfortable, and efficient user experience. Blockchain and smart contracts are utilized to protect user privacy and interests without leaning on any third trusted authority. Additionally, the operational process of the framework is explained and validated by taking printed circuit board design and manufacturing as a case study and ChatGPT as its generator.

关键词： Crowdsourcing Retrieval augmented generation Smart contracts Privacy breach User experience Stability analysis Blockchains Social intelligence Resource management Reliability

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Reinforcement Learning based Data-driven Optimal control Strategy for systems with Disturbance

Reinforcement Learning based Data-driven Optimal Control Str...

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Data Driven control and Learning systems (DDCLS)

作者： Zhong-Xin Fan Shihua Li Rongjie Liu School of Automation Southeast University Nanjing P. R. China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing P. R. China Department of Statistics Florida State University Tallahassee FL USA

This paper proposes a partially model-free optimal control strategy for a class of continuous-time systems in a data-driven way. Although a series of optimal control have achieving superior performance, the following challenges still exist: (i) The controller designed based on the nominal system is difficult to cope with sudden disturbances. (ii) Feedback control is highly dependent on system dynamics and generally requires full state information. A novel composite control method combining output feedback reinforcement learning and input-output disturbance observer for these two challenges is concluded in this paper. Firstly, an output feedback policy iteration (PI) algorithm is given to acquire the feedback gain iteratively. Simultaneously, the observer continuously provides estimates of the disturbance. System dynamic information and states information are not needed to be known in advance in our approach, thus offering a higher degree of robustness and practical implementation prospects. Finally, an example is given to show the effectiveness of the proposed controller.

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