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检索条件"任意字段=8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019"

共 336 条记录，以下是1-10 订阅

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Proceedings of 2019 ieee 8th data driven control and learning systems conference, ddcls 2019

Proceedings of 2019 IEEE 8th Data Driven Control and Learnin...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

ISBN: (纸本)9781728114545

the proceedings contain 250 papers. the topics discussed include: dual attention message passing model for scene graph generation;feature extraction for controller design by deep auto-encoder neural network and least squares policy iteration;data driven control for a class of nonlinear SISO systems with uniform quantizer using encoding and decoding mechanism;content based image retrieval via sparse representation and feature fusion;active disturbance rejection control of ship course based on deep belief network;remaining useful life prediction of lithium-ion battery based on unscented kalman filter and back propagation neural network;and multiple fault diagnosis for sucker rod pumping systems based on matter element analysis with F-statistics.

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Graph-Based Modeling for Multi-input Temperature Prediction in Power Plant 8th

Graph-Based Modeling for Multi-input Temperature Prediction...

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8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Gang, Yusen Peng, Chen Cheng, Chuanliang Shanghai University Shangda Road Baoshan District Shanghai99 China

ISBN: (纸本)9789819602247

the operational efficiency and safety of thermal power generation units are intricately dependent on the precise control of combustion chamber temperatures. Accurate temperature prediction is indispensable for optimizing boiler combustion efficiency and swiftly identifying abnormal operational conditions. In this research, we propose an innovative end-to-end data-driven model designed to address the inherent complexity of multi-input multi-output temperature prediction. By fusing temperature data with a graph-based framework and harnessing spatiotemporal attention mechanisms, our model, underpinned by Graph Convolutional Network and Transformers, adeptly captures intricate spatial relationships among output variables. Empirical validation using real-world plant data attests to the model’s exceptional predictive performance. the results highlight its potential to significantly enhance temperature prediction accuracy in power plant operations. Notably, the model achieves a root mean squared error of 2.257 and a mean absolute error of 1.803 as the best prediction scores. Overall, our proposed approach presents a promising solution for the accurate prediction of temperature, with potential implications for improving operational efficiency and safety in thermal power generation units. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Intelligent Computing Signal Analysis Signal Processing systems Modelling

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Bias Correction of data-driven Deep Reinforcement learning in Economic Scheduling of Integrated Energy systems 8th

Bias Correction of Data-Driven Deep Reinforcement Learning ...

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8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Gong, Jiakai Yu, Nuo Han, Fen Tang, Bin Wu, Haolong Ge, Yuan Anhui Polytechnic University Anhui Wuhu241000 China

ISBN: (纸本)9789819602247

the integrated energy system (IES) can make multiple energy sources couple and complement each other, which further improves the operational economy. However, the uncertainty of renewable energy resources and the demand response program’s complexity will aggravate the optimal scheduling difficulty. In this paper, a weight twin delayed deep deterministic policy gradient (WTD3) deep reinforcement learning-based approach is proposed for addressing the IES scheduling problem considering the uncertainties of multiple energy and the integrated demand response. the proposed method can reduce the deviation predicted through weight adjustment of the neural network, and improves the decision-making ability of optimal scheduling. the simulation results confirmed that our proposed WD3-based dispatch strategy outperforms the obtained by other DRL methods and traditional approaches. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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An Extended Single-Particle Model Based on Physics-Informed Neural Network for SOC State Estimation of Lithium-Ion Batteries 8th

An Extended Single-Particle Model Based on Physics-Informed ...

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8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Tian, Aina He, Luyao Dong, Kailang Ding, Tao Gao, Yang Jiang, Jiuchun Yang, Xiaoguang Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of Technology Wuhan430068 China School of Electric Power South China University of Technology Guangzhou510640 China Beijing Institute of Technology Shenzhen Automobile Research Institute Shenzhen China

ISBN: (纸本)9789819602315

Because of its great accuracy, the electrochemical model is frequently utilized in the algorithm design process for lithium-ion batteries. Sadly, the electrochemical model requires a lot of time to solve since it is made up of many nonlinear partial differential equations. In order to solve an extended single particle model (ESPM) fast, a neural network based on physical information (PINN) is examined in this paper. the PINN-ESPM structure can not only estimate the state of charge, but also quickly and accurately estimate the lithium-ion concentration and potential under various application currents, which has stronger adaptability and scalability. In the process of neural network learning, different from the traditional neural network that needs to be trained by labeled data, the loss function is designed only based on the physical constraints brought by equations, boundary conditions and initial values, which makes it an unsupervised learning method. Finally, by comparing the PINN-ESPM proposed in this paper with the data obtained by the P2D model under various current conditions and the experiment battery voltage, the maximum relative error is maintained at 4%. the error of SOC based on the model is less than 4%. While under the same computing resources, PINN-ESPM is 500 times faster than the traditional numerical method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Nonlinear equations

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On extended state observer based control for a class of nonaffine systems with unknown saturation 8

On extended state observer based control for a class of nona...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Song, Xiaojing Chen, Sen Xue, Wenchao School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781728114545

this paper studies the control problem for a class of nonaffine uncertain systems with unknown saturation. the system model is built to describe many physical plants satisfying generalized saturation model of control input. the inflection point of the saturation model, which is critical for conventional controller design, is assumed to be unknown in this paper. the extended state observer is constructed to not only online estimate the 'total disturbance' but also identify the inflection point. the simulation results on the typical aircraft flight control module show the effectiveness of the proposed controller. © 2019 ieee.

关键词： controllers

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Consensus of nonlinear multi-agent system under independent topology 8

Consensus of nonlinear multi-agent system under independent ...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Zhang, Dan Li, Junmin School of Mathematics and Statistics XiDian University Xi'an710126 China

ISBN: (纸本)9781728114545

this article solves the consensus problem for second-order multi-agent systems (MASs) in continuous time with nonlinear dynamics under different position and velocity graph. Combining the graph theory, a novel distributed iterative learning control mechanism is derived, and a sufficient convergence condition is proposed under the compressing mapping framework. It is proved that the convergence can be attained with the existence of the control gain matrix by rigorous analysis. Lastly, the validity of main result is verified by a simulation. © 2019 ieee.

关键词： Multi agent systems

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Adaptive neural control for euler-lagrange systems with unknown control direction 8

Adaptive neural control for euler-lagrange systems with unkn...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Meng, Fanfeng Zhao, Lin Yu, Jinpeng School of Automation Qingdao University Qingdao266071 China

ISBN: (纸本)9781728114545

In this paper, the adaptive neural network control is proposed for Euler-Lagrange systems with unknown control direction. the command filters based virtual control signal, adaptive update law and error compensation mechanism are designed respectively under the Nussbaum type function, which can guarantee that the joint position tracking error reaches to a desired region when the control direction is unknown. the used finite-time command filter can guarantee that the output of the command filter fast approximate the differential of virtual signal. © 2019 ieee.

关键词： Error compensation

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Closed-loop iterative learning control for linear singular systems with fixed initial state errors 8

Closed-loop iterative learning control for linear singular s...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Ding, Shuang Tian, Senping Yu, Shali Li, Xiangyang Academy of Automation Science and Engineering South China University of Technology Guangzhou510000 China Guangzhou College of South China University of Technology Guangzhou510800 China

ISBN: (纸本)9781728114545

With regard to a class of linear singular systems, the problem of iterative learning control in consideration of fixed initial state errors is investigated. By constructing the initial modified signal, a new type of closed-loop iterative learning algorithm is applied to design the iterative learning control system. After enough iterations, complete tracking of the reference signal over the preset time interval can be realized. A simulation example is given to show the effectiveness of the approach. © 2019 ieee.

关键词： learning algorithms

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Distributed switching learning control for nonlinear multi-agent systems with unknown control directions and input disturbances 8

Distributed switching learning control for nonlinear multi-a...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Niu, Xinglong Liu, Peng Ma, Tiehua School of Electrical and Control Engineering North University of China Taiyuan030051 China

ISBN: (纸本)9781728114545

In this paper, we studies the leader-following consensus for a class of uncertain nonlinear multi-agent systems (N-MASs) via switching learning control method. the novelties are reflected in the unknown input disturbances, unknown Lipschitz rate and unknown control directions. these unknowns make the consensus problem different from the existing related literature. To capture these unknowns, we propose a distributed consensus protocol based on a switching logic scheme, which guarantees the leader-following consensus. © 2019 ieee.

关键词： Multi agent systems

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Iterative learning control for nonlinear stieltjes differential equations 8

Iterative learning control for nonlinear stieltjes different...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Chen, Yu Wang, Jinrong Cao, Xiaokai Guizhou University Department of Mathematics Guiyang Guizhou550025 China

the article study the ILC for nonlinear Stieltjes differential equations. We establish sufficient conditions and prove Stieltjes D-type and Stieltjes PD-type learning algorithms convergence results for such equation. ... 详细信息

ISBN: (纸本)9781728114545

关键词： Differential equations

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