检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Reiter, Rudolf Ghezzi, Andrea Baumgärtner, Katrin Hoffmann, Jasper McAllister, Robert D. Diehl, Moritz University of Freiburg Freiburg79110 Germany The Department of Computer Science University of Freiburg Freiburg79110 Germany The Delft Center for Systems and Control Delft University of Technology Delft2628 Netherlands The Department of Microsystems Engineering The Department of Mathematics University of Freiburg Freiburg79110 Germany

Nonlinear model predictive control (MPC) and reinforcement learning (RL) are two powerful control strategies with, arguably, complementary advantages. In this work, we show how actor-critic RL techniques can be leveraged to improve the performance of MPC. The RL critic is used as an approximation of the optimal value function, and an actor roll-out provides an initial guess for primal variables of the MPC. A parallel control architecture is proposed where each MPC instance is solved twice for different initial guesses. Besides the actor roll-out initialization, a shifted initialization from the previous solution is used. Thereafter, the actor and the critic are again used to approximately evaluate the infinite horizon cost of these trajectories. The control actions from the lowest-cost trajectory are applied to the system at each time step. We establish that the proposed algorithm is guaranteed to outperform the original RL policy plus an error term that depends on the accuracy of the critic and decays with the horizon length of the MPC formulation. Moreover, we do not require globally optimal solutions for these guarantees to hold. The approach is demonstrated on an illustrative toy example and an automated driving (AD) overtaking scenario. © 2024, CC BY-NC-ND.

关键词： Dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Boolean matrix compressed sensing

Boolean matrix compressed sensing

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qiang Liu Mahdi Soleymani Hessam Mahdavifar National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements.

关键词： Image coding Signal processing algorithms Signal processing Acoustics Sensors Decoding Sparse matrices Speech processing Compressed sensing

来源：评论

学校读者我要写书评

暂无评论

Robust Voltage Regulation for DC Microgrids via Passivity-Based Sliding Mode control

Robust Voltage Regulation for DC Microgrids via Passivity-Ba...

引用

International Workshop on Variable Structure Systems, VSS

作者： Edoardo Vacchini Michele Cucuzzella Pablo Borja Antonella Ferrara Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Jan C. Willems Center for Systems and Control Engineering and Technology Institute Groningen (ENTEG) Faculty of Science and Engineering University of Groningen Groningen The Netherlands School of Engineering Computing and Mathematics University of Plymouth Plymouth U.K.

ISBN: (数字)9798350353686

ISBN: (纸本)9798350353693

This paper proposes a decentralized sliding mode control approach to the voltage regulation problem in a DC microgrid consisting of distributed generation units interconnected with each other through resistive-inductive power lines and supplying unknown nonlinear loads. In particular, the port-Hamiltonian structure of the system suggests the design of a suitable sliding manifold such that the system on this manifold exhibits desired passivity properties. This approach simplifies the control design and relaxes some restrictive assumptions required by other controllers proposed in the literature, while offering satisfactory performance.

关键词： Manifolds Variable structure systems Power transmission lines Microgrids Power system stability Stability analysis Robustness Reduced order systems Voltage control Sliding mode control

来源：评论

学校读者我要写书评

暂无评论

Design and Simulation of Nano Scale FIN FET Using Silvaco TCAD

Design and Simulation of Nano Scale FIN FET Using Silvaco TC...

引用

2021 International Conference engineering and Telecommunication, En and T 2021

作者： Saurabh, Raman Tripathi, Pramod Narayan Bhasme, Sarang Balasaheb Nazarov, Alexey Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Federal Research Center Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781728183909

We present design and simulation of a single gate Fin-FET structure on the SOI substrate using Silvaco TCAD. A 45 nm channel length Fin-FET structure is compared with conventional MOSFET structure having same channel length. Different structural and process parameters such as length, width, height, dielectric thickness, doping etc has been optimized for designing the Fin-FET. The simulated structure shows the threshold voltage of 0.37643, lower DIBL, higher drain saturation current and improved parameters as compared to the conventional MOSFET structure. © 2021 IEEE.

关键词： Fins (heat exchange)

来源：评论

学校读者我要写书评

暂无评论

Learning-based control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy

arXiv

引用

arXiv 2023年

作者： Yin, Zhenxiao Dai, Xiaobing Yang, Zewen Shen, Yang Hattab, Georges Zhao, Hang Guangzhou China Information-oriented Control Technical University of Munich Munich Germany Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Hong Kong

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

来源：评论

学校读者我要写书评

暂无评论

Robust Consensus of Perturbed Euler-Lagrange Agents with Unknown Disturbances

引用

IFAC-PapersOnLine 2024年第6期58卷 190-195页

作者： Alexey Bobtsov Romeo Ortega Jose Guadalupe Romero Emmanuel Nuño Department of Control Systems and Robotics ITMO University Kronverkskiy av. 49 197101 Saint Petersburg Russia Departament of Electrical and Electronic Engineering ITAM Progreso Tizapán 1 04100 Ciudad de México México Department of Computer Science CUCEI University of Guadalajara 44430 Guadalajara Mexico

In this paper we consider the problems of leaderless consensus for networks of fully actuated Euler-Lagrange agents perturbed by unknown additive disturbances. The network is an undirected weighted graph with time delays. The proposed controller has a PD structure that incorporates, in a certainty-equivalent way, the estimate of the unknown disturbance. The design of the disturbance estimator proceeds along the following steps. First, the derivation of a regression equation, that turns out to be nonlinearly parameterized, but with an injective mapping. Second, we propose to use a recently introduced least-squares plus dynamic regressor extension algorithm that allows us to estimate the unknown frequencies imposing extremely weak excitation assumptions. In this way, we derive a sufficient condition on the proportional and derivative gains of the controller to ensure that the systems globally and asymptotically converge to a consensus position.

关键词： Robust adaptive control Multi-agent systems Disturbance rejection Robust time delay systems

来源：评论

学校读者我要写书评

暂无评论

NFV-Enabled Service Recovery in Space-Air-Ground Integrated Networks: A Matching Game Based Approach

arXiv

引用

arXiv 2025年

作者： Jia, Ziye Cao, Yilu He, Lijun Li, Guangxia Zhou, Fuhui Wu, Qihui Han, Zhu College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China State Key Laboratory of ISN Xidian University Xian710071 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

To achieve ubiquitous connectivity of the sixth generation communication, the space-air-ground integrated network (SAGIN) is a popular topic. However, the dynamic nodes in SAGIN such as satellites and unmanned aerial vehicles, may be fragile and out of operation, which can potentially cause service failure. Therefore, the research on service recovery in SAGIN under situations of resource failure is critical. In order to facilitate the flexible resource utilization of SAGIN, the network function virtualization technology (NFV) is proposed to be employed. Firstly, the task management is transformed into the deployment of service function chains (SFCs). Then, we design an NFV-based SFC recovery model in SAGIN in the face of resource failure, so that tasks can quickly select alternative resources to complete deployments. Moreover, the problem of SFC recovery is formulated to minimize the total time consumption for all completed SFCs. Since it is an NP-hard integer linear programming problem, we propose the efficient recovery algorithm based on the matching game. Finally, via various simulations, the effectiveness of the proposed algorithm and its advantages are verified, where the total time consumption is optimized by about 25%, compared with other benchmark methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Network function virtualization

来源：评论

学校读者我要写书评

暂无评论

DyHGDAT: Dynamic Hypergraph Dual Attention Network for multi-agent trajectory prediction

DyHGDAT: Dynamic Hypergraph Dual Attention Network for multi...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Weilong Lin Xinhua Zeng Chengxin Pang Jing Teng Jing Liu Academy for Engineering&Technology Fudan University Shanghai China School of Electronics and Information Engineering Shanghai University of Electric Power Shanghai China School of Control and Computer Engineering North China Electric Power University Beijing China School of Information Science and Technology Fudan University Shanghai China Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Modeling the interactions among agents based on their historical trajectories is key to precise multi-agent trajectory prediction. Hypergraph Convolutional Networks (HGCN) have become a proper choice for capturing high-order interactions among agents in this field. However, most existing works only consider static hypergraphs, and ignore that in a hypergraph, the power of influence varies between vertices (or hyperedges). Therefore, we propose DyHGDAT, a dynamic hypergraph dual attention network to capture the high-order interactions among agents, which not only models the evolution of hypergraph over time but also highlights the vertices and hyperedges with larger impacts. We apply DyHGDAT to a CVAE-based prediction system for predicting plausible trajectories. To validate the effectiveness of prediction, we evaluate our proposed method on two well-established trajectory prediction datasets: the ETH/UCY datasets and the Stanford Drone Dataset (SDD). The experimental results show that with DyHGDAT, the CVAE-based prediction system out-performs state-of-the-art methods by 12.5%/5.3% in ADE/FDE on ETH/UCY, and the improvement on SDD is 6.4%/7.4%.

关键词： Attention mechanisms Accuracy Predictive models Trajectory Convolutional neural networks Robotics and automation Drones

来源：评论

学校读者我要写书评

暂无评论

Learning-based Rigid Tube Model Predictive control

arXiv

引用

arXiv 2023年

作者： Gao, Yulong Yan, Shuhao Zhou, Jian Cannon, Mark Abate, Alessandro Johansson, Karl H. Department of Electrical and Electronic Engineering Imperial College London United Kingdom Department of Mathematics University of Stuttgart Germany Department of Electrical Engineering Linköping University Sweden Department of Engineering Science University of Oxford United Kingdom Department of Computer Science University of Oxford United Kingdom Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

This paper is concerned with model predictive control (MPC) of discrete-time linear systems subject to bounded additive disturbance and mixed constraints on the state and input, whereas the true disturbance set is unknown. Unlike most existing work on robust MPC, we propose an algorithm incorporating online learning that builds on prior knowledge of the disturbance, i.e., a known but conservative disturbance set. We approximate the true disturbance set at each time step with a parameterised set, which is referred to as a quantified disturbance set, using disturbance realisations. A key novelty is that the parameterisation of these quantified disturbance sets enjoys desirable properties such that the quantified disturbance set and its corresponding rigid tube bounding disturbance propagation can be efficiently updated online. We provide statistical gaps between the true and quantified disturbance sets, based on which, probabilistic recursive feasibility of MPC optimisation problems is discussed. Numerical simulations are provided to demonstrate the effectiveness of our proposed algorithm and compare with conventional robust MPC algorithms. © 2023, CC BY.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：