检索结果-内蒙古大学图书馆

63rd IEEE Conference on Decision and control, CDC 2024

作者： Broens, Yorick Butler, Hans Toth, Roland Asml Veldhoven Netherlands Eindhoven University of Technology Department of Electrical Engineering Eindhoven Netherlands Hunren Institute for Computer Science and Control Systems and Control Laboratory Hungary

ISBN: (纸本)9798350316339

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator prototype. © 2024 IEEE.

关键词： Continuous time systems

来源：评论

学校读者我要写书评

暂无评论

A Novel Trajectory Planning Method Based on Trust Region Policy Optimization

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Ye, Bao-Lin Zhang, Jiajie Li, Lingxi Wu, Weimin Jiaxing Key Laboratory of Smart Transportations the School of Information Science and Engineering Jiaxing University China Elmore Family School of Electrical and Computer Engineering Purdue University Indianapolis USA State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Trajectory planning method is a research hotspot in autonomous driving. Existing reinforcement learning-based trajectory planning methods suffer from unstable performance due to the strong randomness of network weight parameter updates during the training process. Therefore, this paper proposes a novel trajectory planning method based on deep reinforcement learning trust region policy optimization (TRPO). Firstly, in order to enhance the robustness of the trajectory planning method based on deep reinforcement learning TRPO, a TRPO-LSTM based decision model was proposed. More specifically, a long short term memory (LSTM) based state feature extraction network was designed and embeded into a TRPO-based decision model to enhance the ability of TRPO to extract information from the environmental state space. Secondly, in order to make the planned trajectory adaptive to the dynamic changes of traffic environment, we presented a novel TRPO-LSTM trajectory fitting algorithm. To the best of our knowledge, this is the first work aiming at applying the TRPO-LSTM based decision model in the trajectory fitting process to search the optimal longitudinal trajectory speed. Finally, the proposed trajectory planning method was implemented and simulated on the CARLA simulator. The experimental results show that, compared with existing trajectory planning methods based on deep reinforcement learning algorithms, our proposed method achieves a cumulative reward improvement of over 28.9% in the scenario of four lane highway, and has better robustness. Meanwhile, the proposed method can achieve a lower collision rate of 0.93% while improving the average speed and comfort of vehicle driving. IEEE

关键词： Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption

引用

computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

来源：评论

学校读者我要写书评

暂无评论

Behavioural Swarm Optimisation for Stable Slung-Load Aerial Transportation

Behavioural Swarm Optimisation for Stable Slung-Load Aerial ...

引用

2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Chen, Jingyu Oyekan, John The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom University of York Department of Computer Science York United Kingdom

ISBN: (纸本)9798350314588

Collaboration of agents in a natural swarm enables the accomplishment of tasks that would be difficult or impossible for a single agent to complete alone. For example, a swarm of autonomous Unmanned Aerial Vehicles (UAVs) enables the collaborative sensing of bulky loads for transportation over impassable terrains when the load weighs several times more than each UAV. In this work, we propose a hierarchical algorithmic architecture that supports the search and coverage of various unknown payload profiles for subsequent transportation. The grasping formation of UAVs over the payload emerges from the synthetic behaviours in the architecture without any path planning. Experiments show that our proposed design can be successfully applied in searching and coverage of various loads and has been validated in the real world through the use of Crazyflie micro-UAVs. Furthermore, the proposed grasping formation satisfies static equilibrium thereby reducing orientation changes in the load-swarm system during transportation. © 2023 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

On tuning of ADRC with competing design indices:a quantitative study

引用

control Theory and Technology 2023年第1期21卷 16-33页

作者： Yutao Zhao Yi Huang Zhiqiang Gao Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Department of Electrical Engineering and Computer Science Center for Advanced Control TechnologiesCleveland State UniversityClevelandOH 44115USA

controller optimization has mostly been done by minimizing a certain single cost *** practice,however,engineers must contend with multiple and conflicting considerations,denoted as design indices(DIs)in this *** to account for such complexity and nuances is detrimental to the applications of any advanced control *** paper addresses this challenge heads on,in the context of active disturbance rejection controller(ADRC)and with four competing DIs:stability margins,tracking,disturbance rejection,and noise *** this end,the lower bound for the bandwidth of the extended state observer is first established for guaranteed closed-loop ***,one by one,the mathematical formula is meticulously derived,connecting each DI to the set of controller *** our best knowledge,this has not been done in the context of *** formulas allow engineers to see quantitatively how the change of each tuning parameter would impact all of the DIs,thus making the guesswork *** example is given to show how such analytical methods can help engineers quickly determine controller parameters in a practical scenario.

关键词： Uncertain systems Active disturbance rejection control(ADRC) Design indices(DIs) Extended state observer(ESO)

来源：评论

学校读者我要写书评

暂无评论

Enhanced cyber-physical system with semantic technologies and machine learning to support smart farming 24

Enhanced cyber-physical system with semantic technologies an...

引用

24th International Conference on control systems and computer science, CSCS 2023

作者： Ciolofan, Sorin N. Dragoicea, Monica Popeanga, Daniel-Catalin Politehnica University of Bucharest Department of Computer Science Bucharest Romania Politehnica University of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

An important aspect related to the effects of agricultural activities on the environment is represented by the nutrient loss in water and air (specifically nitrogen). The interactions between catchments hydrological processes, management of farm activities, climate changes and nitrogen losses constitute a complex phenomenon yet not well understood, being an important concern from the sustainable agriculture perspective. Nitrogen can be lost with water as leaching or runoff, or as gas as ammonia volatilization. Nitrous oxide (N2O) is particularly problematic because it is also a powerful greenhouse gas. The goal of the current article is to present innovative digital techniques to advance in understanding of this phenomena through an Information System that integrates Artificial Intelligence techniques such as Semantic Technologies and Machine Learning (ML) into Cyber-Physical systems (CPS) to support smart farming and sustainable agriculture. © 2023 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Regularized Iterative Weighted Total Least Squares for Vehicle Mass Estimation ⁎

引用

IFAC-PapersOnLine 2024年第15期58卷 37-42页

作者： Hugo Koide Jeremy Vayssettes Guillaume Mercère Laboratory of Computer Science and Automatic Control for Systems Poitiers University Poitiers France Michelin Research and Development Center Cébazat 63118 France

This work addresses the challenge of vehicle mass estimation using a longitudinal vehicle dynamics model, which adopts an errors-in-variables formulation due to the presence of noise-contaminated measurements in both input and output variables. The reduced vehicle dynamics model is ill-conditioned by nature of correlated input variables and a lack of persistent excitation in the measured data. A regularized iterative weighted total least squares (RIWTLS) method is therefore developed and has the advantage of producing parameter uncertainty quantification and measurement bias estimation alongside the estimated system parameters. A complementary adaptive regularization scheme is developed and serves to control the numerical stability of the RIWTLS algorithm based on the conditioning of incoming data. Experimental tests using electric vehicle data and a batch estimation scheme highlight the performance of the proposed RIWTLS algorithm, estimating vehicle mass to within ±1% accuracy.

关键词： Vehicle mass estimation Errors-in-variables Weighted total least squares Uncertainty quantification

来源：评论

学校读者我要写书评

暂无评论

Robust Triangular Decoupling Velocity Varying controllers for Four Wheel Steering Autonomous Vehicles 29

Robust Triangular Decoupling Velocity Varying Controllers fo...

引用

29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Skarpetis, Michael G. Koumboulis, Fotis N. School of Science National and Kapodistrian University of Athens Robotics Automatic Control and Cyber-Physical Systems Laboratory Department of Digital Industry Technologies Euripus Campus Euboea 34400 Greece

ISBN: (纸本)9798350361230

This study explores the challenges of achieving triangular decoupling in a specific group of linear parameter-dependent systems that have uncertain structures, with a particular emphasis on its relevance to four-wheel steering (4WS) vehicles. A new method is presented, combining a state feedback controller that includes static and time-varying elements. The main goal of this controller design is to guarantee triangular decoupling properties and Hurwitz invariability in the face of system uncertainties. The proposed method involves developing the controller and setting solvability criteria to transform the original Parameter Dependent Uncertain System into an uncertain Linear Time-Invariant (LTI) system and subsequently meeting LTI triangular decoupling characteristics. The practical significance of the results is shown by their effective use in controlling the motion of a velocity varying four-wheel steering (4WS) car model with uncertain virtual mass and cornering stiffnesses. © 2024 IEEE.

关键词： Four wheel steering

来源：评论

学校读者我要写书评

暂无评论

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING 11

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

引用

IEEE/CAA Journal of automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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：