检索结果-内蒙古大学图书馆

Adaptive Fault-Tolerant Tracking control of Direct-Drive Servo systems with Actuator Faults and Saturation

IEEE Transactions on Transportation Electrification 2025年

作者： Shi, Pengwei Yu, Xinghu Lin, Xiaotian Qin, Jiahu Sun, Weichao Yongjiang Laboratory Ningbo315200 China University of Science and Technology of China Department of Automation Hefei230027 China Ningbo Institute of Intelligent Equipment Technology Co. Ltd Ningbo315200 China Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin150001 China

Direct-drive servo systems are widely used in the field of transportation, but the existence of actuator failures and saturation will threaten system safety and cause serious traffic accidents. In this paper, an adaptive fault-tolerant tracking control (AFTTC) method for direct-drive servo systems is proposed to address the complex constraint problems in traffic tasks. The system dynamics model with actuator failures and asymmetric saturation is first constructed, and an estimation algorithm with convergence characteristics is comprehensively adopted to deal with actuator failures and parameter uncertainties. In particular, a smooth function is cleverly introduced to achieve a smooth transition of the saturation inflection point output. In order to minimize the influence of various nonlinearities and uncertainties, a finite-time neural network observer is designed. It is worth mentioning that the designed controller is suitable for the above-mentioned complex multi-constraint conditions and can achieve asymptotically stable tracking of the closed-loop system. Experimental results show that compared with traditional adaptive fault-tolerant control, the proposed AFTTC method improves the steady-state performance of the system by 26.5% and 28.2%, which further improves the safety of transportation industry tasks. © 2015 IEEE.

关键词： Adaptive control systems

来源：评论

学校读者我要写书评

暂无评论

Short-Term Load Forecasting for Buildings Based on Dynamic Weighted edRVFL Model

Short-Term Load Forecasting for Buildings Based on Dynamic W...

引用

Chinese automation Congress (CAC)

作者： Junwei Xie Meng Yuan Zhezhuang Xu Chi Liu Yuxiong Xia Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Fuzhou China Fujian Huading Intelligent Manufacturing Technology Co. Ltd Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Short-term load forecasting of buildings plays a key role in demand management, however, when dealing with massive data, the prevalent practice of training and testing the system as a whole overlooks pattern variations inside data and potentially limits predictive performance. To address this issue, this paper proposes an ensemble deep random vector functional link (edRVFL) model that combines adaptive ensemble clustering and dynamic weighting strategy for load forecasting of an industrial building. Firstly, the adaptive ensemble clustering that combines ordering points to identify the clustering structure and K nearest neighbor is employed to determine the electricity load usage patterns. Subsequently, the improved particle swarm optimization algorithm is applied to adjust the weights of load curves in different typical day scenarios. Finally, a dynamic weighting strategy is designed to dynamically assign model weights to two clusters, where one matches the type of predicted load pattern for the day being forecasted and another for the cluster of the previous day. The experiment in a real building verifies the effectiveness of the proposed algorithm, which has higher prediction accuracy than other comparison algorithms including one-week interval and different ensemble clustering.

关键词： Adaptation models Load forecasting Heuristic algorithms Buildings Clustering algorithms Predictive models Prediction algorithms Vectors Forecasting Load modeling

来源：评论

学校读者我要写书评

暂无评论

A Stacking Ensemble Learning Method for Non-Intrusive Appliance Load Monitoring

A Stacking Ensemble Learning Method for Non-Intrusive Applia...

引用

Chinese control and Decision Conference, CCDC

作者： Xudong Cheng Ding Li Wenkai Hu Jinghao Xu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Non-Intrusive Load Monitoring (NILM) collects the overall power consumption information from the power panel with advanced metering infrastructure, and obtains the operating states and power consumption information of each electrical appliance through load decomposition algorithms, so as to come up with appropriate strategies to reduce energy consumption and improve energy efficiency, which is of great significance for energy conservation and reduction. In recent years, NILM methods based on deep learning algorithms have received much attention and achieved good results, but the type of features extracted by a single deep learning model is limited, resulting in the model’s inability to overcome the bottleneck of accurate decomposition. To address the above problems, this paper proposes a deep ensemble learning framework for NILM, which takes the total active power sequence as input and synthesizes the advantages of multiple deep learning neural network models to further improve the performance of appliance identification and power disaggregation. Experiments are designed and conducted on the UK-DALE dataset to verify the effectiveness of the proposed deep ensemble learning method for NILM. The experimental results show that the method proposed in this paper has more advantages and is more promising in terms of various metrics compared to single deep learning approaches.

关键词： Deep learning Training Load monitoring Power demand Accuracy Stacking Neural networks

来源：评论

学校读者我要写书评

暂无评论

Continuous Flow Synthesis of Mesalazine via Data-Driven Nonlinear Model Predictive control

Continuous Flow Synthesis of Mesalazine via Data-Driven Nonl...

引用

European control Conference (ECC)

作者： Ismael Castillo Jakob Rehrl Martin Steinberger Martin Horn Institute of Automation and Control Graz University of Technology Graz Austria Research Center Pharmaceutical Engineering GmbH Inffeldgasse 13/2 Graz Austria Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Institute of Automation and Control Graz University of Technology Graz Austria

The synthesis of Mesalazine via data-driven modelling and a control scheme for the underlying complex continuous flow chemistry process is presented. The challenges of modelling continuous flow synthesis of Mesalazine are overcome by the usage of Neuro-Fuzzy Models together with the so-called Local Linear Model Tree (NFM-LoLiMoT) training algorithm based on data from a highly detailed simulator for the reactor. A state-space representation of the NFM-LoLiMoT allows the implementation of a Non-linear Model Predictive control (NMPC) strategy in order to perform output tracking and fulfil all input and output constraints. The NMPC scheme guarantees stability through the approximation of an Infinite Horizon cost function using a terminal cost and terminal state constraints. The proposed method provides a systematic approach that can be applied for different setup configurations and reduces the time-consuming process of first-principles modelling of the chemical processes. Simulations of a hydrogenation reactor for the synthesis of Mesalazine are presented to show the performance of the introduced method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Stability analysis of systems with two additive time-varying delay components via the zero-valued equations 48

Stability analysis of systems with two additive time-varying...

引用

48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Liu, Meng He, Yong Jiang, Lin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Liverpool Department of Electrical Engineering & Electronics LiverpoolL69 3GJ United Kingdom

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In the case of introducing the double integral state into the augmented vector, the time-varying delay square terms in the derivative of the Lyapunov-Krasovskii functional (LKF) usually needs to be treated with some negative-determination lemmas in the existing literatures, which are only sufficient conditions and are conservative to some extent. In this work, by introducing some new augmented variables, some zero-valued equations are proposed to avoid the appearance of these time-varying delay square terms, which have great potential to reduce the conservatism. Then, a stability result in terms of linear matrix inequalities is derived via the presented zero-valued equations. Finally, a representative example is provided to testify the usefulness and meliority of the raised stability criterion. © 2022 IEEE.

关键词： Additives

来源：评论

学校读者我要写书评

暂无评论

Secure Nonlinear Fusion Estimation Against FDI Attacks:The Completely Distributed Condition

Secure Nonlinear Fusion Estimation Against FDI Attacks:The C...

引用

第42届中国控制会议

作者： Pindi Weng Jiyu Zhang Bo Chen Zheming Wang Tian Wang Department of Automation Zhejiang University of Technology Zhejiang Provincial United Key Laboratory of Embedded Systems School of Instrumentation Science and Engineering Harbin Institute of Technology

This paper considers the problem of multi-sensors fusion estimation for cyber-physical systems(CPSs),where each sensor node sends a local estimate to the fusion center and a subset of the transmitted signals can potentially be under false data injection *** the aforementioned problem,locating the compromised local estimates is of crucial importance for obtaining an accurate fusion state estimate of *** this case,a secure fusion estimation method is proposed based on a Gaussian mixture model based detection ***,the proposed method first clusters the local state estimates autonomously and provides a belief for each sensor,based on which the local estimates can be fused ***,an illustrative example is employed to show the effectiveness of the proposed method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Discovery of Stochastic Differential Equations

引用

engineering 2022年第10期17卷 244-252页

作者： Yasen Wang Huazhen Fang Junyang Jin Guijun Ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye Yuan Jorge Gonçalves Jürgen Kurths Han Ding School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrenceKS 66045USA HUST-Wuxi Research Institute Wuxi 214174China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Department of Plant Sciences University of CambridgeCambridge CB23EAUK Luxembourg Centre for Systems Biomedicine University of LuxembourgBelvaux 4367Luxembourg Department of Physics Humboldt University of BerlinBerlin 12489Germany Department of Complexity Science Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different *** identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s *** practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data *** study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis *** importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series *** effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin *** framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.

关键词： Data-driven method System identification Sparse Bayesian learning Stochastic differential equations Random phenomena

来源：评论

学校读者我要写书评

暂无评论

Open-/Closed-loop Active Learning for Data-driven Predictive control

arXiv

引用

arXiv 2024年

作者： Feng, Shilun Shi, Dawei Shi, Yang Zheng, Kaikai The State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing100081 China The Department of Mechanical Engineering Faculty of Engineering University of Victoria VictoriaBCV8N 3P6 Canada

An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear system with bounded disturbance for both open-loop and closed-loop stages. The learning objective is to minimize the volume of the set of admissible systems. First, a performance measure based on historical data and the input sequence is introduced to characterize the upper bound of the volume of the set of admissible systems. On the basis of this performance measure, an open-loop active learning strategy is proposed to minimize the volume by actively designing inputs during the open-loop stage. For the closed-loop stage, an closed-loop active learning strategy is designed to select and learn from informative closed-loop data. The efficiency of the proposed closed-loop active learning strategy is proved by showing that the unselected data cannot benefit the learning performance. Furthermore, an adaptive predictive controller is designed in accordance with the proposed data acquisition approach. The recursive feasibility and the stability of the controller are proved by analyzing the effect of the closed-loop active learning strategy. Finally, numerical examples and comparisons illustrate the effectiveness of the proposed data acquisition strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

Optimization of the Landslide Susceptibility Model based on Deep Forest and Twice-Sample

Optimization of the Landslide Susceptibility Model based on ...

引用

2022 Chinese automation Congress, CAC 2022

作者： Yang, Yuelin Yu, Wanke Liu, Zepeng Zhao, Lingran Lu, Chengda Wu, Min Niu, Ruiqing China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China China University of Geosciences Institute of Geophysics and Geomatics Wuhan China

ISBN: (纸本)9781665465335

In the analysis of regional landslide susceptibility, the geological data used usually have the characteristics of multiplicity and nonlinearity. At the same time, in the past researches, the selection of non-landslide units in the training set is usually random or subjective, and there is no guarantee sample accuracy. In this paper, the deep forest algorithm combined with the twice-sample method is used to evaluate the landslide susceptibility in the Zigui Badong area of the Three Gorges Reservoir area. First, 11 landslide impact factors were selected according to the geological data to initially construct a sample set, and the cleaned data set was obtained by twice-sample. After dividing the training set and the test set, the deep forest model was used to predict the landslide susceptibility and evaluate the accuracy. The results show that the AUC of the susceptibility prediction model built using the deep forest is 1%-10% higher than that of GBDT, MLP and other methods, while the Twice-Sample method improves the AUC by 2%. © 2022 IEEE.

关键词： Landslides

来源：评论

学校读者我要写书评

暂无评论

High-Performance Synchronization control of Multi-Axis Motion System Based on Fully-Actuated System Approach 26

High-Performance Synchronization Control of Multi-Axis Motio...

引用

26th International Conference on Industrial technology, ICIT 2025

作者： Zhang, Dufei Wang, Yao-Wei Gu, Caoyuan Jia, Yu Wu, Qi Wu, Xiang School of Information Science and Engineering Wuhan University of Science and Technology Wuhan30064 China Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430064 China Zhejiang University of Technology Department of Automation China Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou310023 China

ISBN: (纸本)9798331521950

This paper investigates a class of synchronization control problem of multi-axis motion systems(MAMSs) with uncertainty and external disturbances. Firstly, a system model that satisfies the full-actuation condition is obtained by performing differential decomposition on each axis(subsystem). The uncertainty and external disturbances are treated as the lumped disturbances of the system. Secondly, a fully-actuated system approach based controller is designed to obtain a linear closed-loop system, which the parameters of the system are determined by parametric approach. Furthermore, the synchronization error model is established and a generalized extended state observer(GESO) is designed to estimate the system state while also estimating the lumped disturbances of the system. Then, the controller with disturbance compensation is designed to achieve high-performance synchronization control of the MSMSs, and system stability analysis is provided. Finally, the effectiveness and superior performance of the proposed approach is verified by simulation experiments. © 2025 IEEE.

关键词： Closed loop control systems

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：