Transactions of the Institute of Measurement and Control最新文献_第4页

Fixed-time sliding mode observer-based multi-faults estimation method and its application to PMSM drive system 基于定时滑模观测器的多故障估计方法及其在永磁同步电机驱动系统中的应用

4区计算机科学

Transactions of the Institute of Measurement and Control Pub Date : 2023-10-18 DOI: 10.1177/01423312231200058

Zhangyu Zhou, Feng Xu, Yuchen Dai

引用次数: 0

Factors affecting trust in the transparency index for stable and intuitive physical human–robot cooperation 稳定直观的物理人机合作透明度指标中影响信任的因素

4区计算机科学

Transactions of the Institute of Measurement and Control Pub Date : 2023-10-18 DOI: 10.1177/01423312231200340

Ding Duan, Yuling Li, Hongbing Li

引用次数: 0

Compound fault separation and diagnosis method for online rolling bearings based on RSEUnet and 1DCNN 基于RSEUnet和1DCNN的在线滚动轴承复合故障分离与诊断方法

4区计算机科学

Transactions of the Institute of Measurement and Control Pub Date : 2023-10-17 DOI: 10.1177/01423312231198953

Xiufang Wang, Shuang Sun, Chunlei Jiang, Hongbo Bi, Wendi Yan

{"title":"Compound fault separation and diagnosis method for online rolling bearings based on RSEUnet and 1DCNN","authors":"Xiufang Wang, Shuang Sun, Chunlei Jiang, Hongbo Bi, Wendi Yan","doi":"10.1177/01423312231198953","DOIUrl":"https://doi.org/10.1177/01423312231198953","url":null,"abstract":"Compound fault signals interfere with one another, resulting in an inconspicuous feature extraction that requires sophisticated signal processing techniques and expert experience. However, good online diagnostic methods are not available to carry out this process. This paper proposes a method based on Residual Connection and Squeeze-and-Excitation Unet (RSEUnet) and one-dimensional convolutional neural network (1DCNN). The process includes fault separation and diagnosis. First, the feature extraction module of the RSEUnet network introduces an attention mechanism and a residual connection that adaptively assigns various weights to different channels. This model is used to train the maps of the fault signal after time-frequency transformation. Ideal binary masks with excellent performance are the training targets to complete the intelligent separation of compound faults. Second, the 1DCNN is used as a feature learning model to efficiently learn the features of single faults from time-domain signals. An embedded system consisting of a Jetson Nano and a signal acquisition circuit is then built to perform online diagnosis. The test is carried out on the fault experimental platform. Results show that the method has an accuracy of 99.71%, making it highly suitable for the diagnosis of bearing compound faults.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136032935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive dynamic windowing approach based on risk degree function 基于风险度函数的自适应动态窗口方法

4区计算机科学

Transactions of the Institute of Measurement and Control Pub Date : 2023-10-17 DOI: 10.1177/01423312231199807

Liu Yang, Chunhui Li, Qingtao Wang, Shuxian Shi, Mengru Yang

{"title":"Adaptive dynamic windowing approach based on risk degree function","authors":"Liu Yang, Chunhui Li, Qingtao Wang, Shuxian Shi, Mengru Yang","doi":"10.1177/01423312231199807","DOIUrl":"https://doi.org/10.1177/01423312231199807","url":null,"abstract":"Aiming at the problem that the dynamic window approach (DWA) has low planning efficiency and it is difficult to avoid fast-moving obstacles, an improved adaptive DWA based on risk degree function is proposed in this paper. The risk function is designed and introduced into the evaluation function of the traditional DWA to evaluate the risk of collision between dynamic obstacles and the robot, so that the robot can effectively avoid faster obstacles. Then, according to the fuzzy control principle, the adaptive weight coefficient is designed to improve the evaluation function, so that the mobile robot can move to the target point more efficiently. The simulation results show that compared with the traditional DWA, the adaptive DWA based on risk degree function reduces the time of completing the planning task by about 8%, and the path length after the completion of the planning by about 8%, it indicates that the improved algorithm has higher efficiency. After 50 repeated experiments, using the adaptive DWA based on risk degree function successfully avoids all obstacles to complete the planning task, which shows that this algorithm has higher security.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136032638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Control-error-based output-feedback adaptive decentralized neural network controller for interconnected uncertain strict-feedback nonlinear systems with input saturation 输入饱和不确定严格反馈互联系统基于控制误差的输出反馈自适应分散神经网络控制器

4区计算机科学

Transactions of the Institute of Measurement and Control Pub Date : 2023-10-16 DOI: 10.1177/01423312231198920

Oussama Bey, Mohamed Chemachema

{"title":"Control-error-based output-feedback adaptive decentralized neural network controller for interconnected uncertain strict-feedback nonlinear systems with input saturation","authors":"Oussama Bey, Mohamed Chemachema","doi":"10.1177/01423312231198920","DOIUrl":"https://doi.org/10.1177/01423312231198920","url":null,"abstract":"In this paper, a control-error-based decentralized neural network (NN) direct adaptive controller is presented for uncertain interconnected nonlinear systems, in strict-feedback form, subject to input saturation and external disturbances with unavailable states for measurement. Different from the existing results in the literature, the proposed approach is based on the control error instead of the tracking error resulting in a separation-like principle. Furthermore, the explosion of complexity due to back-stepping recursive design is completely avoided along with discarding all restrictive assumptions imposed on the unmatched interconnections. Actually, NNs are used to approximate the unknown ideal control laws, and auxiliary control terms are appended to deal with approximation errors and enhance the stability of the closed-loop system. Besides, fuzzy inference systems are introduced to estimate the unknown control errors, leading to simplified derivation of adaptive laws. Thanks to the strictly positive real (SPR) property, the tracking errors are proved to converge asymptotically to zero using Lyapunov theory, which is superior to bounded stability results usually found in the literature. Simulation results show the effectiveness of the proposed approach.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136113503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive stabilization of constrained stochastic nonlinear systems with input saturation: A combined BLF and NN approach 输入饱和约束随机非线性系统的自适应镇定:BLF和NN相结合的方法

4区计算机科学

Transactions of the Institute of Measurement and Control Pub Date : 2023-10-16 DOI: 10.1177/01423312231200040

Huifang Min, Shang Shi, Hongyan Feng

引用次数: 0

Security bipartite synchronization of MASs resilient to DoS attacks 大规模抗DoS攻击的安全双侧同步

4区计算机科学

Transactions of the Institute of Measurement and Control Pub Date : 2023-10-14 DOI: 10.1177/01423312231198950

Zhenyu Chang, Hongjing Liang, Liang Cao

引用次数: 0

Stability analysis and L1-gain characterization of positive sampled-data systems 正采样数据系统的稳定性分析和l1增益特性

4区计算机科学

Transactions of the Institute of Measurement and Control Pub Date : 2023-10-14 DOI: 10.1177/01423312231198559

Meng-Jie Hu, Xinchen Shi, Zhuoqi Sun

引用次数: 0

A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces 基于反向传播神经网络的不确定曲面自适应采样策略

4区计算机科学

Transactions of the Institute of Measurement and Control Pub Date : 2023-10-14 DOI: 10.1177/01423312231198567

Feng Gao, Yuan Zheng, Yan Li, Wenqiang Li

{"title":"A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces","authors":"Feng Gao, Yuan Zheng, Yan Li, Wenqiang Li","doi":"10.1177/01423312231198567","DOIUrl":"https://doi.org/10.1177/01423312231198567","url":null,"abstract":"Owing to the lack of prior knowledge, the accurate reconstruction of surfaces with high uncertainty is dependent on the reasonable real-time selection of the next best point (NBP) during the sampling process. In this study, a new informative criterion called the MaxCWVar weighting shape effect is proposed for NBP selection. The responses to the geometric features of the candidate locations are predicted by a back propagation neural network (BPNN), which is then used in combination with the jackknife method to estimate the candidate uncertainty. The blade cross-section sampling case is considered to validate the flexibility and effectiveness of the proposed method. A comparison with other adaptive sampling strategies shows that BPNN-based response prediction is well-suited for allocating sample points. In contrast to other NBP selection criteria, the sample point distribution recommended by the MaxCWVar criterion is preferable as it improves the reconstruction accuracy and modeling efficiency. This study promotes the exploration of metrological methods for the fast and intelligent reconstruction of complex surfaces with high uncertainty.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135803886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A model predictive control trajectory tracking lateral controller for autonomous vehicles combined with deep deterministic policy gradient 结合深度确定性策略梯度的自动驾驶汽车模型预测控制轨迹跟踪横向控制器

4区计算机科学

Transactions of the Institute of Measurement and Control Pub Date : 2023-10-13 DOI: 10.1177/01423312231197854

Zhaokang Xie, Xiaoci Huang, Suyun Luo, Ruoping Zhang, Fang Ma

{"title":"A model predictive control trajectory tracking lateral controller for autonomous vehicles combined with deep deterministic policy gradient","authors":"Zhaokang Xie, Xiaoci Huang, Suyun Luo, Ruoping Zhang, Fang Ma","doi":"10.1177/01423312231197854","DOIUrl":"https://doi.org/10.1177/01423312231197854","url":null,"abstract":"To solve the problem of trajectory tracking lateral control in autonomous driving technology, a model predictive control (MPC) controller trajectory tracking lateral control method combined with a deep deterministic policy gradient algorithm (DDPG) is proposed in this paper. This method inputs the real-time state of the vehicle into DDPG to achieve real-time automatic optimization of the prediction time domain and control time domain parameters of the MPC controller, and then affects the specific performance of the MPC controller in trajectory tracking lateral control. Specifically, the state space, action space, and reward function of DDPG are defined, and the automatic driving trajectory tracking lateral controller is designed in combination with the vehicle dynamics model. To reduce the exploration space of DDPG and improve the training efficiency of the entire model, the technique of advantage-disadvantage experience separation and extraction is introduced. Finally, the proposed method was trained and verified in various scenarios, and compared with two other lateral control methods for autonomous driving. The results showed that the learning and training time of the trajectory tracking lateral control method based on DDPG-MPC was shorter than that of the DDPG-based method, and the evaluation indicators in the trajectory tracking control process were better than those of the DDPG-based method and original MPC-based method.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135859050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0