International Journal of Control Automation and Systems最新文献_第8页

Application of a Hybrid Improved Particle Swarm Algorithm for Prediction of Cutting Energy Consumption in CNC Machine Tools 应用混合改进型粒子群算法预测数控机床的切削能耗

IF 3.2 3区计算机科学

International Journal of Control Automation and Systems Pub Date : 2024-05-29 DOI: 10.1007/s12555-022-0784-2

Jidong Du, Yan Wang, Zhicheng Ji

{"title":"Application of a Hybrid Improved Particle Swarm Algorithm for Prediction of Cutting Energy Consumption in CNC Machine Tools","authors":"Jidong Du, Yan Wang, Zhicheng Ji","doi":"10.1007/s12555-022-0784-2","DOIUrl":"https://doi.org/10.1007/s12555-022-0784-2","url":null,"abstract":"Estimation and analysis of energy consumption for machine tool is the basis of energy efficiency improvement. To improve the accuracy of ELM algorithm in CNC machine tool energy consumption prediction, a prediction method based on an improved particle swarm optimization (CAPSO) algorithm and an extreme learning machine (ELM) is proposed. The contribution of the algorithm includes the following three aspects. First, sobol sequence is used to initialize the PSO population to make distribution of initial population more even in solution space. Second, the center wanders and boundary neighborhood updates strategy are used to improve the population quality and convergence rate of PSO. Then, to avoid the optimal local solution, the adaptive inertia weight is introduced to achieve the stochastic perturbation of the population. The performance of the algorithm is tested by ten benchmark function, indicating that the CAPSO ensures the search accuracy and improves the algorithm’s convergence rate. Finally, the CAPSO algorithm is used to optimize the weights and thresholds of an ELM, and the CAPSO-ELM cutting energy consumption prediction model is established. Case analysis and comparative experiments show that the stability, prediction accuracy and generalization ability of CAPSO-ELM model are better than those of other models.","PeriodicalId":54965,"journal":{"name":"International Journal of Control Automation and Systems","volume":"10 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141172171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Hybrid Systems Approach to Consensus of Nonlinear Multi-agent Systems With Self-triggered Output Feedback Control 利用自触发输出反馈控制实现非线性多代理系统共识的混合系统方法

IF 3.2 3区计算机科学

International Journal of Control Automation and Systems Pub Date : 2024-05-29 DOI: 10.1007/s12555-023-0409-4

Wenliang Pei, Changchun Hua, Hailong Cui, Guanglei Zhao

引用次数: 0

Convergence of a Democratic System Controlled by Dynamic Social Networks 由动态社会网络控制的民主制度的融合

IF 3.2 3区计算机科学

International Journal of Control Automation and Systems Pub Date : 2024-05-29 DOI: 10.1007/s12555-023-0515-3

Seong-Jin Park

引用次数: 0

Nonsingular Fixed-time Fault-tolerant Sliding Mode Control of Robot Manipulator With Disturbance Observer 带有扰动观测器的机器人机械手非奇异固定时间容错滑模控制

IF 3.2 3区计算机科学

International Journal of Control Automation and Systems Pub Date : 2024-05-29 DOI: 10.1007/s12555-022-0594-6

Xiaohan Fang, Rong Cheng, Songsong Cheng, Yuan Fan

引用次数: 0

Output Feedback Based Adaptive Continuous Sliding Mode Control for Mechanical Systems With Actuator Faults 基于输出反馈的致动器故障机械系统自适应连续滑模控制

IF 3.2 3区计算机科学

International Journal of Control Automation and Systems Pub Date : 2024-05-29 DOI: 10.1007/s12555-023-0490-8

Tao Jiang, Yan Yan, Shuang-He Yu, Tie-Shan Li

{"title":"Output Feedback Based Adaptive Continuous Sliding Mode Control for Mechanical Systems With Actuator Faults","authors":"Tao Jiang, Yan Yan, Shuang-He Yu, Tie-Shan Li","doi":"10.1007/s12555-023-0490-8","DOIUrl":"https://doi.org/10.1007/s12555-023-0490-8","url":null,"abstract":"In this article, an adaptive continuous sliding mode control (SMC) scheme is presented for the trajectory tracking problem of mechanical systems with parameter uncertainties, external disturbances and actuator faults. The hyperbolic tangent function is widely used to replace the signum function in SMC to ensure that the robust term is continuous and to reduce chattering. Since such an approach is difficult for SMC schemes with adaptive gain to induce system stability through Lyapunov functions, we reconstruct the hyperbolic tangent function by taking both the adaptive control gain and sliding variables as inputs. The designed gain dynamics do not require a priori upper bound on lumped uncertainties, including parameter uncertainties, external disturbances and actuator faults, and ensure no overestimated gains. Besides, an adaptive dual-layer super-twisting (ADLST) observer is adopted to accurately estimate unmeasurable velocities, which achieves the synthesis of an adaptive sliding mode observer and the continuous SMC method with adaptive gain. It is proven through the Lyapunov function that all closed-loop signals are ultimately bounded. Comparative simulations are conducted on a two-link rigid manipulator to demonstrate the effectiveness of the adopted observer and the proposed scheme.","PeriodicalId":54965,"journal":{"name":"International Journal of Control Automation and Systems","volume":"70 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141172246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

General Decay Synchronization of State and Spatial Diffusion Coupled Delayed Memristive Neural Networks With Reaction-diffusion Terms 带有反应扩散项的状态和空间扩散耦合延迟记忆神经网络的一般衰减同步化

IF 3.2 3区计算机科学

International Journal of Control Automation and Systems Pub Date : 2024-05-29 DOI: 10.1007/s12555-023-0122-3

Yanli Huang, Xin Zhao

{"title":"General Decay Synchronization of State and Spatial Diffusion Coupled Delayed Memristive Neural Networks With Reaction-diffusion Terms","authors":"Yanli Huang, Xin Zhao","doi":"10.1007/s12555-023-0122-3","DOIUrl":"https://doi.org/10.1007/s12555-023-0122-3","url":null,"abstract":"This paper aims at the study of the general decay synchronization of state and spatial diffusion coupled delayed reaction-diffusion memristive neural networks (CDRDMNNs). At first, we present the general decay synchronization concept for the considered network based on ψ-type stability, then we go deeply into the establishment of general decay synchronization criterion of state CDRDMNNs by excogitating a proper controller and utilizing an opportune Lyapunov functional, thereby gain a sufficient condition for attaining general decay synchronization of state CDRDMNNs. Subsequently, we devote ourselves to investigating the general decay synchronization of spatial diffusion CDRDMNNs and derive an adequate condition for realizing the general decay synchronization of this type of network. Next, some sufficient conditions which ensure robust general decay synchronization of state CDRDMNNs and spatial diffusion CDRDMNNs with uncertain parameters are concluded by utilizing various types of inequality techniques. Ultimately, the efficacy of the acquired general decay synchronization results are certified via numerical simulations of two examples.","PeriodicalId":54965,"journal":{"name":"International Journal of Control Automation and Systems","volume":"25 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141172247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Event-triggered Hybrid Force Feedback Architecture With Tank-based Stabilization Method for Complicated Bilateral Teleoperation Tasks 采用基于坦克的稳定方法的事件触发混合力反馈架构，适用于复杂的双侧远程操作任务

IF 3.2 3区计算机科学

International Journal of Control Automation and Systems Pub Date : 2024-05-29 DOI: 10.1007/s12555-023-0173-5

Zhitao Gao, Fangyu Peng, Chen Chen, Yukui Zhang, Yu Wang, Rong Yan, Xiaowei Tang

{"title":"Event-triggered Hybrid Force Feedback Architecture With Tank-based Stabilization Method for Complicated Bilateral Teleoperation Tasks","authors":"Zhitao Gao, Fangyu Peng, Chen Chen, Yukui Zhang, Yu Wang, Rong Yan, Xiaowei Tang","doi":"10.1007/s12555-023-0173-5","DOIUrl":"https://doi.org/10.1007/s12555-023-0173-5","url":null,"abstract":"Bilateral teleoperation with force feedback allows the operators to apply their skills to accomplish challenging tasks safely. Most teleoperation bilateral systems are designed for single interaction scenarios and low-frequency force feedback, which limits their overall performance in complex interaction tasks. Furthermore, the use of passive controllers to ensure system stability can lead to further reductions in force transparency. This paper addresses the hybrid force feedback problem in complex interaction tasks with multiple stages, aiming at enhancing the practicality and robustness of teleoperation systems for complex interaction tasks, as well as reducing the force distortion caused by passive controllers. Firstly, an event-triggered hybrid force feedback architecture is proposed. Within this architecture, we introduce a two-channel fully transparent method with an explicit force controller (FT2-EFC), to enable model-free force tracking during both free motion and vibration contact stages. Besides, an adaptive impedance matching (AIM) algorithm is proposed to improve the physical interaction characteristics in the contact transient stage. Secondly, we present the operator passivity reference dual boundary energy tank (OPRDB-ET) method, which not only ensures the delay stability of the force architecture but also minimizes force distortion resulting from passive damping injection. Finally, experiments demonstrated that the proposed methods ensure the accurate tracking ability of hybrid forces in all stages of complicated interaction tasks and the slight force distortion under communication delay.","PeriodicalId":54965,"journal":{"name":"International Journal of Control Automation and Systems","volume":"98 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141172176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Method of Predicting Braking Intention Using LSTM-CNN-Attention With Hyperparameters Optimized by Genetic Algorithm 利用遗传算法优化超参数的 LSTM-CNN-Attention 预测制动意图的方法

IF 3.2 3区计算机科学

International Journal of Control Automation and Systems Pub Date : 2024-05-29 DOI: 10.1007/s12555-021-1113-x

Wei Yang, Yu Huang, Kongming Jiang, Zhen Zhang, Ketong Zong, Qin Ruan

{"title":"Method of Predicting Braking Intention Using LSTM-CNN-Attention With Hyperparameters Optimized by Genetic Algorithm","authors":"Wei Yang, Yu Huang, Kongming Jiang, Zhen Zhang, Ketong Zong, Qin Ruan","doi":"10.1007/s12555-021-1113-x","DOIUrl":"https://doi.org/10.1007/s12555-021-1113-x","url":null,"abstract":"Prediction of a driver’s braking intention enables the advanced driver assistance system (ADAS) to intervene in the braking system as early as possible, which may shorten braking distance and improve driving safety. This paper proposes a novel deep learning model called LSTM-CNN-Attention that combines a long short-term memory (LSTM) neural network, convolutional neural network (CNN), and Attention mechanism for extracting spatiotemporal features of multi-sensor data to improve prediction accuracy. The proposed model inherits both temporal and spatial feature extraction abilities from LSTM and CNN. The LSTM-CNN-Attention model has a parallel architecture, which enhances the feature extraction ability of the model for multi-sensor time series data and improves the prediction accuracy of the driver’s braking intention before the braking action. Furthermore, a driving simulator is set up to sample driving data for training and evaluating the proposed method. According to the results of the experiment, the model obtains up to 3.16% higher accuracy than the baseline models such as LSTM, CNN, and bidirectional LTSM (Bi-LSTM). Additionally, the influence of sliding window size and prediction horizon on the performance of the method is investigated. A method of tuning hyperparameters using the genetic algorithm is presented. The results demonstrate that the prediction accuracy increases by about 2% after being optimized by GA.","PeriodicalId":54965,"journal":{"name":"International Journal of Control Automation and Systems","volume":"33 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141172242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Exponential Synchronization of Stochastic Time-delayed Memristor-based Neural Networks via Pinning Impulsive Control 通过针刺脉冲控制实现基于随机延时记忆器的神经网络指数同步

IF 3.2 3区计算机科学

International Journal of Control Automation and Systems Pub Date : 2024-05-29 DOI: 10.1007/s12555-022-1090-8

Yao Cui, Pei Cheng

引用次数: 0

Fuzzy Adaptive Fault-tolerant Control for Underactuated Wheeled Mobile Robot With Prescribed Performance 具有规定性能的欠驱动轮式移动机器人的模糊自适应容错控制

IF 3.2 3区计算机科学

International Journal of Control Automation and Systems Pub Date : 2024-05-28 DOI: 10.1007/s12555-023-0454-z

Pingfan Liu, Shaocheng Tong

引用次数: 0