Transactions of the Institute of Measurement and Control最新文献_第2页

Volterra operator-based fixed-time adaptive parameter estimation for DC-DC buck converters without current sensors 基于 Volterra 算子的固定时间自适应参数估计，适用于无电流传感器的 DC-DC 降压转换器

Transactions of the Institute of Measurement and Control Pub Date : 2024-08-08 DOI: 10.1177/01423312241262540

Jiazhou Lu, Guosheng Zhang, Liaoxuan Dai, H. Min, Shi Shang

引用次数: 0

Nonlinear disturbance observer–based adaptive neural control for electro-hydraulic servo system with model uncertainty and full-state constraints 基于非线性扰动观测器的自适应神经控制，用于具有模型不确定性和全状态约束的电液伺服系统

Transactions of the Institute of Measurement and Control Pub Date : 2024-08-08 DOI: 10.1177/01423312241266687

Zhenshuai Wan, Chong Liu, Yu Fu

{"title":"Nonlinear disturbance observer–based adaptive neural control for electro-hydraulic servo system with model uncertainty and full-state constraints","authors":"Zhenshuai Wan, Chong Liu, Yu Fu","doi":"10.1177/01423312241266687","DOIUrl":"https://doi.org/10.1177/01423312241266687","url":null,"abstract":"The electro-hydraulic servo system (EHSS) performs model uncertainty and state constraints such that the exact model-based controller is difficult to be designed. In this work, a nonlinear disturbance observer (NDO)-based adaptive neural control (ANC) is proposed for the EHSS, in which a nonlinear transformation function is constructed to make the state constraints problem transformed into state unconstraint problem. The NDO is introduced to improve the disturbance rejection ability. The ANC is utilized to approximate unmodeled dynamics. The second-order filters are integrated with backstepping control to solve the explosion of complexity. The proposed NDO-based ANC scheme confines all states within the predefined bounds, improves the robustness of closed-loop system, and alleviates the computation burden. Moreover, the stability analysis for the closed-loop system is given within the Lyapunov framework. Simulations and experiments show that the proposed control scheme can achieve excellent control performance and robustness with regard to full-state constraints and model uncertainty.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"18 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent automobile path tracking control based on T-S fuzzy 基于 T-S 模糊的智能汽车路径跟踪控制

Transactions of the Institute of Measurement and Control Pub Date : 2024-08-08 DOI: 10.1177/01423312241266663

Liang Huang, Qiping Chen, Zhiqiang Jiang, Chengping Zhong, Daoliang You

{"title":"Intelligent automobile path tracking control based on T-S fuzzy","authors":"Liang Huang, Qiping Chen, Zhiqiang Jiang, Chengping Zhong, Daoliang You","doi":"10.1177/01423312241266663","DOIUrl":"https://doi.org/10.1177/01423312241266663","url":null,"abstract":"To coordinate the accuracy and driving stability of intelligent automobile in the path tracking process and improve the adaptive capability of the control algorithm to different working conditions, an intelligent automobile path tracking control method based on T-S fuzzy is proposed. First, the lateral deviation and heading angle deviation during tracking are considered, and the path tracking error equation is established using a 2 degree-of-freedom single-track dynamic model. Second, an adaptive preview algorithm based on vehicle speed, reference path curvature and heading angle deviation is designed, and feedforward control is designed based on the results of the algorithm. Then, the T-S fuzzy control method with fast decision-making capability is utilized to realize the adaptive adjustment of the weight coefficients of the linear quadratic regulation (LQR) controller to adapt to the variable weight path tracking control under different working conditions. Finally, the designed control method is tested on a double-lane road condition using the Carsim-Simulink co-simulation platform. The results show that the designed controller has high tracking accuracy, and can maintain good accuracy and driving stability under different working conditions.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gearbox fault diagnosis based on improved multi-scale fluctuation dispersion entropy and multi-cluster feature selection 基于改进的多尺度波动离散熵和多聚类特征选择的齿轮箱故障诊断

Transactions of the Institute of Measurement and Control Pub Date : 2024-08-08 DOI: 10.1177/01423312241267043

Baoyue Li, Yonghua Yu, Weicheng Wang, Ning Zhang, Meiqiang Xie

{"title":"Gearbox fault diagnosis based on improved multi-scale fluctuation dispersion entropy and multi-cluster feature selection","authors":"Baoyue Li, Yonghua Yu, Weicheng Wang, Ning Zhang, Meiqiang Xie","doi":"10.1177/01423312241267043","DOIUrl":"https://doi.org/10.1177/01423312241267043","url":null,"abstract":"The vibration signal of a gearbox contains a large amount of information and can be used for fault diagnosis of gearboxes. In order to efficiently extract fault features from the vibration signals and improve the reliability of fault diagnosis, a gearbox fault diagnosis method based on improved multi-scale fluctuation dispersion entropy (IMFDE) is proposed. The method takes full advantage of sliding coarse-grained processing to alleviate the shortcomings of traditional multi-scale entropy methods and improve the stability of multi-scale fluctuating dispersion entropy (MFDE). The multi-cluster feature selection (MCFS) method is then combined with the selection of low-dimensional sensitive features from the original multi-scale features, and the sensitive feature matrix is input to a random forest (RF) classifier to mine the complex mapping relationship between the input features and the fault type to achieve fault diagnosis of gearboxes. Finally, experimental data of two gearboxes are used to verify the reliability of the proposed method. The results show that the proposed method can accurately determine different fault types of gearboxes and has significant advantages in terms of reliability and stability of fault identification compared with other existing methods.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"14 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal bounded policy for nonlinear tracking control of unknown constrained-input systems 未知约束输入系统非线性跟踪控制的最优约束策略

Transactions of the Institute of Measurement and Control Pub Date : 2024-06-07 DOI: 10.1177/01423312241254590

F. Sabahi

{"title":"Optimal bounded policy for nonlinear tracking control of unknown constrained-input systems","authors":"F. Sabahi","doi":"10.1177/01423312241254590","DOIUrl":"https://doi.org/10.1177/01423312241254590","url":null,"abstract":"This paper introduces a novel algorithm that seamlessly integrates type-2 fuzzy systems with a sliding mode controller, aiming to create an optimal bounded control policy for tracking nonlinear problems that are plagued with uncertain or incomplete system dynamics and control input constraints. Proving its efficacy in navigating uncertainties, the proposed approach maintains effectiveness even when such encounters are sporadic or infrequent. The algorithm tactically employs three type-2 fuzzy systems. Among these, the actor and critic fuzzy systems are specifically tasked to resolve the optimal control tracking problem, while the third fuzzy system is designated to approximate the system’s unknown dynamics. The sliding mode controller’s role is instrumental in this setup. It dynamically adjusts to ensure the system’s convergence, enabling precise tracking of the desired trajectory, undeterred by the prevalent uncertainties. We validate the stability of the entire amalgamation, consisting of the actor, critic, identifier and controller. The robustness and efficiency of this innovative method are confirmed through rigorous simulation testing on a nonlinear system. Our results substantiate that the proposed solution excels in optimal tracking control, particularly in situations where system dynamics are uncertain or incomplete and where control input constraints are a critical factor.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive PID controller using deep deterministic policy gradient for a 6D hyperchaotic system 针对 6D 超混沌系统使用深度确定性策略梯度的自适应 PID 控制器

Transactions of the Institute of Measurement and Control Pub Date : 2024-06-07 DOI: 10.1177/01423312241253639

Mohammad Ali Labbaf Khaniki, Amirhossein Samii, Mahsan Tavakoli‐Kakhki

{"title":"Adaptive PID controller using deep deterministic policy gradient for a 6D hyperchaotic system","authors":"Mohammad Ali Labbaf Khaniki, Amirhossein Samii, Mahsan Tavakoli‐Kakhki","doi":"10.1177/01423312241253639","DOIUrl":"https://doi.org/10.1177/01423312241253639","url":null,"abstract":"This article introduces a method for the adaptive control of a six-dimensional (6D) hyperchaotic system using a multi-input multi-output (MIMO) approach, leveraging the deep deterministic policy gradient (DDPG) algorithm. The states and tracking errors of the hyperchaotic system are amalgamated to form an input image signal. This signal is then processed by a deep convolutional neural network (CNN) to extract profound features. Subsequently, the DDPG determines the coefficients of the proportional–integral–derivative (PID) controller based on the features discerned from the CNN. The proposed approach exhibits robustness to uncertainties and varying initial conditions, attributed to the DDPG’s ability to learn from the input image signal and adaptively adjust control policies and PID coefficients. The results demonstrate that the proposed adaptive PID controller, integrated with DDPG and CNN, surpasses conventional controllers in terms of synchronization accuracy and response speed. The paper presents the following: a 6D hyperchaotic system’s dynamic model, a CNN-based DDPG’s structure, and how it performs and compares to traditional methods. Then, it summarizes the main findings.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-representation transfer adversarial network for intelligent fault diagnosis of rotating machinery 用于旋转机械智能故障诊断的多表示转移对抗网络

Transactions of the Institute of Measurement and Control Pub Date : 2024-03-20 DOI: 10.1177/01423312241234000

Hongfei Zhang, D. She, Hu Wang, Yaoming Li, Jin Chen

引用次数: 0

A new fault component selection strategy based on statistical detection for slewing bearing weak signal de-noising 基于统计检测的回转支承弱信号去噪新故障成分选择策略

Transactions of the Institute of Measurement and Control Pub Date : 2024-03-20 DOI: 10.1177/01423312241234409

Yubin Pan, Hua Wang, Jie Chen, R. Hong

{"title":"A new fault component selection strategy based on statistical detection for slewing bearing weak signal de-noising","authors":"Yubin Pan, Hua Wang, Jie Chen, R. Hong","doi":"10.1177/01423312241234409","DOIUrl":"https://doi.org/10.1177/01423312241234409","url":null,"abstract":"Slewing bearing is a critical transmission component in large-size construction machinery due to its low-speed and heavy-load conditions. Fault prognostics and health management of slewing bearing are crucial for ensuring their high availability and profitable operation. However, the presence of background noise in construction machinery signals restricts the applicability of existing signal processing approaches in prognostics and health management. To address this challenge, a novel signal de-noising method is proposed based on adaptive decomposition, along with a new strategy for recognizing fault components using statistic detection through kernel principal component analysis (KPCA). First, robust local mean decomposition is utilized to adaptively decompose the fault and normal vibration signal over the entire service life. Then, product functions (PFs) decomposed by fault and normal vibration signal are used for KPCA anomaly detection. Finally, the fault PFs are reconstructed to obtain the de-noised signal. The effectiveness of the proposed method is validated through the use of both simulated and experimental vibration signals obtained from a slewing-bearing life-cycle test. The results illustrate that the proposed method has superior de-noising capability and decomposition efficiency, making it an effective signal preprocessing technique for prognostics and health management.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"9 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140225397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on adaptive practical prescribed-time consensus of multiple mechanical systems with full-state constraints 具有全状态约束的多机械系统的自适应实用规定时间共识研究

Transactions of the Institute of Measurement and Control Pub Date : 2024-03-17 DOI: 10.1177/01423312241233822

Shaoqi Xu, Mingjie Cai, Baofang Wang

引用次数: 0

Improved data-driven high-order model-free adaptive iterative learning control with fast convergence for trajectory tracking systems 针对轨迹跟踪系统的改进型数据驱动高阶无模型自适应迭代学习控制，可实现快速收敛

Transactions of the Institute of Measurement and Control Pub Date : 2024-03-17 DOI: 10.1177/01423312241235105

Liangpei Huang, Hua Huang

{"title":"Improved data-driven high-order model-free adaptive iterative learning control with fast convergence for trajectory tracking systems","authors":"Liangpei Huang, Hua Huang","doi":"10.1177/01423312241235105","DOIUrl":"https://doi.org/10.1177/01423312241235105","url":null,"abstract":"The data-driven high-order pseudo-partial derivative-based model-free adaptive iterative learning control (HOPPD-MFAILC) is always slow to converge and difficult to have excellent tracking results. To address the problem, an improved high-order pseudo-partial derivative-based model-free adaptive iterative learning control (iHOPPD-MFAILC) with fast convergence is proposed. First, to reduce the impact of the initial value of the pseudo-partial derivative (PPD) on the convergence speed of the algorithm, the initial PPD is corrected by introducing the high-order model estimation error. Second, to reduce the influence of system noise on the control performance, the original HOPPD-MFAILC control law is improved by introducing time-varying iterative proportional and time-varying iterative integral terms. Then, the convergence of the proposed improved control algorithm is demonstrated by theoretical analysis. Finally, simulations and experiments on the ball screw motion system show that the proposed iHOPPD-MFAILC can track the desired trajectory better. In addition, iHOPPD-MFAILC has better robustness in the noisy environment and achieves better convergence as well as trajectory tracking performance under different initial PPD conditions. The proposed control scheme has excellent application potential in precision motion control.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"189 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140235716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0