ISA transactions最新文献

英文中文

Wasserstein distance-enhanced variable edge-weight graph convolutional networks for industrial fault diagnosis 用于工业故障诊断的Wasserstein距离增强变边权图卷积网络。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-12-01 DOI: 10.1016/j.isatra.2025.07.046

Zi-Yang Lu , Qun-Xiong Zhu , Yan-Lin He

Modern industrial systems generate large volumes of high-dimensional and correlated sensor data, making the fault diagnosis increasingly challenging. Traditional methods often struggle to handle such non-Euclidean and nonlinear structures, and they typically fail to exploit the intrinsic topological or relational information embedded in the data. These limitations hinder their effectiveness in capturing complex inter-variable dependencies, which are critical for accurate fault identification. In contrast, Graph Neural Networks (GNNs) offer a promising framework to model such structural information. However, many existing GNN-based models overlook temporal correlations or suffer from high computational costs due to fully data-driven graph construction. To address these challenges, we propose a Wasserstein Distance Variable Edge-Weight Graph Convolutional Network (WVEGCN). This method integrates a mechanism-informed adjacency matrix specific to chemical processes and introduces adaptive edge-weight coefficients to improve robustness. We also design a feature extraction method based on Wasserstein distance to distinguish fault types more effectively and apply a novel feature selection strategy to enhance representation. A random forest classifier is used to improve stability in the final diagnosis. Experiments on two benchmark datasets (TE and TFF) demonstrate that our method significantly outperforms existing approaches in both accuracy and robustness, showing strong potential for real-world fault diagnosis applications.

现代工业系统产生大量高维、相关的传感器数据，使得故障诊断越来越具有挑战性。传统的方法往往难以处理这种非欧几里得和非线性结构，而且它们通常无法利用嵌入在数据中的固有拓扑或关系信息。这些限制阻碍了它们捕获复杂变量间依赖关系的有效性，而这对于准确的故障识别至关重要。相比之下，图神经网络（gnn）提供了一个有前途的框架来模拟这种结构信息。然而，许多现有的基于gnn的模型忽略了时间相关性，或者由于完全数据驱动的图构建而导致计算成本高。为了解决这些挑战，我们提出了一种Wasserstein距离变边权图卷积网络（WVEGCN）。该方法集成了特定于化学过程的机制通知邻接矩阵，并引入自适应边权系数来提高鲁棒性。设计了一种基于Wasserstein距离的特征提取方法来更有效地区分故障类型，并采用了一种新的特征选择策略来增强表征。随机森林分类器用于提高最终诊断的稳定性。在两个基准数据集（TE和TFF）上的实验表明，我们的方法在准确性和鲁棒性方面都明显优于现有方法，显示出强大的实际故障诊断应用潜力。

{"title":"Wasserstein distance-enhanced variable edge-weight graph convolutional networks for industrial fault diagnosis","authors":"Zi-Yang Lu , Qun-Xiong Zhu , Yan-Lin He","doi":"10.1016/j.isatra.2025.07.046","DOIUrl":"10.1016/j.isatra.2025.07.046","url":null,"abstract":"<div><div>Modern industrial systems generate large volumes of high-dimensional and correlated sensor data, making the fault diagnosis increasingly challenging. Traditional methods often struggle to handle such non-Euclidean and nonlinear structures, and they typically fail to exploit the intrinsic topological or relational information embedded in the data. These limitations hinder their effectiveness in capturing complex inter-variable dependencies, which are critical for accurate fault identification. In contrast, Graph Neural Networks (GNNs) offer a promising framework to model such structural information. However, many existing GNN-based models overlook temporal correlations or suffer from high computational costs due to fully data-driven graph construction. To address these challenges, we propose a Wasserstein Distance Variable Edge-Weight Graph Convolutional Network (WVEGCN). This method integrates a mechanism-informed adjacency matrix specific to chemical processes and introduces adaptive edge-weight coefficients to improve robustness. We also design a feature extraction method based on Wasserstein distance to distinguish fault types more effectively and apply a novel feature selection strategy to enhance representation. A random forest classifier is used to improve stability in the final diagnosis. Experiments on two benchmark datasets (TE and TFF) demonstrate that our method significantly outperforms existing approaches in both accuracy and robustness, showing strong potential for real-world fault diagnosis applications.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"167 ","pages":"Pages 78-89"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Fixed-time optimal time-varying formation control for unmanned surface vehicle systems based on reinforcement learning 基于强化学习的无人水面车辆系统定时最优时变编队控制。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-12-01 DOI: 10.1016/j.isatra.2025.07.057

Qiaokun Kang, Qintao Gan, Ruihong Li, Luke Li, Guoquan Ren

This article proposes the distributed fixed-time optimal time-varying formation control (TVFC) strategy based on reinforcement learning (RL) for unmanned surface vehicle systems (USVSs) with partially unmeasurable states and unknown dynamics. The fixed-time adaptive neural network state observer (FANNSO) is introduced for reconstructing unknown dynamics and unmeasurable states of the system. On this basis, a distributed optimization performance index function containing exponential terms is proposed, and a distributed fixed-time optimal TVFC strategy is developed by combining the actor-critic structure. This strategy achieves the dual objectives of formation control and cost optimization by adaptively adjusting the controller through the RL algorithm. Theoretical analyses show that the proposed control strategy can make the error signals bounded within a fixed time. Simulation results demonstrate the effectiveness and superiority of the method.

针对部分状态不可测和动态未知的无人水面车辆系统，提出了一种基于强化学习的分布式固定时间最优时变编队控制策略。引入定时自适应神经网络状态观测器（FANNSO）对系统的未知动态和不可测状态进行重构。在此基础上，提出了包含指数项的分布式优化性能指标函数，并结合演员-评论家结构提出了分布式固定时间最优TVFC策略。该策略通过RL算法自适应调整控制器，实现了编队控制和成本优化的双重目标。理论分析表明，所提出的控制策略能使误差信号在固定时间内有界。仿真结果验证了该方法的有效性和优越性。

引用次数: 0

Optimal task-priority projection for behavioral control of networked nonholonomic Mobile robots 网络非完整移动机器人行为控制的最优任务优先级投影。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-12-01 DOI: 10.1016/j.isatra.2025.08.003

Zhenyi Zhang , Shuzong Xie , Zhibin Mo , Jiahao Dai

This paper addresses the task-priority projection problem within the Null-Space-based Behavioral Control (NSBC) framework. We propose a Reliable Intelligent Task Supervisor (RITS) for dynamic task-priority projection in networked Nonholonomic Mobile Robots (NMRs). Firstly, the NSBC task paradigm is extended to accommodate the NMRs by integrating nonholonomic constraints into both task functions and the projection operator. Subsequently, a Multi-agent Learning Task Supervisor (MLTS) is developed to allocate task priorities through environment interactions. The MLTS formulates an optimization problem for task-priority decision-making. The optimization variables are the task priorities for null-space projections, while the optimization constraints are the nonholonomic constraints. Furthermore, a Model Predictive Control Redundancy Replanner (MPCRR) is developed to enhance the reliability of the MLTS. The proposed RITS is the integration of the MLTS and the MPCRR. Simulations show that, compared to the finite state automata task supervisor, the RITS reduces average switching by approximately 36 times; compared to the model predictive control task supervisor, it reduces average iteration time by approximately 26 s. The RITS has been successfully implemented in JetAuto robots.

本文研究了基于空空间的行为控制框架下的任务优先级投影问题。针对网络非完整移动机器人（NMRs）的动态任务优先级投影问题，提出了一种可靠智能任务监督器（RITS）。首先，通过将非完整约束整合到任务函数和投影算子中，扩展NSBC任务范式以适应非完整约束。随后，开发了多智能体学习任务监督器（MLTS），通过环境交互来分配任务优先级。MLTS提出了一个任务优先级决策的优化问题。优化变量为零空间投影的任务优先级，优化约束为非完整约束。在此基础上，提出了一种模型预测控制冗余重规划器（MPCRR），以提高MLTS的可靠性。拟议的RITS是MLTS和MPCRR的整合。仿真结果表明，与有限状态自动机任务监督器相比，RITS的平均切换次数减少了约36倍；与模型预测控制任务监督器相比，它将平均迭代时间缩短了约26 s。RITS已成功应用于JetAuto机器人。

{"title":"Optimal task-priority projection for behavioral control of networked nonholonomic Mobile robots","authors":"Zhenyi Zhang , Shuzong Xie , Zhibin Mo , Jiahao Dai","doi":"10.1016/j.isatra.2025.08.003","DOIUrl":"10.1016/j.isatra.2025.08.003","url":null,"abstract":"<div><div>This paper addresses the task-priority projection problem within the Null-Space-based Behavioral Control (NSBC) framework. We propose a Reliable Intelligent Task Supervisor (RITS) for dynamic task-priority projection in networked Nonholonomic Mobile Robots (NMRs). Firstly, the NSBC task paradigm is extended to accommodate the NMRs by integrating nonholonomic constraints into both task functions and the projection operator. Subsequently, a Multi-agent Learning Task Supervisor (MLTS) is developed to allocate task priorities through environment interactions. The MLTS formulates an optimization problem for task-priority decision-making. The optimization variables are the task priorities for null-space projections, while the optimization constraints are the nonholonomic constraints. Furthermore, a Model Predictive Control Redundancy Replanner (MPCRR) is developed to enhance the reliability of the MLTS. The proposed RITS is the integration of the MLTS and the MPCRR. Simulations show that, compared to the finite state automata task supervisor, the RITS reduces average switching by approximately 36 times; compared to the model predictive control task supervisor, it reduces average iteration time by approximately 26 s. The RITS has been successfully implemented in JetAuto robots.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"167 ","pages":"Pages 914-925"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 novel PT-SMC based resilient control of cyber-physical robotic system under malicious-threats 恶意威胁下基于PT-SMC的网络物理机器人系统弹性控制。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-12-01 DOI: 10.1016/j.isatra.2025.07.063

Yun-Peng DING , Chun-Wu YIN , Saleem Riaz

To address the problem of predefined time (PT) guaranteed performance control for uncertain robotic cyber physical systems (CPSs) under multiple cyber-attacks, this paper proposes a sliding-mode (SM) resilient control strategy with predefined time convergence (PTC) and guaranteed performance. This strategy aims to enhance the resilience of CPSs against diverse cyber-attacks (e.g., denial-of-service (DoS) and false data injection (FDI)) and improve trajectory tracking precision. First, the impact mechanisms of common cyber-attacks on CPS control signals are analyzed, and these attacks are categorized into multiplicative and additive types with respect to the control signals. Subsequently, an initial value Error Conversion Function (ECF) is designed to map arbitrary initial tracking errors into a prescribed neighborhood, thereby addressing the limitation of traditional prescribed performance control (PPC) strategies where parameter design is dependent on initial error magnitudes. Finally, for robotic CPSs under malicious cyber-attacks, an improved extreme learning machine (ELM) is employed to approximate the time-varying lumped component comprising uncertainties and cyber-attacks. By integrating the PTC sliding mode surface (SMS) and an enhanced PPC strategy, an initial-state-independent prescribed performance SM resilient control strategy is designed for robotic CPSs, which ensures control performance regardless of initial conditions. Theoretical analysis confirms the PTC of the closed-loop system. Numerical simulation results demonstrate that, under varying attack frequencies and amplitudes, the trajectory tracking errors (TTE) of the manipulator across all initial states converge within the specified time frame in accordance with the prescribed performance, achieving a tracking precision of 0.00013 rad. These findings validate the proposed algorithm's strong robustness against cyber-attacks and its practical applicability in engineering scenarios.

针对不确定机器人网络物理系统（cps）在多重网络攻击下的预定义时间保证性能控制问题，提出了一种具有预定义时间收敛（PTC）和性能保证的滑模弹性控制策略。该策略旨在增强cps抵御各种网络攻击（例如，拒绝服务（DoS）和虚假数据注入（FDI））的弹性，并提高轨迹跟踪精度。首先，分析了常见网络攻击对CPS控制信号的影响机制，并根据控制信号将这些攻击分为乘法型和加性型。随后，设计了一个初始值误差转换函数（ECF），将任意初始跟踪误差映射到规定的邻域，从而解决了传统的规定性能控制（PPC）策略中参数设计依赖于初始误差大小的局限性。最后，针对恶意网络攻击下的机器人cps，采用改进的极限学习机（ELM）逼近包含不确定性和网络攻击的时变集总分量。通过将PTC滑模面（SMS）与一种增强的PPC策略相结合，设计了一种初始状态无关的规定性能SM弹性控制策略，使机器人cps无论初始条件如何都能保证控制性能。理论分析证实了闭环系统的PTC。数值仿真结果表明，在不同攻击频率和幅度下，机械臂各初始状态的轨迹跟踪误差（TTE）在规定的时间范围内按照规定的性能收敛，跟踪精度为0.00013 rad。这些结果验证了该算法对网络攻击的强鲁棒性及其在工程场景中的实际适用性。

{"title":"A novel PT-SMC based resilient control of cyber-physical robotic system under malicious-threats","authors":"Yun-Peng DING , Chun-Wu YIN , Saleem Riaz","doi":"10.1016/j.isatra.2025.07.063","DOIUrl":"10.1016/j.isatra.2025.07.063","url":null,"abstract":"<div><div>To address the problem of predefined time (PT) guaranteed performance control for uncertain robotic cyber physical systems (CPSs) under multiple cyber-attacks, this paper proposes a sliding-mode (SM) resilient control strategy with predefined time convergence (PTC) and guaranteed performance. This strategy aims to enhance the resilience of CPSs against diverse cyber-attacks (e.g., denial-of-service (DoS) and false data injection (FDI)) and improve trajectory tracking precision. First, the impact mechanisms of common cyber-attacks on CPS control signals are analyzed, and these attacks are categorized into multiplicative and additive types with respect to the control signals. Subsequently, an initial value Error Conversion Function (ECF) is designed to map arbitrary initial tracking errors into a prescribed neighborhood, thereby addressing the limitation of traditional prescribed performance control (PPC) strategies where parameter design is dependent on initial error magnitudes. Finally, for robotic CPSs under malicious cyber-attacks, an improved extreme learning machine (ELM) is employed to approximate the time-varying lumped component comprising uncertainties and cyber-attacks. By integrating the PTC sliding mode surface (SMS) and an enhanced PPC strategy, an initial-state-independent prescribed performance SM resilient control strategy is designed for robotic CPSs, which ensures control performance regardless of initial conditions. Theoretical analysis confirms the PTC of the closed-loop system. Numerical simulation results demonstrate that, under varying attack frequencies and amplitudes, the trajectory tracking errors (TTE) of the manipulator across all initial states converge within the specified time frame in accordance with the prescribed performance, achieving a tracking precision of 0.00013 rad. These findings validate the proposed algorithm's strong robustness against cyber-attacks and its practical applicability in engineering scenarios.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"167 ","pages":"Pages 256-267"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144796525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Ridge-based general synchrosqueezing transform for flexible thin-wall bearing fault diagnosis 基于脊的柔性薄壁轴承通用同步压缩变换故障诊断。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-12-01 DOI: 10.1016/j.isatra.2025.08.040

Yanjiang Yu, Xuezhi Zhao

Accurate fault diagnosis in flexible thin-wall bearings is crucial for harmonic drive reliability but remains challenging, as fault impulses are often masked by strong operational vibrations. In response to this challenge, a ridge-based general synchrosqueezing transform (RGST) is proposed in this paper. This method unifies time-frequency analysis by operating at the ridge level, using energy trajectories extracted from both instantaneous frequency and group delay estimators. Key features of RGST include a binary ridge expansion mask to enhance energy concentration and suppress noise, and an agglomerative clustering algorithm to separate signal components. Experimental results demonstrate that RGST achieves a concentrated time-frequency representation with superior component separation and noise robustness, thereby improving the reliability of fault diagnosis under multiple fault conditions in flexible thin-wall bearings.

柔性薄壁轴承的准确故障诊断对于谐波驱动的可靠性至关重要，但由于故障脉冲通常被强烈的运行振动所掩盖，因此仍然具有挑战性。针对这一挑战，本文提出了一种基于脊线的通用同步压缩变换（RGST）。该方法将时频分析统一起来，在脊水平上操作，使用从瞬时频率和群延迟估计中提取的能量轨迹。RGST的主要特点包括一个二值脊扩展掩模，增强能量集中和抑制噪声，以及一个聚类算法，分离信号成分。实验结果表明，RGST实现了集中的时频表示，具有较好的分量分离性和噪声鲁棒性，从而提高了柔性薄壁轴承多故障条件下故障诊断的可靠性。

引用次数: 0

Predicting wear damage in moving mechanical contacts: Comparative analysis of regression algorithms and feature selection techniques 预测运动机械接触的磨损损伤：回归算法和特征选择技术的比较分析。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-12-01 DOI: 10.1016/j.isatra.2025.08.027

Jianjie Jiang, Intisar Omar, Muhammad Khan

+Accurate wear prediction is essential for industries such as manufacturing, transportation, and power generation, as it helps reduce operational risks, minimise downtime, and extend the lifespan of critical components. This study presents a machine learning-based predictive model for estimating wear volume in pin-on-disc systems. The methodology comprises four key stages: feature selection, sample size determination, regression model selection, and model evaluation. The experimental data include parameters such as friction coefficient, tangential force, penetration depth, sliding distance, sound pressure, and load. Feature selection is employed to identify the most relevant parameters for wear prediction, utilising two methods —wrapping and embedding —to refine the feature subset and enhance accuracy. To optimise model performance, the sample size is determined to balance underfitting and overfitting. Initially, linear regression is applied, followed by adjustments to the sample size. Where necessary, more complex algorithms, such as support vector machines (SVMs) and random forests (RFs), are explored to enhance accuracy. Model evaluation employs metrics including mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), and the coefficient of determination (R²) to assess predictive performance. This research offers a systematic approach to wear volume estimation and presents a comparative analysis of regression algorithms, providing valuable insights for researchers and practitioners in wear prediction applications.

+准确的磨损预测对于制造，运输和发电等行业至关重要，因为它有助于降低操作风险，最大限度地减少停机时间，并延长关键部件的使用寿命。本研究提出了一种基于机器学习的预测模型，用于估计销盘式系统的磨损量。该方法包括四个关键阶段：特征选择、样本量确定、回归模型选择和模型评估。实验数据包括摩擦系数、切向力、穿透深度、滑动距离、声压、载荷等参数。特征选择用于识别最相关的磨损预测参数，利用两种方法-包裹和嵌入-细化特征子集并提高准确性。为了优化模型性能，确定样本量以平衡欠拟合和过拟合。首先，应用线性回归，然后调整样本量。在必要的地方，更复杂的算法，如支持向量机（svm）和随机森林（RFs），探索以提高准确性。模型评估采用平均绝对误差（MAE）、平均偏倚误差（MBE）、均方根误差（RMSE）和决定系数（R²）等指标来评估预测性能。本研究提供了一种系统的磨损量估计方法，并对回归算法进行了比较分析，为磨损预测应用的研究人员和实践者提供了有价值的见解。

{"title":"Predicting wear damage in moving mechanical contacts: Comparative analysis of regression algorithms and feature selection techniques","authors":"Jianjie Jiang, Intisar Omar, Muhammad Khan","doi":"10.1016/j.isatra.2025.08.027","DOIUrl":"10.1016/j.isatra.2025.08.027","url":null,"abstract":"<div><div>+Accurate wear prediction is essential for industries such as manufacturing, transportation, and power generation, as it helps reduce operational risks, minimise downtime, and extend the lifespan of critical components. This study presents a machine learning-based predictive model for estimating wear volume in pin-on-disc systems. The methodology comprises four key stages: feature selection, sample size determination, regression model selection, and model evaluation. The experimental data include parameters such as friction coefficient, tangential force, penetration depth, sliding distance, sound pressure, and load. Feature selection is employed to identify the most relevant parameters for wear prediction, utilising two methods —wrapping and embedding —to refine the feature subset and enhance accuracy. To optimise model performance, the sample size is determined to balance underfitting and overfitting. Initially, linear regression is applied, followed by adjustments to the sample size. Where necessary, more complex algorithms, such as support vector machines (SVMs) and random forests (RFs), are explored to enhance accuracy. Model evaluation employs metrics including mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE), and the coefficient of determination (R²) to assess predictive performance. This research offers a systematic approach to wear volume estimation and presents a comparative analysis of regression algorithms, providing valuable insights for researchers and practitioners in wear prediction applications.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"167 ","pages":"Pages 675-687"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Anti-disturbance motion control of servo motors: An adaptive sliding-mode approach with disturbance observer compensation 伺服电机的抗扰动运动控制：一种具有扰动观测器补偿的自适应滑模方法。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-12-01 DOI: 10.1016/j.isatra.2025.08.008

Rui Xu , Jinsong Zhou , Zhongshi Wang , Lei Shi , Dapeng Tian

This research presents a novel adaptive sliding-mode disturbance observer (NASMDO) with an adaptive non-singular terminal sliding-mode control (ANTSMC) method to solve the problem of time-varying disturbances and achieve high-accuracy control for servomotor systems. Firstly, an adaptive disturbance observer is designed using motor velocity information. Secondly, a sliding-mode-assisted term is designed with position information to estimate the residual disturbance observation error. The convergence performance is rigorously analyzed and demonstrated for NASMDO. Following this, a composite control method is formulated combined with ANTSMC to achieve high-accuracy control of the servomotor system with time-varying disturbances, which ensures the reaching towards a vicinity of the sliding-mode manifold in finite time. Lastly, realistic validations are carried out on a servomotor turntable device with a braking mechanism. The established composite anti-disturbance tracking control method exhibits superior robustness and performance. The tracking error of proposed method decreases by over 35 % on average compared to traditional methods under various disturbance conditions.

为了解决伺服系统的时变扰动问题，实现高精度控制，提出了一种新的自适应滑模扰动观测器（NASMDO）和自适应非奇异末端滑模控制（ANTSMC）方法。首先，利用电机速度信息设计自适应扰动观测器。其次，利用位置信息设计滑模辅助项来估计剩余扰动观测误差；对该算法的收敛性能进行了严格的分析和论证。在此基础上，提出了一种与ANTSMC相结合的复合控制方法，实现了对具有时变扰动的伺服电机系统的高精度控制，保证了系统在有限时间内逼近滑模流形附近。最后，对带有制动机构的伺服电机转台装置进行了实际验证。所建立的复合抗扰动跟踪控制方法具有良好的鲁棒性和性能。在各种干扰条件下，与传统方法相比，该方法的跟踪误差平均降低了35% %以上。

{"title":"Anti-disturbance motion control of servo motors: An adaptive sliding-mode approach with disturbance observer compensation","authors":"Rui Xu , Jinsong Zhou , Zhongshi Wang , Lei Shi , Dapeng Tian","doi":"10.1016/j.isatra.2025.08.008","DOIUrl":"10.1016/j.isatra.2025.08.008","url":null,"abstract":"<div><div>This research presents a novel adaptive sliding-mode disturbance observer (NASMDO) with an adaptive non-singular terminal sliding-mode control (ANTSMC) method to solve the problem of time-varying disturbances and achieve high-accuracy control for servomotor systems. Firstly, an adaptive disturbance observer is designed using motor velocity information. Secondly, a sliding-mode-assisted term is designed with position information to estimate the residual disturbance observation error. The convergence performance is rigorously analyzed and demonstrated for NASMDO. Following this, a composite control method is formulated combined with ANTSMC to achieve high-accuracy control of the servomotor system with time-varying disturbances, which ensures the reaching towards a vicinity of the sliding-mode manifold in finite time. Lastly, realistic validations are carried out on a servomotor turntable device with a braking mechanism. The established composite anti-disturbance tracking control method exhibits superior robustness and performance. The tracking error of proposed method decreases by over 35 % on average compared to traditional methods under various disturbance conditions.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"167 ","pages":"Pages 724-734"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Tracking control of surface vessel systems with disturbances and deferred full state constraints 具有扰动和延迟全状态约束的水面舰船系统跟踪控制。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-12-01 DOI: 10.1016/j.isatra.2025.08.013

Liqiang Yao, Shaojun Xu, Mingyue Cui, Hui Shang

This paper studies the tracking control of surface vessel systems with unknown disturbances and deferred full state constraints, employing a continuous controller and a dynamic event-triggered controller, respectively. To ensure the tracking performance while accommodating deferred full state constraints, a continuous controller with a nonnegative time-varying gain is constructed. By adjusting the value of nonnegative time-varying gain (i.e.,

κ \geq 0

), both the asymptotic tracking performance (

κ > 0

) and the practical tracking performance (

κ = 0

) can be realized. Compared with existing studies on state constraints, the proposed controller in this paper guarantees the stability of the closed-loop system. On that basis, a dynamic event-triggered mechanism is further developed to decrease the number of controller updates without compromising system control performance. Compared with the existing dynamic event-triggered mechanisms, the proposed dynamic event-triggered mechanism removes a certain stringent restriction. Illustrative results exhibit the feasibility and effectiveness of the presented control schemes in this paper.

本文分别采用连续控制器和动态事件触发控制器，研究了具有未知扰动和延迟全状态约束的水面舰船系统的跟踪控制问题。为了在适应延迟全状态约束的同时保证跟踪性能，构造了非负时变增益的连续控制器。通过调整非负时变增益（即κ≥0）的值，既可以实现渐近跟踪性能（κ>0），又可以实现实际跟踪性能（κ=0）。与已有的状态约束研究相比，本文所提出的控制器保证了闭环系统的稳定性。在此基础上，进一步开发了动态事件触发机制，在不影响系统控制性能的情况下减少控制器更新次数。与现有的动态事件触发机制相比，本文提出的动态事件触发机制消除了一定的严格限制。算例结果表明了本文提出的控制方案的可行性和有效性。

{"title":"Tracking control of surface vessel systems with disturbances and deferred full state constraints","authors":"Liqiang Yao, Shaojun Xu, Mingyue Cui, Hui Shang","doi":"10.1016/j.isatra.2025.08.013","DOIUrl":"10.1016/j.isatra.2025.08.013","url":null,"abstract":"<div><div>This paper studies the tracking control of surface vessel systems with unknown disturbances and deferred full state constraints, employing a continuous controller and a dynamic event-triggered controller, respectively. To ensure the tracking performance while accommodating deferred full state constraints, a continuous controller with a nonnegative time-varying gain is constructed. By adjusting the value of nonnegative time-varying gain (i.e., <span><math><mi>κ</mi><mo>≥</mo><mn>0</mn></math></span>), both the asymptotic tracking performance (<span><math><mi>κ</mi><mo>></mo><mn>0</mn></math></span>) and the practical tracking performance (<span><math><mi>κ</mi><mo>=</mo><mn>0</mn></math></span>) can be realized. Compared with existing studies on state constraints, the proposed controller in this paper guarantees the stability of the closed-loop system. On that basis, a dynamic event-triggered mechanism is further developed to decrease the number of controller updates without compromising system control performance. Compared with the existing dynamic event-triggered mechanisms, the proposed dynamic event-triggered mechanism removes a certain stringent restriction. Illustrative results exhibit the feasibility and effectiveness of the presented control schemes in this paper.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"167 ","pages":"Pages 453-467"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Optimizing trajectory tracking control for hypersonic flight vehicles via ADDHP 基于ADDHP的高超声速飞行器轨迹跟踪控制优化。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-12-01 DOI: 10.1016/j.isatra.2025.08.017

Ziyang Zhou , Xiaohui Liang , Bin Xu

This article proposes an intelligent hybrid control strategy for hypersonic flight vehicles (HFVs) that integrates sliding mode control (SMC) with actor-dependent dual heuristic programming (ADDHP) to address trajectory tracking challenges. An SMC baseline controller is first developed to ensure stable tracking with model uncertainties. Additionally, a novel angle of attack (AOA) protection mechanism is designed, which maintains the AOA within constraint boundaries by generating smooth modifying signals. Furthermore, multiple ADDHP-based optimal compensators are then implemented in the velocity and altitude subsystems. These model-free compensators dynamically optimize control performance through error-driven learning, significantly improving tracking accuracy and adaptability in complex environments. Lyapunov stability analysis proves the convergence of both SMC and ADDHP. The effectiveness and superiority of the proposed strategy are validated through comparative simulations.

本文提出了一种将滑模控制（SMC）与参与者相关的双启发式规划（ADDHP）相结合的高超声速飞行器（hfv）智能混合控制策略，以解决轨迹跟踪问题。为了保证在模型不确定情况下的稳定跟踪，首先设计了一种SMC基准控制器。此外，设计了一种新的攻角保护机制，通过产生平滑的修正信号使攻角保持在约束边界内。然后，在速度和高度子系统中实现了多个基于addhp的最优补偿器。这些无模型补偿器通过错误驱动学习动态优化控制性能，显著提高了复杂环境下的跟踪精度和适应性。Lyapunov稳定性分析证明了SMC和ADDHP的收敛性。通过对比仿真验证了该策略的有效性和优越性。

引用次数: 0

Aerodynamic parameter identification method based on physics-informed radial basis function-deep neural networks 基于物理信息径向基函数-深度神经网络的气动参数识别方法。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions

Pub Date : 2025-12-01 DOI: 10.1016/j.isatra.2025.08.039

Jungu Chen, Junhui Liu, Jiayuan Shan, Jianan Wang

This paper investigates the perturbations estimation between the real and nominal aerodynamic parameters. To address this issue, this study proposes an aerodynamic parameter identification method based on the physics-informed radial basis function-deep neural network (PIRBF-DNN). PIRBF-DNN utilizes an integration-based loss function to achieve precise estimation of aerodynamic parameters perturbations and adopts a radial basis function-deep neural network (RBF-DNN) structure to enhance fitting capability of the network. The proposed identification method is validated through simulation in different scenarios and comparison with other aerodynamic parameters identification methods based on physics-informed neural networks (PINNs).

本文研究了实际气动参数与标称气动参数之间的摄动估计。为了解决这一问题，本研究提出了一种基于物理信息径向基函数-深度神经网络（PIRBF-DNN）的气动参数识别方法。PIRBF-DNN利用基于积分的损失函数实现对气动参数扰动的精确估计，并采用径向基函数-深度神经网络（RBF-DNN）结构增强网络的拟合能力。通过不同场景下的仿真，并与其他基于物理信息神经网络（pinn）的气动参数识别方法进行了比较，验证了所提辨识方法的有效性。

引用次数: 0

首页上一页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

ISA transactions

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀