ISA transactions最新文献_第3页

Iteration-free implicit Euler realization of integral sliding mode control for integrator-chain systems. 积分链系统积分滑模控制的无迭代隐式欧拉实现。

IF 6.5

ISA transactions Pub Date : 2026-09-25 DOI: 10.1016/j.isatra.2026.09.031

Shanhai Jin, Dejin Zhao, Xiaodan Wang, Yinhao Cui

引用次数: 0

Health-aware scheduling framework for transport tasks in container terminals. 集装箱码头运输任务的健康感知调度框架。

IF 6.5

ISA transactions Pub Date : 2026-09-23 DOI: 10.1016/j.isatra.2026.09.029

Marcin Mrugalski, Bogdan Lipiec, Paweł Dworak, Ludmiła Filina-Dawidowicz

引用次数: 0

Robust data-induced learning control for uncertain nonlinear systems via dual-timescale adaptation. 基于双时间尺度自适应的不确定非线性系统鲁棒数据诱导学习控制。

IF 6.5

ISA transactions Pub Date : 2026-09-23 DOI: 10.1016/j.isatra.2026.09.030

Changxin Lu, Deyuan Meng, Jingyao Zhang

引用次数: 0

Backstepping actor-critic optimal control for full-pose tracking of tilt-rotor quadcopters. 倾转旋翼四轴飞行器全姿态跟踪的反步actor- critical最优控制。

IF 6.5

ISA transactions Pub Date : 2026-09-19 DOI: 10.1016/j.isatra.2026.09.024

Yuwei Yao, Changshi Xiao, Qiliang Li, Haiwen Yuan

引用次数: 0

Predefined-time decentralized dynamic surface control for interconnected system. 互联系统的预定义时间分散动态面控制。

IF 6.5

ISA transactions Pub Date : 2026-09-19 DOI: 10.1016/j.isatra.2026.09.023

Guoju Zhang, Xiaoli Li, Tongming Huo, Kang Wang

{"title":"Predefined-time decentralized dynamic surface control for interconnected system.","authors":"Guoju Zhang, Xiaoli Li, Tongming Huo, Kang Wang","doi":"10.1016/j.isatra.2026.09.023","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.09.023","url":null,"abstract":"A decentralized dynamic surface control approach with predefined-time convergence guarantees is developed for high-order interconnected systems subject to model uncertainties and external disturbances. This strategy ensures that subsystem tracking errors reach a vicinity of the equilibrium point within a user-defined time interval. Specifically, neural networks approximate the unknown interconnection effects using local subsystem states and the reference states of coupled subsystems. This method removes the conventional requirement that interconnections must satisfy a matching condition and possess a known upper bound. To circumvent the repeated differentiation of virtual control signals inherent in traditional backstepping, a predefined-time nonlinear filter is introduced. This modification not only prevents potential control-input chattering but also establishes an explicit bound on the first derivative of each virtual control signal. Furthermore, leveraging predefined-time stability theory, a compensation mechanism mitigates performance degradation caused by filtering errors during the transient phase. A novel neural network disturbance observer, grounded in the universal approximation principle, is proposed to handle both plant model uncertainties and unknown disturbances concurrently. Numerical simulations and a practical application case study ultimately validate the effectiveness of the presented approach.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149067022","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

Hierarchical steering coordination for reconfigurable modular wheeled platforms via virtual axle motion representation. 基于虚拟轴运动表示的可重构模块化轮式平台分层转向协调。

IF 6.5

ISA transactions Pub Date : 2026-09-18 DOI: 10.1016/j.isatra.2026.09.022

Yicai Liu, Quantong Li, Heng Wei, Linghuan Zheng, Xiangyu Wang, Liang Li

{"title":"Hierarchical steering coordination for reconfigurable modular wheeled platforms via virtual axle motion representation.","authors":"Yicai Liu, Quantong Li, Heng Wei, Linghuan Zheng, Xiangyu Wang, Liang Li","doi":"10.1016/j.isatra.2026.09.022","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.09.022","url":null,"abstract":"Reconfigurable modular wheeled platforms (R-MWPs) enable flexible axle configurations and steering behaviors, making them promising for heavy-duty logistics and warehousing. However, architectural reconfiguration and modular heterogeneity fundamentally challenge accurate and synchronized execution in multi-axle systems. To address this problem, this paper proposes a hierarchical steering coordination framework based on a virtual-axle motion representation. At the platform level, a twist-based interface reformulates discrete steering-mode switching as continuous instantaneous center of rotation (ICR) evolution, enabling feasibility-aware motion iteration under physical angle and rate constraints. At the module level, a generalized topology is derived to characterize both intra- and inter-axle coupling, based on which a predefined-time consensus controller is developed to guarantee synchronized tracking under structural and environmental heterogeneity. At the actuator level, a gated deep deterministic policy gradient (DDPG) compensator is integrated to mitigate structural nonlinearities while preserving closed-loop stability and interpretability. The proposed framework is validated through real-vehicle experiments on 2-, 4-, and 6-wheel platforms, demonstrating that the scheme achieves precise execution, continuous transitions, and scalable coordination.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149043036","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

Data-driven preview Q-learning for H_∞ tracking control of unknown linear discrete-time systems. 未知线性离散系统H∞跟踪控制的数据驱动预览q学习。

IF 6.5

ISA transactions Pub Date : 2026-09-18 DOI: 10.1016/j.isatra.2026.09.025

Yujie Su, Di Wang, Xiaoyu Mi, Yue Sun

{"title":"Data-driven preview Q-learning for H∞ tracking control of unknown linear discrete-time systems.","authors":"Yujie Su, Di Wang, Xiaoyu Mi, Yue Sun","doi":"10.1016/j.isatra.2026.09.025","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.09.025","url":null,"abstract":"Conventional tracking controllers relying only on the current tracking error may suffer from tracking lag when the reference trajectory changes rapidly. To address this problem, a data-driven preview Q-learning algorithm is presented for H∞ tracking control of unknown linear discrete-time systems. The algorithm integrates preview information, enabling the controller to utilize known future reference variations and adjust the control action in advance. By incorporating the tracking error and preview information, an augmented error system is constructed. A corresponding state reconstruction relation is then established, allowing the Q-function to be reformulated using measurable input-output and disturbance data together with preview information. Based on this formulation, the data-driven preview Q-learning algorithm is developed to solve the tracking problem without requiring prior knowledge of the system model, full state information, or an initially admissible policy. Simulation results demonstrate that the proposed method achieves higher tracking accuracy, improved transient tracking performance, and satisfactory robustness under noisy external disturbances.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149043046","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

Multiscale symbolic hellinger spectral entropy: A novel nonlinear metric for feature extraction of mechanical signals. 多尺度符号海灵格谱熵：一种用于机械信号特征提取的非线性度量。

IF 6.5

ISA transactions Pub Date : 2026-09-18 DOI: 10.1016/j.isatra.2026.09.026

Yi Du, Zong Meng, Shaochuan Zhang, Yan Wang, Fengjie Fan, Jimeng Li, Guangbin Wang

{"title":"Multiscale symbolic hellinger spectral entropy: A novel nonlinear metric for feature extraction of mechanical signals.","authors":"Yi Du, Zong Meng, Shaochuan Zhang, Yan Wang, Fengjie Fan, Jimeng Li, Guangbin Wang","doi":"10.1016/j.isatra.2026.09.026","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.09.026","url":null,"abstract":"Entropy-based methods have been widely used for feature measurement and condition assessment of mechanical vibration signals. Addressing the instability of traditional entropy measures in noisy environments and the limited ability of classical spectral entropy to characterize subtle spectral structure changes, this paper proposes a nonlinear spectral-structure divergence measure termed Symbolic Hellinger Spectral Entropy and its multiscale extension. This method first maps spectral information to symbol sequences through energy weighting and an adaptive quantile strategy. Then, it utilizes Hellinger distance to quantify the differences between symbol probability distributions, thereby quantifying the non-uniformity of symbolic spectral probability distributions. To further improve robustness and nonlinear separability, a logarithmic mapping is introduced to stabilize and smooth feature representations. Experimental results show that SHSE exhibits competitive and stable performance compared with the evaluated baseline entropy methods in data length sensitivity, consistency, and noise robustness. By combining multi-scale analysis and the SOFBIS classifier, the proposed intelligent diagnostic framework achieves stable and satisfactory results on three typical wind turbine datasets and a real factory bearing dataset. It is worth noting that under low signal-to-noise ratio conditions, MSHSE provides stronger discriminative features than traditional entropy methods on the tested datasets, providing an efficient and robust solution for the detection, extraction, and diagnosis of fault features in rotating machinery in noisy environments.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149083367","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

Corrigendum to "Event-triggered fuzzy integral control for a class of nonlinear singularly perturbed systems" [ISA Trans. 139 (2023) 71-85]. “一类非线性奇摄动系统的事件触发模糊积分控制”的更正[ISA Trans. 139(2023) 71-85]。

IF 6.5

ISA transactions Pub Date : 2026-09-17 DOI: 10.1016/j.isatra.2026.09.011

Artit Visavakitcharoen, Wudhichai Assawinchaichote, Yan Shi, Chrissanthi Angeli

引用次数: 0

A multi-band enhanced dynamic learning method for robust fault diagnosis under signal and label noise. 一种多频带增强动态学习方法用于信号和标签噪声下的鲁棒故障诊断。

IF 6.5

ISA transactions Pub Date : 2026-09-17 DOI: 10.1016/j.isatra.2026.09.018

Xin Li, Man Yang, Jin Lv, Rui Fan, Lixin Wei, Kai Ma

{"title":"A multi-band enhanced dynamic learning method for robust fault diagnosis under signal and label noise.","authors":"Xin Li, Man Yang, Jin Lv, Rui Fan, Lixin Wei, Kai Ma","doi":"10.1016/j.isatra.2026.09.018","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.09.018","url":null,"abstract":"Accurate fault diagnosis of rotating machinery from vibration measurements faces challenges from real-world data imperfections, especially the concurrent presence of measurement noise and label noise. Measurement noise corrupts vibration signals and blurs fault patterns, whereas label noise compromises supervision reliability and induces biased optimization in deep diagnostic models. To address this compound noise challenge, this paper proposes a robust framework named MSD-DCSFNet. The framework applies a multi-band signal decomposition module combining variational mode decomposition and the fast Fourier transform to construct a structured multi-channel spectral representation. This step enhances the visibility of harmonics, sidebands, and impact-related bands under low signal-to-noise conditions. A deep collaborative synergy feature network featuring a dual-branch architecture subsequently learns complementary spectral-structural cues and impact-transient cues, producing highly discriminative features. A dynamic asymmetric cross-entropy loss is also introduced to improve training robustness. It adaptively adjusts sample weights according to prediction confidence, suppressing high-risk supervision from corrupted labels and unstable measurements. Extensive experiments on the CWRU dataset, the SEU dataset, and a self-built dataset demonstrate that the proposed framework maintains over 90% diagnostic accuracy even under a 0 dB signal-to-noise ratio combined with a 60% label noise rate. These results validate its practical value for diagnosis using imperfect industrial data.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066973","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