{"title":"Integrated physics-data-based LPV-LMS-MPC coordinated control for simultaneous luffing-slewing of truck cranes.","authors":"Rui Zhou, Zhenzhi Zhang, Xiuheng Wu, Dongpu Cao, Fei-Yue Wang, Jun Ling","doi":"10.1016/j.isatra.2026.10.012","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.10.012","url":null,"abstract":"<p><p>Simultaneous luffing-slewing control of truck cranes is challenging due to nonlinear dynamics, parameter variations, and motion coupling. To overcome these issues, this article introduces an integrated physics-data-based LPV-LMS-MPC framework for the coordinated control of an electro-hydraulic system. A physics-based affine linear parameter-varying (LPV) model is first derived from fundamental principles to capture cross-coupling dynamics. To address parameter time-variation without compromising model interpretability, a Kronecker-product reformulation is introduced. This transformation enables the least mean square (LMS) algorithm to selectively update only the uncertain coefficients online using real system output data while preserving the predefined physical structure and structural zeros. The updated coefficients are mapped back to the LPV state-space matrices to reconstruct a constrained incremental model predictive control (MPC) prediction model, enabling real-time adaptive optimization. Simulation and prototype experiments validate the framework. Results show that the LPV-LMS model reliably captures disturbed system dynamics, and the integrated LPV-LMS-MPC scheme delivers superior coordinated tracking while requiring far less computation time than nonlinear MPC.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225651","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}
{"title":"Enhanced vehicle state estimation using PVDF-based intelligent tire force measurements.","authors":"Zhenqiang Quan, Xiaoqiang Sun, Tianli Gu, Yingfeng Cai, Chen Huang","doi":"10.1016/j.isatra.2026.10.006","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.10.006","url":null,"abstract":"<p><p>Intelligent tire systems enable real-time measurement of tire forces, offering new opportunities for improving vehicle state estimation. This paper proposes a state estimation approach that integrates longitudinal, lateral, and vertical tire force information obtained from a PVDF-based intelligent tire system. An optimal in-tire sensor layout is determined using global sensitivity analysis, and tire forces are estimated from in-tire voltage signals via Gaussian Process Regression. To effectively utilize the estimated forces, a tire force update frequency compensation strategy and a vehicle parameter estimation method are incorporated into an adaptive cubature Kalman filter framework. Simulation and experimental results demonstrate that the proposed approach significantly improves the accuracy of vehicle speed and sideslip angle estimation and enhances robustness against variations in vehicle and tire parameters, including tire-road friction.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187148","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}
ISA transactionsPub Date : 2026-10-10DOI: 10.1016/j.isatra.2026.09.040
Haiyu Li, Huang Xun, Jinde Zheng, Haiyang Pan
{"title":"Arnoldi mode decomposition: A subspace sparse constrained method for periodic pulse extraction.","authors":"Haiyu Li, Huang Xun, Jinde Zheng, Haiyang Pan","doi":"10.1016/j.isatra.2026.09.040","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.09.040","url":null,"abstract":"<p><p>To accurately detect early mechanical faults, a novel fault feature extraction method named Arnoldi mode decomposition (AMD) is proposed in this paper. First, AMD method constructs a numerically stable operator based on the output variance, projects the large-scale matrix formed from the signal onto a Krylov subspace via the Arnoldi process, and builds a set of low-dimensional orthogonal bases. The multi-parameter coupled filter optimization problem is then projected into the subspace for refined solution. Second, by exploiting the numerical stability of the orthogonal bases and the low-dimensional approximation capability of the Krylov subspace, numerical errors are effectively suppressed, thereby enhancing the stability of the feature extraction process. Finally, a nonlinear optimization is performed within the subspace using the reweighted kurtosis (RK) as the criterion, and a subspace sparse constraint strategy is adopted to update the filter along as few critical directions as possible, suppressing noise components and improving interpretability. Simulation signals and experimental bearing fault data demonstrate that the proposed AMD significantly improves the extraction ability of weak fault impulses, feature distinctiveness, and numerical stability, while maintaining low computational cost.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187127","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}
ISA transactionsPub Date : 2026-10-09DOI: 10.1016/j.isatra.2026.10.004
Zongsheng Geng, Dongbin Jiao, Dongdong Zhao, Tenglong Huang, Xinde Li, Shi Yan
{"title":"Fully distributed protocol for multi-agent systems with dynamic event-triggered communication under DoS attacks.","authors":"Zongsheng Geng, Dongbin Jiao, Dongdong Zhao, Tenglong Huang, Xinde Li, Shi Yan","doi":"10.1016/j.isatra.2026.10.004","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.10.004","url":null,"abstract":"<p><p>Denial-of-service (DoS) attacks pose a major challenge to reliable leader-following coordination in linear multi-agent systems. To address this issue, an adaptive dynamic event-triggered protocol is developed. Specifically, the proposed protocol is fully distributed, requires no global topology information, and operates without a global coordinate frame by using only relative state information. Furthermore, a dynamically evolving auxiliary variable is introduced into the triggering condition to enlarge inter-event intervals and reduce the communication burden. Moreover, Lyapunov-based analysis shows that the proposed protocol achieves exponential leader-following consensus in the absence of external disturbances, while in the presence of external disturbances, the consensus error remains bounded with minor modifications to the adaptive law. Finally, simulation results verify the effectiveness of the proposed method.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198724","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}
ISA transactionsPub Date : 2026-10-09DOI: 10.1016/j.isatra.2026.10.002
Delei Min, Jianxiao Cao, Yidan Mei, Guodong Yin
{"title":"A fast unknown-input reconstruction solution framework for desired steady-state steering of distributed drive vehicles.","authors":"Delei Min, Jianxiao Cao, Yidan Mei, Guodong Yin","doi":"10.1016/j.isatra.2026.10.002","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.10.002","url":null,"abstract":"<p><p>This paper develops a solution framework for fast reconstruction of unknown wheel-end inputs for four-wheel steering distributed drive vehicles, enabling steady-state steering with the desired small turning radius while maintaining the desired vehicle attitude and driving speed. The core contributions of this paper include a novel system constraint analysis method to determine the valid known-variable combinations, a simplified nonlinear tire model for fast computation without accuracy loss, and differentiated iterative solving procedures adapted to different reconstruction problems. Simulations validate rotational speed reconstruction and steering angle reconstruction with output errors of only 1.78% and 3.07%, respectively, with robustness under varying road adhesion coefficients and driving speeds. Compared with model predictive control and deep reinforcement learning, this framework outperforms mainstream path-tracking methods in both accuracy and efficiency.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169177","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}
{"title":"Cross-condition rolling bearing fault diagnosis via channel-wise adaptive decomposition and gated time-frequency fusion.","authors":"Liguo Han, Qilong He, Xianchao Wang, Minghao Li, Kai Wang, Fei Ding, Haining Zhang","doi":"10.1016/j.isatra.2026.10.003","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.10.003","url":null,"abstract":"<p><p>Cross-condition bearing diagnosis remains challenging because speed-load shifts and heterogeneous sensor responses can weaken fault signatures. This study proposes a measurement-driven framework combining channel-wise adaptive decomposition and dual-branch time-frequency fusion with gating for multi-channel bearing signals. Channel-wise VMD optimized by the Improved Whale Optimization Algorithm (IWOA-VMD) enhances fault-sensitive components, while temporal impact features and envelope-spectrum modulation features are jointly recalibrated for classification. The evaluation on measured data covers nine source-to-target tasks across discrete speed-load conditions. The proposed method reached 90.20% mean task-level accuracy, numerically higher than Raw 1D-CNN (87.12%) and WDCNN (83.02%); significance was observed for WDCNN only. Supplementary public-dataset results further support its applicability under different acquisition settings.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187188","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}
{"title":"Dynamic edge-event-based consensus of nonlinear multi-agent systems with sampled-data measurement.","authors":"Yifan Zhang, Jingtao Man, Yin Sheng, Jingang Lai, Zhigang Zeng","doi":"10.1016/j.isatra.2026.10.005","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.10.005","url":null,"abstract":"<p><p>This paper investigates the leader-following consensus problem for a class of nonlinear multi-agent systems (MASs) by developing a novel edge-event-triggered control framework. To alleviate communication burdens, an edge-based triggering strategy is employed, wherein both the control protocols and triggering conditions depend exclusively on local relative information, eliminating the reliance on global network knowledge. Recognizing the constraints of practical communication scenarios, asynchronous information interactions across different edges are explicitly considered. As a core contribution, we seamlessly integrate sampled-data measurements with the edge-triggering paradigm, proposing a sampled-data edge-event triggered mechanism. This framework fundamentally circumvents the stringent requirement of continuous state monitoring and intrinsically excludes Zeno behavior, thereby significantly saving computational and communication resources. Furthermore, the framework is extended to a dynamic sampled-data-based edge-event-triggered mechanism, which introduces an auxiliary variable to provide greater flexibility in communication scheduling. Finally, the effectiveness and superiority of the proposed methods are demonstrated through an application example and comparative simulations.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187198","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}
ISA transactionsPub Date : 2026-10-08DOI: 10.1016/j.isatra.2026.10.001
Jiayi Yang, Xinyuan Li, Ti Chen, Zhou Yang, Dongping Jin
{"title":"Hybrid-triggered backstepping control for a rotating flexible beam with a mobile actuator.","authors":"Jiayi Yang, Xinyuan Li, Ti Chen, Zhou Yang, Dongping Jin","doi":"10.1016/j.isatra.2026.10.001","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.10.001","url":null,"abstract":"<p><p>This paper considers a rotating flexible beam equipped with a mobile actuator. The actuator leads to an extra translational degree of freedom and makes the spatial distribution of the control input vary with the actuator position. A coupled PDE-ODE model of the system is derived via Hamilton's principle. For the strongly coupled rigid-flexible dynamics, a continuous-time backstepping controller incorporating barrier Lyapunov functions is developed for attitude regulation and vibration suppression under output constraints. A hybrid-triggered control scheme is proposed to further balance control performance with control update frequency and mitigate mechanical wear of the actuator. Specifically, event-triggered control is applied to the rigid-body motions, while continuous-time control is maintained for the vibration suppression task. Numerical simulations verify the effectiveness of the proposed control scheme.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198689","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}
ISA transactionsPub Date : 2026-10-07DOI: 10.1016/j.isatra.2026.09.041
Wei-Cheng Zhao, Ming-Feng Ge, Qian Chen
{"title":"Distributed time-varying optimal formation control for uncertain networked marine surface vehicles with explicit safety constraints.","authors":"Wei-Cheng Zhao, Ming-Feng Ge, Qian Chen","doi":"10.1016/j.isatra.2026.09.041","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.09.041","url":null,"abstract":"<p><p>This paper studies distributed time-varying optimal formation control of networked marine surface vehicles (NMSVs) with explicit safety constraints. A distributed constrained optimization and tracking control framework is proposed for uncertain NMSVs, comprising an optimization layer for constrained optimal reference generation and a local control layer for physical reference tracking. In the optimization layer, the formation task is formulated as a continuous time constrained optimization problem with time-varying local cost functions. A Newton-like logarithmic barrier update law is developed to generate time-varying optimal references while preserving constraint feasibility in the optimization layer under the stated assumptions. The relaxation parameter provides a designable initialization margin that enlarges the relaxed admissible domain to accommodate a prescribed initial state. In the local control layer, a robust finite-time tracking controller is designed to compensate for model uncertainties and drive the physical NMSVs to track the optimized references generated by the optimization layer. Theoretical analysis establishes constraint feasibility and convergence of the optimization dynamics, together with finite-time tracking of the physical NMSVs. Simulation results verify the effectiveness of the proposed framework.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149129595","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}
ISA transactionsPub Date : 2026-10-04DOI: 10.1016/j.isatra.2026.09.038
Mohammad Lotfi, Mohammad Bagher Menhaj, Mehdi Karrari, Mohammad Haeri
{"title":"A two-stage learning framework for piecewise affine system identification.","authors":"Mohammad Lotfi, Mohammad Bagher Menhaj, Mehdi Karrari, Mohammad Haeri","doi":"10.1016/j.isatra.2026.09.038","DOIUrl":"https://doi.org/10.1016/j.isatra.2026.09.038","url":null,"abstract":"<p><p>This paper proposes a fully data-driven and end-to-end adaptive framework for the complete black-box identification of piecewise affine ARX (PWARX) systems, in which model structure discovery, submodel parameter estimation, recursive parameter fine-tuning, and explicit polyhedral partition identification are integrated into a unified two-stage learning architecture. In Stage 1, an unsupervised process determines the number and order of submodels, computes consistent initial submodel parameter estimates, and constructs a compact labeled training set. In Stage 2, a self-tuning semi-supervised support vector machine (STS3VM) iteratively learns the submodel classifiers, while adaptive-forgetting recursive least squares fine-tunes the submodel parameters. The learned decision rule is then converted into explicit polyhedral partitions, with stage-wise convergence guarantees. Extensive simulations demonstrate high accuracy, reproducibility, robustness under diverse noise conditions, and scalability.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170304","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}