ISA transactions最新文献_第6页

FGO-Tuned 2-DOF PI controller for pressure regulation in centrifugal fans driven by induction motors using an experimentally validated model 基于实验验证模型的fgo调谐2-DOF PI控制器用于感应电机驱动的离心风机压力调节。

IF 6.5 2区计算机科学

ISA transactions Pub Date : 2026-02-01 Epub Date: 2025-12-10 DOI: 10.1016/j.isatra.2025.12.012

Cebrail Turkeri , Serdar Ekinci , Davut Izci , Oleh Kiselychnyk

引用次数: 0

An online reinforcement learning-based conflict resolution approach for human–vehicle cooperative systems 基于在线强化学习的人车合作系统冲突解决方法。

IF 6.5 2区计算机科学

ISA transactions Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.isatra.2025.12.017

Viet Thang Dang , Duy Lam Mac , Duc Thinh Le , Tung Lam Nguyen , Van Trong Dang

引用次数: 0

Reinforcement learning-based pitch control for wind turbines using double deep Q-networks 基于强化学习的双深q网络风力发电机俯仰控制。

IF 6.5 2区计算机科学

ISA transactions Pub Date : 2026-02-01 Epub Date: 2026-01-09 DOI: 10.1016/j.isatra.2026.01.015

Víctor Espinoza , Carolina Ormaza , Christian Tutivén , Yolanda Vidal

{"title":"Reinforcement learning-based pitch control for wind turbines using double deep Q-networks","authors":"Víctor Espinoza , Carolina Ormaza , Christian Tutivén , Yolanda Vidal","doi":"10.1016/j.isatra.2026.01.015","DOIUrl":"10.1016/j.isatra.2026.01.015","url":null,"abstract":"<div><div>This paper presents a novel reinforcement learning-based framework for pitch control in wind turbines, addressing the challenges posed by high nonlinearity and uncertainties in wind dynamics. Traditional pitch control methods, such as proportional-integral controllers, often struggle with adaptability and load mitigation under fluctuating wind conditions. To enhance control efficiency, the proposed framework employs a two-stage approach: policy transfer and policy refinement. In the policy transfer stage, a proportional-integral controller is used to estimate an initial control policy. This policy is then refined using the double deep Q-Network algorithm. Furthermore, a novel reward function is introduced as an improved version of a recent formulation in the literature, ensuring better alignment with practical operational scenarios. Simulations demonstrate that the proposed controller outperforms the industry-standard ROSCO controller, achieving a 15.87% reduction in power fluctuations, while preserving comparable structural load levels, with slight reductions in some cases. These results highlight the potential of reinforcement learning-based control to enhance wind turbine power regulation without compromising structural safety in terms of loads and vibrations.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"169 ","pages":"Pages 428-435"},"PeriodicalIF":6.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145992371","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 accuracy of cyber-physical systems over bandwidth-limited channel 网络物理系统在有限带宽信道上的跟踪精度。

IF 6.5 2区计算机科学

ISA transactions Pub Date : 2026-02-01 Epub Date: 2025-12-23 DOI: 10.1016/j.isatra.2025.12.027

Xian-He Zhang , Bo Li , Xiao-Wei Jiang , Wenxing Wang , Shaoying Wang

引用次数: 0

Affine formation maneuver control for multi-agent systems against false data injection attacks based on adaptive dual-terminal dynamic triggering 基于自适应双端动态触发的多智能体系统防假数据注入仿射编队机动控制

IF 6.5 2区计算机科学

ISA transactions Pub Date : 2026-02-01 Epub Date: 2025-12-15 DOI: 10.1016/j.isatra.2025.12.023

Yuezheng Xie , Chenjun Liu , Jason J.R. Liu

引用次数: 0

Optimized neural-network-assisted model-based motion control of hydraulic manipulators under unmodeled uncertainties 未建模不确定条件下液压机械臂的优化神经网络辅助模型运动控制。

IF 6.5 2区计算机科学

ISA transactions Pub Date : 2026-02-01 Epub Date: 2025-12-31 DOI: 10.1016/j.isatra.2025.12.055

Manzhi Qi , Yangxiu Xia , Shizhao Zhou , Litong Lyu , Zheng Chen

{"title":"Optimized neural-network-assisted model-based motion control of hydraulic manipulators under unmodeled uncertainties","authors":"Manzhi Qi , Yangxiu Xia , Shizhao Zhou , Litong Lyu , Zheng Chen","doi":"10.1016/j.isatra.2025.12.055","DOIUrl":"10.1016/j.isatra.2025.12.055","url":null,"abstract":"<div><div>Multi-degree-of-freedom (multi-DoF) hydraulic manipulators are widely used in heavy-duty tasks due to their high power density. However, their inherent nonlinearities and uncertain dynamics present significant challenges to precise control. Traditional model-based control methods, while effective in addressing these nonlinearities, rely heavily on accurate dynamic models, which complicates controller design and increases computational demands. To address these limitations, this paper proposes a neural-network-assisted adaptive robust control approach that reduces the dependence on precise modeling by compensating for unmodeled dynamics. Radial basis function neural networks (RBFNNs) are employed to approximate the uncertain dynamics, with the network structure optimized using the K-means<span><math><mo>+</mo><mo>+</mo></math></span> algorithm to enhance approximation accuracy and computational efficiency. Additionally, desired signals are used instead of measured signals to mitigate sensitivity to measurement noise. The proposed method is rigorously analyzed to guarantee the stability and asymptotic tracking performance of the closed-loop system. Experimental results on a hydraulic manipulator validate the effectiveness of the approach, demonstrating substantial improvements in control performance compared with conventional model-based strategies.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"169 ","pages":"Pages 603-613"},"PeriodicalIF":6.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145919435","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

Fault-tolerant control of discrete-time markov jump power systems based on mode detection information 基于模式检测信息的离散马尔可夫跳变电力系统容错控制。

IF 6.5 2区计算机科学

ISA transactions Pub Date : 2026-02-01 Epub Date: 2026-01-03 DOI: 10.1016/j.isatra.2026.01.002

Shen Zhang, Wenhao Zhang, Lei Su, Tian Fang, Kang Wang

引用次数: 0

Bumpless transfer for distributed generation units with mode-dependent controllers 具有模式依赖控制器的分布式发电机组的无颠簸传输。

IF 6.5 2区计算机科学

ISA transactions Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.isatra.2025.12.048

Pablo.E. Muñoz , Ricardo.J. Mantz , Sergio.A. González

引用次数: 0

Cooperative control for FWID-EVs with active suspension under extreme conditions by using off-policy safe reinforcement learning 基于非策略安全强化学习的fwidd - ev主动悬架极端工况协同控制

IF 6.5 2区计算机科学

ISA transactions Pub Date : 2026-02-01 Epub Date: 2026-01-07 DOI: 10.1016/j.isatra.2026.01.010

Zhijiang Gao, Pak Kin Wong, Fuqiu Zhou, Jing Zhao

{"title":"Cooperative control for FWID-EVs with active suspension under extreme conditions by using off-policy safe reinforcement learning","authors":"Zhijiang Gao, Pak Kin Wong, Fuqiu Zhou, Jing Zhao","doi":"10.1016/j.isatra.2026.01.010","DOIUrl":"10.1016/j.isatra.2026.01.010","url":null,"abstract":"<div><div>The unbalanced distribution of tyre normal forces induced by lateral load transfer poses a significant threat to vehicle handling stability under extreme conditions. However, the mitigation of such imbalance is inherently constrained by the suspension support capability. This work proposes a novel off-policy safe reinforcement learning-based cooperative (OSRC) controller that coordinates direct yaw control (DYC) and active suspension system (ASS) to optimize the distribution of tyre normal forces and enhance the handling stability of the four-wheel independent drive electric vehicle (FWID-EV) under extreme conditions. First, a DYC-ASS cooperative architecture is proposed to optimize the distribution of tyre normal forces in the FWID-EV system. Subsequently, a combined tyre-slip-angle and phase-portrait (CTP) method is proposed to characterize the relationship between the tyre force saturation and the lateral stability region (LSR). Based on a two-player game, the OSRC controller is proposed to achieve the cooperation between the DYC and the ASS and enhance the handling stability of the FWID-EV under extreme conditions. Finally, the effectiveness of the proposed OSRC controller is examined via the rapid control prototyping tests.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"169 ","pages":"Pages 589-602"},"PeriodicalIF":6.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145954491","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 formation control for multiagent systems with velocity-based collision avoidance 基于速度的避碰多智能体系统的固定时间编队控制。

IF 6.5 2区计算机科学

ISA transactions Pub Date : 2026-02-01 Epub Date: 2025-12-19 DOI: 10.1016/j.isatra.2025.12.029

Shuangsi Xue , Zihang Guo , Junkai Tan , Kai Qu , Hui Cao , Badong Chen

{"title":"Fixed-time formation control for multiagent systems with velocity-based collision avoidance","authors":"Shuangsi Xue , Zihang Guo , Junkai Tan , Kai Qu , Hui Cao , Badong Chen","doi":"10.1016/j.isatra.2025.12.029","DOIUrl":"10.1016/j.isatra.2025.12.029","url":null,"abstract":"<div><div>This paper investigates the formation tracking problem in collision-free multiagent systems (MASs). To address the disruption to control performance caused by external disturbances and unknown dynamics, an extended state observer (ESO) incorporating sliding mode and bias radial basis function neural network (RBFNN) is designed for expeditious estimation. The feedback from the observed information is then utilized to formulate a fixed-time formation strategy. Simultaneously, addressing these challenges increases the computational load and communication costs in MASs. Therefore, a distributed event-triggered mechanism is introduced to dynamically adjust controllers’ update intervals. Furthermore, to address the high initial speed inherent in the fixed-time control strategy, a velocity-based artificial potential field (APF) is designed to prevent collisions between agents and alleviate actuator strain. The semi-globally ultimately fixed-time boundedness (SGUFTB) of the entire system is demonstrated via Lyapunov theory. The validity of the proposed strategy is subsequently confirmed through the execution of comparative simulation experiments involving five omnidirectional robots.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"169 ","pages":"Pages 26-38"},"PeriodicalIF":6.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145844481","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