ISA transactions最新文献_第2页

Event-triggered distributed state and disturbance estimation for LTI systems using a network of observers. 使用观测器网络的LTI系统的事件触发分布式状态和干扰估计。

ISA transactions Pub Date : 2025-07-14 DOI: 10.1016/j.isatra.2025.07.017

Junqi Yang, Dongzheng Wang, Jianfeng Xu, Yantao Chen

引用次数: 0

Probabilistic fuzzy neural network-based indirect adaptive control framework for dynamic systems. 基于概率模糊神经网络的动态系统间接自适应控制框架。

ISA transactions Pub Date : 2025-07-14 DOI: 10.1016/j.isatra.2025.07.022

A Aziz Khater, Eslam M Gaballah, Mohammad El-Bardini, Ahmad M El-Nagar

{"title":"Probabilistic fuzzy neural network-based indirect adaptive control framework for dynamic systems.","authors":"A Aziz Khater, Eslam M Gaballah, Mohammad El-Bardini, Ahmad M El-Nagar","doi":"10.1016/j.isatra.2025.07.022","DOIUrl":"https://doi.org/10.1016/j.isatra.2025.07.022","url":null,"abstract":"This paper introduces a probabilistic Takagi-Sugeno-Kang fuzzy neural network (PTSK-FNN) within a reliable indirect adaptive control framework that updates the gains of proportional - integral - derivative (PID) controller. The reasons for introducing this study include effective management of chaotic uncertainties by integrating the probabilistic processing with TSK fuzzy neural system, improved system identification needed for calculating control signals, and a novel law for an online learning algorithm based on the Lyapunov theorem to ensure system stability. The proposed controller requires a sensitivity function derived from the system model, which can be obtained through identification techniques utilizing Wiener model based on PTSK-FNN for modeling both linear and nonlinear dynamics of the system. By dynamically modifying both the structure and parameters of the PTSK-FNNs, the PID controller gains are updated, leading to enhance control performance. This control strategy is implemented for nonlinear dynamic systems and compared with other existing controllers, demonstrating its effectiveness in engineering applications. Simulation and experimental results indicate that the proposed controller significantly outperforms its alternatives in mitigating random noise, external disturbances, and system uncertainties. The proposed controller shows minimum performance indices compared to other published controllers, achieving improved performance by reducing the mean absolute error by 34.2 % in simulations and 38.6 % in experimental results, compared to higher-performing published controllers.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661443","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 data driven fault diagnosis approach for robotic cutting tools in smart manufacturing. 基于数据驱动的智能制造机器人刀具故障诊断方法。

ISA transactions Pub Date : 2025-07-12 DOI: 10.1016/j.isatra.2025.07.021

Adel Afia, Fawzi Gougam, Abdenour Soualhi, Mohammed Wadi, Mohamed Tahi, Mohammed Amine Sahraoui

{"title":"A data driven fault diagnosis approach for robotic cutting tools in smart manufacturing.","authors":"Adel Afia, Fawzi Gougam, Abdenour Soualhi, Mohammed Wadi, Mohamed Tahi, Mohammed Amine Sahraoui","doi":"10.1016/j.isatra.2025.07.021","DOIUrl":"https://doi.org/10.1016/j.isatra.2025.07.021","url":null,"abstract":"In smart manufacturing within Industry 4.0, tool condition monitoring (TCM) is used to improve productivity and machine availability by leveraging advanced sensors and computational intelligence to prevent tool damage. This paper develops a hybrid methodology using heterogeneous sensor measurements for monitoring robotic cutting tools with four tool states: healthy, surface damage, flake damage and broken tooth. The proposed approach integrates the maximal overlap discrete wavelet packet transform (MODWPT) with health indicators to construct feature matrices for each tool state. Feature selection is performed using the tree growth algorithm (TGA) to reduce computation time and improve feature space separation by selecting only relevant features. The selected features are input into a Gaussian mixture model (GMM) to detect, identify and classify each tool state with high accuracy. The proposed method provides a classification accuracy of 99.04 % for vibration, 95.51 % for torque, and 91.67 % for force signals. Using unseen vibration data, the model achieved a test accuracy of 98.44 %, demonstrating a high degree of generalizability. Comparative analysis demonstrates that our proposed approach provides superior feature discrimination and model stability, balancing computational efficiency and classification accuracy, validating the TGA-GMM framework as an effective solution for tool fault diagnosis in noisy, high-dimensional data.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651663","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

Immersion and invariance adaptive controller and mixer for coaxial tilt-rotor UAV. 同轴倾转旋翼无人机浸没不变性自适应控制器与混频器。

ISA transactions Pub Date : 2025-07-12 DOI: 10.1016/j.isatra.2025.07.015

Longlong Chen, Yanmei Jia, Sihao Sun, Zongyang Lv, Yuhu Wu

{"title":"Immersion and invariance adaptive controller and mixer for coaxial tilt-rotor UAV.","authors":"Longlong Chen, Yanmei Jia, Sihao Sun, Zongyang Lv, Yuhu Wu","doi":"10.1016/j.isatra.2025.07.015","DOIUrl":"https://doi.org/10.1016/j.isatra.2025.07.015","url":null,"abstract":"This study presents a motion control system for a coaxial tilt-rotor (CTR) unmanned aerial vehicle (UAV) equipped with two CTR modules and a tail rotor. The existing adaptive control strategies for CTRUAVs fail to guarantee the theoretical convergence of estimated parameters to their true values. Additionally, the existing mixer requires frequent and inefficient adjustments of the tilt angles for motion control. To address these issues, this work proposes a control strategy that integrates a robust integral of the sign of the error (RISE)-based immersion and invariance (I&I) adaptive controller with segmented gains and an improved mixer. The RISE-based adaptive controller is theoretically capable of estimating and compensating for external disturbance torques and forces with bounded derivatives. Furthermore, a model of the CTR module that accounts for differences between the upper and lower rotors is introduced, and the proposed mixer is designed to realize efficient control at varying tilt angles of the CTR modules. Experimental results demonstrate that the proposed control scheme significantly improves stability, transient response speed, disturbance rejection performance, and parameter estimation accuracy compared to existing control strategies for the CTRUAV.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661441","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

Feedback chaotic growth optimizer for parameter extraction of a novel direct current arc model. 一种新型直流电弧模型参数提取的反馈混沌生长优化器。

ISA transactions Pub Date : 2025-07-12 DOI: 10.1016/j.isatra.2025.07.023

Zhendong Yin, Li Wang, Xianqun Qiu, Jiyong Zhang

{"title":"Feedback chaotic growth optimizer for parameter extraction of a novel direct current arc model.","authors":"Zhendong Yin, Li Wang, Xianqun Qiu, Jiyong Zhang","doi":"10.1016/j.isatra.2025.07.023","DOIUrl":"https://doi.org/10.1016/j.isatra.2025.07.023","url":null,"abstract":"Direct current (DC) arc faults are a leading cause of fire incidents in photovoltaic (PV) systems. Accurate modeling of DC arc faults is essential for understanding the underlying mechanisms of DC arcs and for developing effective detection strategies. In this study, we propose a novel model for DC arcs, referred to as the exponent segmented noise model. This model effectively characterizes arc noise by establishing an exponential relationship between frequency values and spectral energy. To enable precise parameter extraction from the exponent segmented noise model, we introduce a new metaheuristic algorithm called the feedback chaotic growth optimizer (FCGRO). FCGRO improves upon the traditional growth optimizer (GRO) by integrating feedback operators and chaos mechanisms. Firstly, the convergence performance of FCGRO is rigorously evaluated through comparative experiments on three well-established benchmark engineering optimization problems. Subsequently, based on data collected from an established experimental platform, the proposed FCGRO and eight state-of-the-art algorithms are employed to extract parameters of the exponent segmented noise model for DC arc faults. The FCGRO achieves an overall average root mean square error (RMSE) of 0.0418 with a standard deviation of 0.00818, representing reductions of at least 10.43 % and 26.86 %, respectively, compared to the other eight methods. These results indicate that FCGRO delivers more accurate and stable parameter estimations than the competing algorithms. Regarding computational efficiency, FCGRO has an average processing time of 9.969 s, ranking it third among the nine evaluated methods, which confirms its competitiveness in terms of speed. Finally, compared with existing DC arc models, the proposed exponent segmented noise model reduces RMSE by an average of 53.26 %, demonstrating its superior modeling capability.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692843","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

Neural network-based adaptive decentralized safe control for interconnected nonlinear systems with time delays. 基于神经网络的时滞非线性互联系统自适应分散安全控制。

ISA transactions Pub Date : 2025-07-12 DOI: 10.1016/j.isatra.2025.07.016

Chong Liu, Leiming Wang, Zhousheng Chu, Hanguang Su

{"title":"Neural network-based adaptive decentralized safe control for interconnected nonlinear systems with time delays.","authors":"Chong Liu, Leiming Wang, Zhousheng Chu, Hanguang Su","doi":"10.1016/j.isatra.2025.07.016","DOIUrl":"https://doi.org/10.1016/j.isatra.2025.07.016","url":null,"abstract":"This paper addresses the safety control issue for interconnected nonlinear systems with time delays and asymmetric input constraints by proposing a decentralized dynamic event-triggered (DET) controller based on the adaptive dynamic programming (ADP) method. Unlike other studies on large-scale interconnected systems, the equilibrium point of the system under our study is not zero. Firstly, by incorporating a discount factor and introducing a barrier function and a Lyapunov-Krasovskii (L-K) function, we construct a cost function for the interconnected system with a non-zero equilibrium point, time delay, and constraints, thereby transforming the constrained decentralized control problem into an unconstrained optimal control problem (OCP). Subsequently, an event-based Hamilton-Jacobi-Bellman (HJB) equation is established. To enhance computational efficiency, a DET mechanism is proposed. Then, the event-triggered HJB equation is solved utilizing the learning method based on ADP. Simultaneously, the weights of the neural network (NN) are optimized using a gradient descent algorithm and experience replay (ER) techniques. By employing ER technology, we have eliminated the system's requirement for continuous excitation. Furthermore, through theoretical analysis, we have demonstrated the uniform ultimate boundedness (UUB) of the system states and neural network weights, and excluded Zeno behavior. Finally, the effectiveness of the proposed method is validated by using a spring-pendulum example.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719289","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

Enhanced obstacle avoidance for autonomous underwater vehicles via path integral control based on guiding vector field. 基于导向矢量场的路径积分控制增强自主水下航行器避障能力。

ISA transactions Pub Date : 2025-07-11 DOI: 10.1016/j.isatra.2025.07.020

Jintao Zhao, Tao Liu, Junhao Huang

{"title":"Enhanced obstacle avoidance for autonomous underwater vehicles via path integral control based on guiding vector field.","authors":"Jintao Zhao, Tao Liu, Junhao Huang","doi":"10.1016/j.isatra.2025.07.020","DOIUrl":"https://doi.org/10.1016/j.isatra.2025.07.020","url":null,"abstract":"Autonomous Underwater Vehicles (AUVs) face significant challenges in tracking, navigation, and obstacle avoidance-critical aspects for advancing intelligent underwater robotics. This research presents a new navigation technique that combines Guiding Vector Field (GVF) concepts with Model Predictive Path Integral (MPPI) control to improve the precision and efficiency of vectored thruster AUV operating in complex environments. The proposed approach utilizes the AUV's relative positioning and environmental data to generate obstacle avoidance trajectories as desired GVF. Subsequently, MPPI optimization is applied to control inputs, considering dynamic constraints, to achieve effective tracking and obstacle avoidance. Extensive simulation experiments demonstrate the method's efficacy in navigating complex scenarios with non-convex obstacles. In the aspect of path tracking, the tracking error is reduced by 64 %, while maintaining safe distances in various obstacle configurations. Results show that the integrated method successfully combines local optimization prediction capabilities of MPPI with the global velocity planning of GVF, enabling efficient AUV navigation in intricate environments while ensuring the effectiveness and safety of the execution process. The method demonstrates robust performance even under disturbance conditions, maintaining a tracking error of only 0.017 m. This research contributes a solution for AUV operation in challenging maritime settings, with applications in marine surveying, underwater search and rescue, and offshore operations. By addressing key challenges in underwater navigation, this study advances the practical capabilities of AUV technology, paving the way for more efficient and reliable underwater robotic systems.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144644398","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

Minimum rational entropy fault-tolerant control for nonlinear stochastic distribution control systems with quantized signals. 具有量化信号的非线性随机分布控制系统的最小有理熵容错控制。

ISA transactions Pub Date : 2025-07-11 DOI: 10.1016/j.isatra.2025.07.008

Lifan Li, Lina Yao, Yaoqiang Wang

引用次数: 0

Tem²-KAN: Data-driven temporal temperature prediction via an improved Kolmogorov-Arnold network. Tem2-KAN：基于改进的Kolmogorov-Arnold网络的数据驱动时间温度预测。

ISA transactions Pub Date : 2025-07-10 DOI: 10.1016/j.isatra.2025.07.014

Yongxiang Lei, Bin Deng, Ziyang Wang

{"title":"Tem2-KAN: Data-driven temporal temperature prediction via an improved Kolmogorov-Arnold network.","authors":"Yongxiang Lei, Bin Deng, Ziyang Wang","doi":"10.1016/j.isatra.2025.07.014","DOIUrl":"https://doi.org/10.1016/j.isatra.2025.07.014","url":null,"abstract":"Accurate temperature forecasting relies on traditional meteorological parameters that are essential for monitoring weather informatics and guiding forecasting efforts. This study introduces a deep learning architecture for high-precision climate temperature forecasting via an improved Kolmogorov-Arnold Networks, named Tem2-KAN. Grounded in the Kolmogorov-Arnold representation theorem, Tem2-KAN explores replacing conventional linear weights in neural networks with spline-parameterized univariate functions, enabling dynamic learning of nonlinear climate patterns while maintaining intrinsic interpretability. The proposed framework uniquely integrates the universal approximation capabilities of Multi-Layer Perceptrons (MLPs) with physically meaningful feature visualization through its adaptive activation functions, addressing critical limitations of black-box climate models. A temperature prediction pipeline is established that first preprocesses raw meteorological data from UK monitoring stations, then trains Tem2-KAN to map historical trends to multi-horizon forecasts. Rigorous evaluations on real-world climate datasets demonstrate Tem2-KAN's dual advantage achieving state-of-the-art prediction accuracy while utilizing fewer trainable parameters. In addition, a systematic ablation study quantifies the sensitivity of key Tem2-KAN-specific hyperparameters (spline order k, grid resolution grid) on forecasting performance. Finally, we theoretically prove Tem2-KAN's universal approximation capacity through function space analysis, and practically, we demonstrate its interpretability and prediction performance. These innovations position Tem2-KAN as a paradigm-shifting tool for climate informatics, offering meteorologists both high predictive performance and mechanistic insight into temperature dynamics. The framework's reduced hyperparameter complexity further enhances its viability for operational forecasting systems.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669173","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

Real-time random reference tracking nonlinear model predictive control: a case study on wind turbines. 实时随机参考跟踪非线性模型预测控制：风电机组实例研究。

ISA transactions Pub Date : 2025-07-10 DOI: 10.1016/j.isatra.2025.06.018

Mohammad Soleymani, Nooshin Bigdeli, Mehdi Rahmani

{"title":"Real-time random reference tracking nonlinear model predictive control: a case study on wind turbines.","authors":"Mohammad Soleymani, Nooshin Bigdeli, Mehdi Rahmani","doi":"10.1016/j.isatra.2025.06.018","DOIUrl":"https://doi.org/10.1016/j.isatra.2025.06.018","url":null,"abstract":"Recently, a research effort to extend nonlinear model predictive control methods from setpoint stabilization to reference tracking has been felt increasingly. On the other hand, uncertainty in the reference signal and the requirement for its dynamic forecasting in applications such as wind turbine control motivate the need for robust tracking nonlinear model predictive control approaches more and more. Therefore, this study proposes a random reference tracking nonlinear model predictive control with dynamic forecasting of stochastic references. Convergence to a robust invariant set is guaranteed by an additional constraint limiting the previous step's tracking stage cost function. The proposed predictive approach is implemented using a parallel Newton-type method to make it more efficient and applicable. The proposed approach for wind turbine control is designed considering the random wind speed reference. Simulations are performed for extreme and fatigue load scenarios. The results show that the proposed controller performs more robustly than the nominal nonlinear model predictive control approach, performing better in optimal power extraction and reducing aerodynamic loads.","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621605","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