ISA transactions最新文献

{"title":"Reconstruction of extended speed observer for induction motor stability enhancement with stabilizing function","authors":"Lelisa Wogi ,&nbsp;Marcin Morawiec ,&nbsp;Tadele Ayana ,&nbsp;Andrzej Jąderko ,&nbsp;Janusz Baran","doi":"10.1016/j.isatra.2025.03.026","DOIUrl":"10.1016/j.isatra.2025.03.026","url":null,"abstract":"<div><div>This study proposed an extended speed observer reconstruction for multiscalar control-based speed control of an induction motor (IM) traction drive for electric vehicles (EVs). It employs an extended-speed adaptive observer with a stabilizing function to enhance the sensorless control of IM low-speed operation. An approach for estimating rotor speed using machine models of multiscalar control with the stabilizing function is used for controlling IM drive for EVs. A stabilizing function is built using Lagrange's identity to meet the stability requirement and to reduce the unstable zone during a low-speed regenerating mode of operation. The pole-placement method selects the stabilizing function gains to minimize the estimation of rotor speed errors and to increase resilience against parameter uncertainties at zero and low rotor speed operation. The proposed speed estimator performance is compared to motor speed throughout several driving profiles of EVs, including motoring, constant speed, low speed, and regenerating modes of operation. The findings show that the proposed speed observer performs well across various driving profiles. The study found that using an adaptive full-order observer for speed sensorless control in EV is effective and stable.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"161 ","pages":"Pages 243-260"},"PeriodicalIF":6.3,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904568","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}

{"title":"Prescribed performance observer-controller design for MIMO nonlinear systems with unknown control directions","authors":"Guan-Qun Chen ,&nbsp;Wen-Bo Xie ,&nbsp;Dong Qu ,&nbsp;Yan Peng","doi":"10.1016/j.isatra.2025.03.021","DOIUrl":"10.1016/j.isatra.2025.03.021","url":null,"abstract":"<div><div>In this paper, an observer-controller with performance constraints is proposed for a class of multi-input multi-output (MIMO) nonlinear systems with unknown input directions. First, a novel method for limiting estimation error is proposed in the observer design process, which is achieved by integrating the equivalent output injection approach with the prescribed performance technique. Subsequently, a controller is presented that combines the Nussbaum function with the prescribed performance control (PPC) method, which can address the issue of unknown control directions and ensure constrained tracking errors. Finally, the effectiveness of the proposed methodology is validated via a trajectory tracking control task for an unmanned surface vehicle (USV).</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"161 ","pages":"Pages 136-144"},"PeriodicalIF":6.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789477","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}

{"title":"Optimal hybrid type-3 fuzzy controller for horizontal axis wind turbines: Comparative study","authors":"Adnan Qahtan Adnan ,&nbsp;Mohammed Khalil Hussain ,&nbsp;Ardashir Mohammadzadeh ,&nbsp;Kamran Sabahi","doi":"10.1016/j.isatra.2025.03.025","DOIUrl":"10.1016/j.isatra.2025.03.025","url":null,"abstract":"<div><div>The blade pitch angle (BPA) controller is key factor to improve the power generation of wind turbine (WT). Due to the aerodynamic structural behavior of the rotor blades, wind turbine system performance is influenced by pitch angle and environmental conditions such as wind speed, which fluctuate throughout the day. Therefore, to overcome the pitch angle control (PAC) problem, high wind speed conditions, and due to type-1 and type-2 fuzzy logic limitations for handling high levels of uncertainty, the newly proposed optimal hybrid type-3 fuzzy logic controller has been applied and compared since type-3 fuzzy controllers utilize three-dimensional membership functions, unlike type-2 and type-1 fuzzy logic controllers. In this paper six different controllers are applied and compared for BPA in WT: type-1 fuzzy logic controller (T1-FLC), interval type-2 fuzzy logic controller (IT2-FLC), interval type-3 fuzzy logic controller (IT3-FLC), optimal hybrid type-1 fuzzy-PID controller (HT1-FPIDC), optimal hybrid type-2 fuzzy-PID controller (HT2-FPIDC), and optimal hybrid type-3 fuzzy-PID controller (HT3-FPIDC). The comparison between Mamdani and Sugeno fuzzy inference systems (FIS) has been applied to find the best inference system. Genetic Algorithm (GA) and Particle swarm optimization (PSO) are used to find the optimal tuning of PID parameters. The results of the 500-kw horizontal axis wind turbine show that Sugeno FIS has higher stability in output power generation than Mamdani FIS. Also, optimal HT3-FPIDC based on Mamdani FIS with PSO provides <strong>19.74 %</strong> lower absolute summation error (ASE) than Sugeno FIS in optimal HT2-FLC with PSO and <strong>39.03 %</strong> lower ASE than optimal HT1-FLC based on Sugeno FIS with PSO. Finally, the proposed optimal HT3-FPIDC based on PSO and Mamdani FIS provides the optimal results in terms of consistent output power generation at rated value.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"161 ","pages":"Pages 200-215"},"PeriodicalIF":6.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904421","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}

{"title":"Deep meta-domain-adversarial neural network for machinery fault diagnosis under multiple operating conditions","authors":"Binbei He,&nbsp;Huimin Wang,&nbsp;Wei-Wei Che","doi":"10.1016/j.isatra.2025.03.017","DOIUrl":"10.1016/j.isatra.2025.03.017","url":null,"abstract":"<div><div>This paper investigates the machinery fault diagnosis problem in the case of multiple operating conditions. Considering that the mechanical equipment may experience different operating conditions during operation, a meta-domain-adversarial neural network (MDANN) is established for improving the adaptability of transfer models in multi-target-domain fault diagnosis, which utilizes meta-learning technique to view the multi-target-domain fault diagnosis as multiple tasks. In the data preprocessing stage, the continuous deep belief network is selected for outliers removal. Furthermore, to make the MDANN model available for the partial domain multi-target-domain fault diagnosis problem, a new group-wise comparison approach is proposed. Compared with the existing results, the proposed MDANN allows one trained fault diagnosis model to cope with different operating conditions, and it can be extended to address scenarios where the label spaces in the source and target domains are different. Finally, the proposed fault diagnosis method is experimented on the bearing datasets and compared with several state-of-the-art approaches, which proves its superiority and effectiveness.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"161 ","pages":"Pages 36-47"},"PeriodicalIF":6.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789469","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}

{"title":"Cost optimization for Computer Numerical Control machining workshop: A queueing modeling approach using the meta-heuristic techniques","authors":"Parmeet Kaur Chahal,&nbsp;Kamlesh Kumar","doi":"10.1016/j.isatra.2025.03.018","DOIUrl":"10.1016/j.isatra.2025.03.018","url":null,"abstract":"<div><div>This research paper introduces an innovative approach to optimize repair of failed Computer Numerical Control (CNC) machines within CNC machining workshops by leveraging queueing theory. The proposed model addresses a spectrum of real-world scenarios encountered in manufacturing environments, including CNC machine failures, robotic server breakdowns, reneging behavior of failed CNC machines, and mechanisms to handle unsatisfactory repairs. In this system, failed CNC machines are attended by a single robotic server following a first come first served protocol, while also accounting for potential breakdowns of the robotic server during servicing. The arrival of failed CNC machines is regulated using the <span><math><mi>F</mi></math></span>-policy, and repairs to the robotic server are conducted following Bernoulli’s <span><math><mi>p</mi></math></span>-phases under a threshold recovery (<span><math><mi>Q</mi></math></span>) policy. Through the development of steady-state equations of a system and their solutions through matrix-analytic techniques, the distribution of queue sizes within the system is derived. Numerical results are presented graphically to illustrate the influence of various parameters on overall system performance. Additionally, sensitivity analysis on total expected costs are conducted to assess the impact of parameter variations. To optimize system costs, three meta-heuristic approaches are employed: Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Flower Pollination Algorithm(FPA). Comparative analysis of these techniques’ performances is conducted using data generated through their application. This novel combination of theoretical modeling, numerical analysis, and meta-heuristic optimization offers a comprehensive framework for enhancing efficiency and cost-effectiveness in CNC machining workshops.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"161 ","pages":"Pages 178-199"},"PeriodicalIF":6.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789449","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}

{"title":"Switching dynamic event-triggered disturbance rejection control for uncertain Lipschitz nonlinear system using recursive observers","authors":"Guanghao Su ,&nbsp;Zhenlei Wang","doi":"10.1016/j.isatra.2025.02.012","DOIUrl":"10.1016/j.isatra.2025.02.012","url":null,"abstract":"<div><div>This paper investigates the event-triggered disturbance rejection control problem for a class of uncertain Lipschitz nonlinear systems with time-varying disturbances. The design process to address this problem involves the construction of a state and disturbance joint observer, along with the design of an event-triggered controller that incorporates a designable minimum inter-event time. To design the joint observer, time-varying disturbances are initially transformed into multiple intermediate variables by employing a linear combination of system states and disturbance derivatives. Subsequently, the recursive observers are constructed to obtain intermediate variable estimations, while the recursive mechanism is utilized to compensate for the extra derivative term arising from disturbance transformation. Then, states and disturbance estimations are used to develop the event-triggered disturbance rejection controller. The event-triggered protocol includes a mandatory resting interval and a dynamic trigger variable that monotonically increases during the resting interval, ensuring a guaranteed minimum inter-event time while also decreasing the trigger frequency. Finally, we provide numerical examples to showcase the effectiveness of the proposed method. Simulation results show that the proposed method is able to reduce the steady-state error by 59.6% and extend the minimum trigger interval by at least 2.44 times compared to existing methods.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 103-112"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525557","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}

{"title":"Equivalent-input-disturbance based rotating control of drill-string in compound directional drilling process subject to coal seam variation","authors":"Wangnian Li ,&nbsp;Shipeng Chen ,&nbsp;Chengda Lu ,&nbsp;Lingfeng Mu ,&nbsp;Aoxue Yang ,&nbsp;Youzhen Zhang ,&nbsp;Haitao Song ,&nbsp;Ningping Yao ,&nbsp;Hongliang Tian ,&nbsp;Min Wu","doi":"10.1016/j.isatra.2025.02.011","DOIUrl":"10.1016/j.isatra.2025.02.011","url":null,"abstract":"<div><div>Coal seam variations in coal mine drilling can cause fluctuations in feed speed and rotational speed of the rotary head (ROR), reducing the performance of compound directional drilling control systems. This paper addresses these issues by considering the change in intrinsic specific energy caused by coal seam variations as a disturbance. An equivalent-input-disturbance (EID) based control system is designed to mitigate these effects. An axial-torsional coupled dynamic model of the drill-string system is developed, and EID and improved EID estimators are used to suppress the impact of bit–rock interaction. Field data from a gas extraction borehole is used to validate the effectiveness of the drill-string dynamics model. Simulation results demonstrate that the proposed control system surpasses the PI control system, the disturbance observer-based control system, and the active disturbance rejection control system. Specifically, the feed speed overshoot is reduced from 73%, 47%, and 46% to 33%, with settling time reduced from 3 s, 1.7 s, and 1.8 s to 1.5 s. The ROR overshoot decreases from 54%, 49%, and 43% to 11%, and settling time from 4.7 s, 3.1 s, and 2.8 s to 2.3 s. Additionally, feed speed and ROR fluctuations are minimized to 0.2% and 0.1%, respectively, demonstrating superior performance.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 352-363"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574943","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}

{"title":"Reinforcement learning-based trajectory tracking optimal control of unmanned surface vehicles in narrow water areas","authors":"Ziping Wei,&nbsp;Jialu Du","doi":"10.1016/j.isatra.2025.01.045","DOIUrl":"10.1016/j.isatra.2025.01.045","url":null,"abstract":"<div><div>For unmanned surface vehicles (USVs) navigating in narrow water areas in the presence of unknown dynamics and ocean environmental disturbances, this paper develops a reinforcement learning (RL)-based optimal control scheme for the trajectory tracking of USVs under motion state constraints. A nonlinear map is introduced to transform constrained motion state errors into bounded transformed errors, and then the motion state-constrained trajectory tracking problem of USVs is equivalently transformed into a boundedness problem of the transformed errors. Furthermore, an actor-critic framework is developed by utilizing adaptive neural networks (NNs). Within the actor-critic framework, a novel weight update law is designed for the critic NN by combining the gradient descent approach and the concurrent learning technology, thereby relaxing the persistent excitation condition required for adaptive critic NN weight updates. Subsequently, a disturbance compensator is designed and combined with the actor-critic framework to learn the trajectory tracking optimal control law for USVs in the presence of unknown dynamics and disturbances. Finally, theoretical analyses prove that the developed control scheme guarantees the boundedness of all signals in the USV closed-loop trajectory tracking control system, and simulation results show that the developed control scheme can make USVs track the desired trajectory in narrow water areas while reducing the energy consumption by approximately 14.6 % compared with an existing controller.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 152-164"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143371464","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}

{"title":"Minimum maximum regularized multiscale convolutional neural network and its application in intelligent fault diagnosis of rotary machines","authors":"Yaochun Wu ,&nbsp;Shaohua Du ,&nbsp;Guijun Wu ,&nbsp;Xiaobo Guo ,&nbsp;Jie Wu ,&nbsp;Rongzheng Zhao ,&nbsp;Chi Ma","doi":"10.1016/j.isatra.2025.01.044","DOIUrl":"10.1016/j.isatra.2025.01.044","url":null,"abstract":"<div><div>Convolutional neural networks (CNN) have achieved significant advancements in intelligent fault diagnosis of rotary machines. However, the limitations of using a single scale convolution kernel in convolutional layer and the exclusive focus on classification accuracy by the cross-entropy loss function during model training result in suboptimal diagnostic performance and generalization ability of CNNs in environments with strong background noise and imbalanced data. To address these challenges, a fault recognition method for rotary machines utilizing a minimum maximum regularized multiscale CNN (MMRMCNN) is proposed. A multiscale feature extraction module is devised, which uses convolutional layers with diverse scale kernels to capture multiscale features form input data. Additionally, a minimum maximum regularized objective function is introduced to augment the original cross-entropy loss function. This modification enables the model to consider not only recognition accuracy but also the compactness within classes and separation between classes of learning features during network training. The proposed approach effectively narrows the intra class margin of device health status features while widening the inter class margin, thereby mitigating the impact of noise and data imbalance on the mapping of health status relationship. Performance evaluation of the MMRMCNN is conducted using a measured dataset, the PU bearing dataset, and a rotor dataset. We found that the fault recognition accuracy of the proposed method exceeds 97.79 %, and the accuracy of fault recognition under noisy background and unbalanced data conditions is also above 94.81 % and 94.72 %, respectively. This demonstrate the superior fault recognition capabilities of the proposed method in scenarios characterized by strong background noise and data imbalance. Overall, the results attest to the exceptional performances of the developed MMRMCNN in fault recognition under challenging conditions, underscoring its potential in advancing the field of in Telligent fault diagnosis for rotary machines.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 1-21"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416466","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}

{"title":"A novel zonotopic Kalman filter-based actuator fault detection for time delay systems","authors":"Yu-Qing Ma ,&nbsp;Zi-Yun Wang ,&nbsp;Yan Wang ,&nbsp;Ju H. Park ,&nbsp;Zhi-Cheng Ji","doi":"10.1016/j.isatra.2025.01.037","DOIUrl":"10.1016/j.isatra.2025.01.037","url":null,"abstract":"<div><div>This paper addresses the problems of state estimation and actuator fault diagnosis in linear discrete time delay systems with actuator faults, and a zonotopic Kalman filter-based actuator fault detection (Z-KF-AFD) algorithm is proposed. First, the relationship between faults and states is calculated by approximating small-range noise. Next, by wrapping non-zero initial states with delays and zero initial faults within a zonotopic set, the zonotopic Kalman filter (ZKF) is iteratively derived to establish the association between current and delayed data. The optimal observer estimator gain for the optimal ZKF is then designed by minimizing the size of the zonotopic set. Subsequently, the fault zonotopic set, which links the state at the current time to the delayed state, is separated. Fault detection is achieved by determining whether zero lies within the bounds of the estimated fault zonotope. Finally, the feasibility of the proposed algorithm is validated through fault diagnosis examples involving a numerical system and a bidirectional DC–DC converter system.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"159 ","pages":"Pages 44-54"},"PeriodicalIF":6.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384049","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}