Pub Date : 2023-06-27DOI: 10.1177/00202940231180624
R. Jahantigh, S. Esmailifar, S. A. Sina
This paper proposes a control strategy to achieve minimum wake-induced power losses in a wind farm. At first, the axial-induction-based wake model is developed to consider the aerodynamic wake interactions among wind turbines. To optimize the generated power of the whole wind farm, the axial induction factor of each wind turbine is calculated by the genetic algorithm. As a supervisory controller, each wind turbine’s optimal axial induction factor calculated by the genetic algorithm is implemented as a setpoint of each wind turbine’s internal controller. In the internal control loop, a comprehensive controller is designed to track the commanded axial induction factor. In the partial load region, the commanded axial induction factor was attained by tuning the generator torque. In the transient and full load regions, the blade pitch angle is tuned to keep the generator speed and torque at the rated values. The performance of the proposed control strategy is investigated through case studies, including three different wind speeds and a time-varying wind speed case in a 3 × 3 wind-farm layout. The simulation results show the satisfactory performance of the proposed approach.
{"title":"Wind farm control and power curve optimization using induction-based wake model","authors":"R. Jahantigh, S. Esmailifar, S. A. Sina","doi":"10.1177/00202940231180624","DOIUrl":"https://doi.org/10.1177/00202940231180624","url":null,"abstract":"This paper proposes a control strategy to achieve minimum wake-induced power losses in a wind farm. At first, the axial-induction-based wake model is developed to consider the aerodynamic wake interactions among wind turbines. To optimize the generated power of the whole wind farm, the axial induction factor of each wind turbine is calculated by the genetic algorithm. As a supervisory controller, each wind turbine’s optimal axial induction factor calculated by the genetic algorithm is implemented as a setpoint of each wind turbine’s internal controller. In the internal control loop, a comprehensive controller is designed to track the commanded axial induction factor. In the partial load region, the commanded axial induction factor was attained by tuning the generator torque. In the transient and full load regions, the blade pitch angle is tuned to keep the generator speed and torque at the rated values. The performance of the proposed control strategy is investigated through case studies, including three different wind speeds and a time-varying wind speed case in a 3 × 3 wind-farm layout. The simulation results show the satisfactory performance of the proposed approach.","PeriodicalId":18375,"journal":{"name":"Measurement and Control","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73018722","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}
Pub Date : 2023-06-27DOI: 10.1177/00202940231180626
Chen Shuangxi
A coupled Two-Dimension Convolutional Neural Network-Gated Recurrent Unit (2DCNN-GRU) model is proposed to evaluate and predict the hunting instability of high-speed railway vehicles in this paper. First, vibration accelerations of four measuring points on the surface of the bogie frame of a high-speed railway vehicle in good working condition and with hunting instability are obtained through a line test and model simulation. The vibration acceleration data under different conditions is cut into many pieces at equal intervals. Low-frequency band-pass filtering is applied to each piece to obtain filtered vibration data, which is then analyzed separately to get a sample set of spectrum images, including short-time Fourier spectrum, Hilbert time-frequency-amplitude spectrum, and marginal spectrum. Then, a 2DCNN model is proposed to extract features by deeply studying the spectrum images of each piece of the filtered vibration data. The root-mean-square (RMS) of the vibration responses of four measuring points on the surface of the bogie frame and the mean value of the filtered vibration response envelope are calculated and recorded for each piece. The Hunting Instability Index (HII) is proposed by considering the weighted mean of RMS and the envelope mean of the filtered vibration responses to quantitatively get the extent of hunting instability. Finally, the GRU method is applied to predicting the dynamic change of HII indicators, and the effectiveness and accuracy of the method are verified by typical examples. One contribution of this work is proposing a method to evaluate the hunting motion by image identification of the short-time Fourier spectrum, Hilbert time-frequency-amplitude spectrum, and marginal spectrum of vibration signals, and another is the definition of HII based on 2DCNN and statistics.
{"title":"Prediction of hunting instability index of high-speed railway vehicles based on a coupled 2DCNN-GRU model","authors":"Chen Shuangxi","doi":"10.1177/00202940231180626","DOIUrl":"https://doi.org/10.1177/00202940231180626","url":null,"abstract":"A coupled Two-Dimension Convolutional Neural Network-Gated Recurrent Unit (2DCNN-GRU) model is proposed to evaluate and predict the hunting instability of high-speed railway vehicles in this paper. First, vibration accelerations of four measuring points on the surface of the bogie frame of a high-speed railway vehicle in good working condition and with hunting instability are obtained through a line test and model simulation. The vibration acceleration data under different conditions is cut into many pieces at equal intervals. Low-frequency band-pass filtering is applied to each piece to obtain filtered vibration data, which is then analyzed separately to get a sample set of spectrum images, including short-time Fourier spectrum, Hilbert time-frequency-amplitude spectrum, and marginal spectrum. Then, a 2DCNN model is proposed to extract features by deeply studying the spectrum images of each piece of the filtered vibration data. The root-mean-square (RMS) of the vibration responses of four measuring points on the surface of the bogie frame and the mean value of the filtered vibration response envelope are calculated and recorded for each piece. The Hunting Instability Index (HII) is proposed by considering the weighted mean of RMS and the envelope mean of the filtered vibration responses to quantitatively get the extent of hunting instability. Finally, the GRU method is applied to predicting the dynamic change of HII indicators, and the effectiveness and accuracy of the method are verified by typical examples. One contribution of this work is proposing a method to evaluate the hunting motion by image identification of the short-time Fourier spectrum, Hilbert time-frequency-amplitude spectrum, and marginal spectrum of vibration signals, and another is the definition of HII based on 2DCNN and statistics.","PeriodicalId":18375,"journal":{"name":"Measurement and Control","volume":"157 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77937553","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}
Pub Date : 2023-06-25DOI: 10.1177/00202940231180619
Yunhai Yan, Yu Guo, Xiaoqin Liu
The windowed synchronous averaging (WSA) is widely utilized for planetary structures. However, it cannot be applied for the fault detection of the planetary structure in the industrial robot rotate vector (RV) reducer. The robot usually works within a specified angle range, which causes the RV reducer rotates incompletely. To address this issue, an angle compensation local synchronous fitting scheme is proposed. To detect the localized planet gear fault in the RV reducer, the observed vibration is equi-angle resampled. And the synchronous interference contained in the resampled vibration is constructed and removed according to the angle compensation strategy. The residual data is used to construct the synthetic vibration of the planet gear. Then, the fault feature of the planet gear can be detected. Experiments on the RV reducer test rig under the robot running conditions support the effectiveness of the proposed scheme positively.
{"title":"Tooth root crack detection of planet gear in industrial robot RV reducer","authors":"Yunhai Yan, Yu Guo, Xiaoqin Liu","doi":"10.1177/00202940231180619","DOIUrl":"https://doi.org/10.1177/00202940231180619","url":null,"abstract":"The windowed synchronous averaging (WSA) is widely utilized for planetary structures. However, it cannot be applied for the fault detection of the planetary structure in the industrial robot rotate vector (RV) reducer. The robot usually works within a specified angle range, which causes the RV reducer rotates incompletely. To address this issue, an angle compensation local synchronous fitting scheme is proposed. To detect the localized planet gear fault in the RV reducer, the observed vibration is equi-angle resampled. And the synchronous interference contained in the resampled vibration is constructed and removed according to the angle compensation strategy. The residual data is used to construct the synthetic vibration of the planet gear. Then, the fault feature of the planet gear can be detected. Experiments on the RV reducer test rig under the robot running conditions support the effectiveness of the proposed scheme positively.","PeriodicalId":18375,"journal":{"name":"Measurement and Control","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83763181","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}
Pub Date : 2023-06-19DOI: 10.1177/00202940231180620
Jiangyue Wei, Xiaoxia He
Most works on Support Vector Regression (SVR) focus on kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius [Formula: see text]-tube, affording good predictive performance on datasets. However, the fixed radius limitation prevents the adaptive selection of support vectors according to the data distribution characteristics, compromising the performance of the SVR-based methods. Therefore, this study proposes an “Alterable [Formula: see text]-Support Vector Regression” ([Formula: see text]-SVR) model by applying a novel [Formula: see text], named “Alterable [Formula: see text],” to the SVR model. Based on the data point sparsity at each location, the model solves the different [Formula: see text] at the corresponding position, and thus zoom-in or zoom-out the [Formula: see text]-tube by changing its radius. Such a variable [Formula: see text]-tube strategy diminishes noise and outliers in the dataset, enhancing the prediction performance of the [Formula: see text]-SVR model. Therefore, we suggest a novel non-deterministic algorithm to iteratively solve the complex problem of optimizing [Formula: see text] associated with every location. Extensive experimental results demonstrate that our approach can improve the accuracy and stability on simulated and real data compared with the baseline methods.
{"title":"Support vector regression model with variant tolerance","authors":"Jiangyue Wei, Xiaoxia He","doi":"10.1177/00202940231180620","DOIUrl":"https://doi.org/10.1177/00202940231180620","url":null,"abstract":"Most works on Support Vector Regression (SVR) focus on kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius [Formula: see text]-tube, affording good predictive performance on datasets. However, the fixed radius limitation prevents the adaptive selection of support vectors according to the data distribution characteristics, compromising the performance of the SVR-based methods. Therefore, this study proposes an “Alterable [Formula: see text]-Support Vector Regression” ([Formula: see text]-SVR) model by applying a novel [Formula: see text], named “Alterable [Formula: see text],” to the SVR model. Based on the data point sparsity at each location, the model solves the different [Formula: see text] at the corresponding position, and thus zoom-in or zoom-out the [Formula: see text]-tube by changing its radius. Such a variable [Formula: see text]-tube strategy diminishes noise and outliers in the dataset, enhancing the prediction performance of the [Formula: see text]-SVR model. Therefore, we suggest a novel non-deterministic algorithm to iteratively solve the complex problem of optimizing [Formula: see text] associated with every location. Extensive experimental results demonstrate that our approach can improve the accuracy and stability on simulated and real data compared with the baseline methods.","PeriodicalId":18375,"journal":{"name":"Measurement and Control","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86990070","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}
Pub Date : 2023-06-17DOI: 10.1177/00202940231180830
Zexiang Zhao, Xinyu Zhao, Tianhao Wu
Measurement principle of axis straightness error of cylindrical parts was introduced based on their roundness profiles. The evaluation models of the center coordinates of four kinds of the reference circles were built, and the evaluation models of the axis straightness errors were established by using the least-square and minimum zone criteria. The roundness profiles of eight simulated cylinders, eight holes, and eight shafts were extracted, and their axis straightness errors were evaluated based on the different reference circles and evaluation criteria. The “minimax” issues in the evaluation process of axis straightness errors were be solved by using Equilibrium Optimizer, and its implementation flows were given. Their evaluation results were analyzed under the used reference circles and evaluation criteria. The analysis results showed that both reference circles and evaluation criteria have much influence on the evaluation results, and that among the evaluation results based on the centers’ coordinates of four reference circles and two evaluation criteria, the axis straightness errors evaluated based on the center coordinates of least-square reference circle and the minimum zone criteria is the least one for most of cylindrical parts, the roundness profiles of which may have no singularities, and the differences among the axis straightness errors evaluated on the basis of different reference circles and different evaluation criteria are very large sometimes, which should be noted in checking whether the axis straightness errors of parts are qualified.
{"title":"Influence of reference circles on the evaluation results of axis straightness errors","authors":"Zexiang Zhao, Xinyu Zhao, Tianhao Wu","doi":"10.1177/00202940231180830","DOIUrl":"https://doi.org/10.1177/00202940231180830","url":null,"abstract":"Measurement principle of axis straightness error of cylindrical parts was introduced based on their roundness profiles. The evaluation models of the center coordinates of four kinds of the reference circles were built, and the evaluation models of the axis straightness errors were established by using the least-square and minimum zone criteria. The roundness profiles of eight simulated cylinders, eight holes, and eight shafts were extracted, and their axis straightness errors were evaluated based on the different reference circles and evaluation criteria. The “minimax” issues in the evaluation process of axis straightness errors were be solved by using Equilibrium Optimizer, and its implementation flows were given. Their evaluation results were analyzed under the used reference circles and evaluation criteria. The analysis results showed that both reference circles and evaluation criteria have much influence on the evaluation results, and that among the evaluation results based on the centers’ coordinates of four reference circles and two evaluation criteria, the axis straightness errors evaluated based on the center coordinates of least-square reference circle and the minimum zone criteria is the least one for most of cylindrical parts, the roundness profiles of which may have no singularities, and the differences among the axis straightness errors evaluated on the basis of different reference circles and different evaluation criteria are very large sometimes, which should be noted in checking whether the axis straightness errors of parts are qualified.","PeriodicalId":18375,"journal":{"name":"Measurement and Control","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85388189","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}
Pub Date : 2023-06-12DOI: 10.1177/00202940231180842
Hui Ding, D. Qian, Sukgyu Lee, Linlin Zhu
Since coronary artery disease is the leading global cause of mortality and morbidity, this paper investigates the chaos suppression of coronary artery systems. The motivation of the paper is to discuss and analyze coronary artery disease in the field of dynamics. Firstly, the mathematic model of coronary artery systems is formulated and the properties of this model are illustrated by bifurcation diagram, information entropy analysis, phase plane trajectory, and Poincaré section. With regard to the uncertainties of coronary artery systems, the disturbance observer technique is adopted. Meanwhile, the smooth second-order sliding mode controller is designed to suppress the chaos phenomenon. In light of the combination of the controller and observer, the stability of such a closed-loop system is proven in the sense of Lyapunov. Finally, some numerical simulations demonstrate the feasibility and validity of such design.
{"title":"Sliding-mode-based chaos suppression of coronary artery systems","authors":"Hui Ding, D. Qian, Sukgyu Lee, Linlin Zhu","doi":"10.1177/00202940231180842","DOIUrl":"https://doi.org/10.1177/00202940231180842","url":null,"abstract":"Since coronary artery disease is the leading global cause of mortality and morbidity, this paper investigates the chaos suppression of coronary artery systems. The motivation of the paper is to discuss and analyze coronary artery disease in the field of dynamics. Firstly, the mathematic model of coronary artery systems is formulated and the properties of this model are illustrated by bifurcation diagram, information entropy analysis, phase plane trajectory, and Poincaré section. With regard to the uncertainties of coronary artery systems, the disturbance observer technique is adopted. Meanwhile, the smooth second-order sliding mode controller is designed to suppress the chaos phenomenon. In light of the combination of the controller and observer, the stability of such a closed-loop system is proven in the sense of Lyapunov. Finally, some numerical simulations demonstrate the feasibility and validity of such design.","PeriodicalId":18375,"journal":{"name":"Measurement and Control","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88385119","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}
Pub Date : 2023-06-08DOI: 10.1177/00202940231174427
Ahmad M. Alshamrani, Ibrahim M. Hezam
Although most countries of the world seek to improve their performance to achieve prosperity for their citizens, the performance of some other countries is still disappointing and has witnessed a deterioration in recent years due to civil wars, poverty, and failure to identify shortcomings and work on them. Many global indexes are concerned with ranking and evaluating the performance of countries, the most prominent of which is the Legatum prosperity index. This study presents a novel tool based on the MCDM approach under uncertainty. Twelve pillars were considered as criteria for evaluating the performance of the 19 poorest-performing countries globally, according to the 2021 Legatum prosperity index. The rough-entropy and rough-TOPSIS methods were used to assess the performance of countries and analyze the pillars of prosperity to determine their shortcomings. Further, a comparison with the 2021 Legatum prosperity index and sensitivity analysis is conducted to validate the obtained results. This study analyses and identifies that South Sudan gets the worst ranking while Cameroon is the best. Likewise, the results indicate that the worst performance pillar was the governance pillar, while the best pillar was the health pillar at the level of the studied countries. The proposed approach is essential for researchers working on performance measurement and ranking, as it ensures the robustness and realism of the results. It also gives a glimpse to the leaders of the countries about the actual situation of their countries to work on addressing the failures. In addition, it makes a significant contribution to the current scientific knowledge by providing a novel tool for evaluating performance indexes.
{"title":"Integrating rough-entropy and rough-TOPSIS methods for evaluating the legatum prosperity pillars of weakest performing countries","authors":"Ahmad M. Alshamrani, Ibrahim M. Hezam","doi":"10.1177/00202940231174427","DOIUrl":"https://doi.org/10.1177/00202940231174427","url":null,"abstract":"Although most countries of the world seek to improve their performance to achieve prosperity for their citizens, the performance of some other countries is still disappointing and has witnessed a deterioration in recent years due to civil wars, poverty, and failure to identify shortcomings and work on them. Many global indexes are concerned with ranking and evaluating the performance of countries, the most prominent of which is the Legatum prosperity index. This study presents a novel tool based on the MCDM approach under uncertainty. Twelve pillars were considered as criteria for evaluating the performance of the 19 poorest-performing countries globally, according to the 2021 Legatum prosperity index. The rough-entropy and rough-TOPSIS methods were used to assess the performance of countries and analyze the pillars of prosperity to determine their shortcomings. Further, a comparison with the 2021 Legatum prosperity index and sensitivity analysis is conducted to validate the obtained results. This study analyses and identifies that South Sudan gets the worst ranking while Cameroon is the best. Likewise, the results indicate that the worst performance pillar was the governance pillar, while the best pillar was the health pillar at the level of the studied countries. The proposed approach is essential for researchers working on performance measurement and ranking, as it ensures the robustness and realism of the results. It also gives a glimpse to the leaders of the countries about the actual situation of their countries to work on addressing the failures. In addition, it makes a significant contribution to the current scientific knowledge by providing a novel tool for evaluating performance indexes.","PeriodicalId":18375,"journal":{"name":"Measurement and Control","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87208403","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}
Pub Date : 2023-06-07DOI: 10.1177/00202940231173748
Zhao Zhengtian, Rui Zhiyuan, Duan Xiaoyan
Feature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature selection methods. However, traditional pattern classification methods such as Fisher Score, Laplacian Score, and relief use class labels inadequately. Previous information theory based feature selection methods such as MIFS ignore the intra-class to tight inter-class to sparse property of the samples. To address these problems, a feature selection algorithm for the binary classification problem is proposed, which is based on class label transformation using self-organizing mapping neural network (SOM) and cohesive hierarchical clustering. The algorithm first converts class labels without numerical meaning into numerical values that can participate in operations and retain classification information through class label mapping, and constitutes a two-dimensional vector from it and the attribute values to be judged. Then, these two-dimensional vectors are clustered by using SOM neural network and hierarchical clustering. Finally, evaluation function value is calculated, that is closely related to intra-cluster to tightness, inter-cluster separation, and division accuracy after clustering, and is used to evaluate the ability of alternative attributes to distinguish between classes. It is experimentally verified that the algorithm is robust and can effectively screen attributes with strong classification ability and improve the prediction performance of the classifier.
{"title":"Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering","authors":"Zhao Zhengtian, Rui Zhiyuan, Duan Xiaoyan","doi":"10.1177/00202940231173748","DOIUrl":"https://doi.org/10.1177/00202940231173748","url":null,"abstract":"Feature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature selection methods. However, traditional pattern classification methods such as Fisher Score, Laplacian Score, and relief use class labels inadequately. Previous information theory based feature selection methods such as MIFS ignore the intra-class to tight inter-class to sparse property of the samples. To address these problems, a feature selection algorithm for the binary classification problem is proposed, which is based on class label transformation using self-organizing mapping neural network (SOM) and cohesive hierarchical clustering. The algorithm first converts class labels without numerical meaning into numerical values that can participate in operations and retain classification information through class label mapping, and constitutes a two-dimensional vector from it and the attribute values to be judged. Then, these two-dimensional vectors are clustered by using SOM neural network and hierarchical clustering. Finally, evaluation function value is calculated, that is closely related to intra-cluster to tightness, inter-cluster separation, and division accuracy after clustering, and is used to evaluate the ability of alternative attributes to distinguish between classes. It is experimentally verified that the algorithm is robust and can effectively screen attributes with strong classification ability and improve the prediction performance of the classifier.","PeriodicalId":18375,"journal":{"name":"Measurement and Control","volume":"17 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72598435","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}
Pub Date : 2023-05-23DOI: 10.1177/00202940231173629
Yijun Guo
In this article, the problem of robust tracking control for wheeled mobile robot (WMR) with skidding, slipping, and parameter uncertainties is addressed. Through designing double sliding mode variables, a fixed time disturbance observer is developed to estimate the multiple disturbances within a fixed time, and the convergence time is regardless of the initial estimation error of the system. Based on the fixed-time disturbance observer (FTDOB) and the novel power reaching law sliding mode technique, a robust tracking controller is synthesized. The proposed control method eliminates the chattering problem existing in the traditional sliding mode control, and can guarantee the high-precision tracking control performance even in the presence of skidding, slipping and parameter uncertainties. The closed-loop system stability analysis is verified by the Lyapunov stability theory. Meanwhile, simulation comparative results are carried out to illustrate the effectiveness of the proposed control method.
{"title":"Fixed-time disturbance observer based robust tracking control of wheeled mobile robot with multiple disturbances","authors":"Yijun Guo","doi":"10.1177/00202940231173629","DOIUrl":"https://doi.org/10.1177/00202940231173629","url":null,"abstract":"In this article, the problem of robust tracking control for wheeled mobile robot (WMR) with skidding, slipping, and parameter uncertainties is addressed. Through designing double sliding mode variables, a fixed time disturbance observer is developed to estimate the multiple disturbances within a fixed time, and the convergence time is regardless of the initial estimation error of the system. Based on the fixed-time disturbance observer (FTDOB) and the novel power reaching law sliding mode technique, a robust tracking controller is synthesized. The proposed control method eliminates the chattering problem existing in the traditional sliding mode control, and can guarantee the high-precision tracking control performance even in the presence of skidding, slipping and parameter uncertainties. The closed-loop system stability analysis is verified by the Lyapunov stability theory. Meanwhile, simulation comparative results are carried out to illustrate the effectiveness of the proposed control method.","PeriodicalId":18375,"journal":{"name":"Measurement and Control","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80840899","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}
Flexible job shops motivated by small batches and multiple orders require the collaboration of machines and automated guided vehicles (AGVs) scheduling to boost shop floor flexibility and productivity. The joint scheduling of machines and AGVs can better achieve global optimization. However, joint scheduling requires two NP hard problems to be solved simultaneously. Therefore, this paper employs a multi-AGV flexible job shop scheduling problem (MA-FJSP) with an effective hybrid algorithm. First of all, a model is established with the objectives of minimizing the makespan, the total AGV running time and the total machine load. To solve the MA-FJSP, high-quality initialization methods and improved elite strategies are designed to improve global convergence in the proposed algorithm. In addition, a problem-knowledge-based neighborhood search is integrated to improve its exploitation capability. At last, a series of comparative experimental studies were performed to exam the effectiveness of the improved algorithm. The results demonstrate that the solutions gained by the proposed algorithm perform well in respect of convergence, diversity and distribution.
{"title":"An effective hybrid algorithm for joint scheduling of machines and AGVs in flexible job shop","authors":"Xiaoyu Wen, Yunzhan Fu, Wenchao Yang, Haoqi Wang, Yuyan Zhang, Chunya Sun","doi":"10.1177/00202940231173750","DOIUrl":"https://doi.org/10.1177/00202940231173750","url":null,"abstract":"Flexible job shops motivated by small batches and multiple orders require the collaboration of machines and automated guided vehicles (AGVs) scheduling to boost shop floor flexibility and productivity. The joint scheduling of machines and AGVs can better achieve global optimization. However, joint scheduling requires two NP hard problems to be solved simultaneously. Therefore, this paper employs a multi-AGV flexible job shop scheduling problem (MA-FJSP) with an effective hybrid algorithm. First of all, a model is established with the objectives of minimizing the makespan, the total AGV running time and the total machine load. To solve the MA-FJSP, high-quality initialization methods and improved elite strategies are designed to improve global convergence in the proposed algorithm. In addition, a problem-knowledge-based neighborhood search is integrated to improve its exploitation capability. At last, a series of comparative experimental studies were performed to exam the effectiveness of the improved algorithm. The results demonstrate that the solutions gained by the proposed algorithm perform well in respect of convergence, diversity and distribution.","PeriodicalId":18375,"journal":{"name":"Measurement and Control","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88592015","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}
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