Pub Date : 2024-07-27DOI: 10.1177/01423312241267061
Weiping Wang, Zhou Xinyi, Lu Shun
An auxiliary variable-based output feedback method is constructed in this paper. To obtain the state estimates, an auxiliary variable-based state observer is presented. Instead of calculating the estimates indirectly via the estimation dynamic, the distinguishing characteristic of the proposed observer lies in its ability to directly derive estimates by simply applying a low-pass filter to the observer. Therefore, the proposed observer is similar as a filter, which is more intuitive and concise in terms of structure and parameter tuning. Then, a backstepping-free controller is constructed based on the estimation results, and only one step is required. To facilitate the design procedure, the desired compensation approach is applied both in the observer and the controller. Utilizing the Lyapunov method, the system stability is assured, demonstrating that the presented controller excels in precise tracking tasks despite the presence of time-varying uncertainties. The feasibility of this approach is further corroborated through comparative results.
{"title":"Auxiliary variable-based output feedback control for hydraulic servo systems with desired compensation approach","authors":"Weiping Wang, Zhou Xinyi, Lu Shun","doi":"10.1177/01423312241267061","DOIUrl":"https://doi.org/10.1177/01423312241267061","url":null,"abstract":"An auxiliary variable-based output feedback method is constructed in this paper. To obtain the state estimates, an auxiliary variable-based state observer is presented. Instead of calculating the estimates indirectly via the estimation dynamic, the distinguishing characteristic of the proposed observer lies in its ability to directly derive estimates by simply applying a low-pass filter to the observer. Therefore, the proposed observer is similar as a filter, which is more intuitive and concise in terms of structure and parameter tuning. Then, a backstepping-free controller is constructed based on the estimation results, and only one step is required. To facilitate the design procedure, the desired compensation approach is applied both in the observer and the controller. Utilizing the Lyapunov method, the system stability is assured, demonstrating that the presented controller excels in precise tracking tasks despite the presence of time-varying uncertainties. The feasibility of this approach is further corroborated through comparative results.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.1177/01423312241260917
Sayed Reza Mohamadi, S. Khorashadizadeh
Time-varying impedance control is pivotal in shaping the dynamics of both patients and robots concurrently, facilitating tailored training for rehabilitation within human–robot interaction (HRI) scenarios, particularly for exoskeleton robots. Given the diverse physical characteristics of patients, sudden movement variations can pose challenges, potentially disrupting the robot’s functionality. Moreover, the inherent dynamics of robots coupled with uncertainties present additional hurdles for ensuring optimal and safe rehabilitation exercises. In this study, we introduce a novel approach: fuzzy adaptive time-varying impedance control, adept at mitigating external disturbances and addressing all uncertainties associated with both robot and patient dynamics, thereby ensuring safe and effective rehabilitation protocols. A primary concern with time-varying impedance control lies in system stability. Leveraging Lyapunov stability analysis, we delineate the safe operational boundaries of time-varying impedance control, thus averting potential instability. Our proposed impedance modulation facilitates desired dynamics while facilitating passive and isometric exercises for patients. Through simulations conducted in MATLAB2023, we demonstrate the efficacy of our approach, comparing its performance against conventional constant impedance control methods and also we used the controller for three different patients with various physical features that shows good results for all of them.
{"title":"Adaptive fuzzy control of time-varying impedance in rehabilitation exercises","authors":"Sayed Reza Mohamadi, S. Khorashadizadeh","doi":"10.1177/01423312241260917","DOIUrl":"https://doi.org/10.1177/01423312241260917","url":null,"abstract":"Time-varying impedance control is pivotal in shaping the dynamics of both patients and robots concurrently, facilitating tailored training for rehabilitation within human–robot interaction (HRI) scenarios, particularly for exoskeleton robots. Given the diverse physical characteristics of patients, sudden movement variations can pose challenges, potentially disrupting the robot’s functionality. Moreover, the inherent dynamics of robots coupled with uncertainties present additional hurdles for ensuring optimal and safe rehabilitation exercises. In this study, we introduce a novel approach: fuzzy adaptive time-varying impedance control, adept at mitigating external disturbances and addressing all uncertainties associated with both robot and patient dynamics, thereby ensuring safe and effective rehabilitation protocols. A primary concern with time-varying impedance control lies in system stability. Leveraging Lyapunov stability analysis, we delineate the safe operational boundaries of time-varying impedance control, thus averting potential instability. Our proposed impedance modulation facilitates desired dynamics while facilitating passive and isometric exercises for patients. Through simulations conducted in MATLAB2023, we demonstrate the efficacy of our approach, comparing its performance against conventional constant impedance control methods and also we used the controller for three different patients with various physical features that shows good results for all of them.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.1177/01423312241266680
Nandhini M, Mohamed Rabik M
Enhancing road capacity, safety, and energy efficiency is a potential outcome of vehicle platooning. Since platooning involves driving close to each other, it is essential to have minimal stopping distance (SD) during emergency braking. However, the anti-lock braking system (ABS) in a vehicle and unknown road type would further increase the SD. For this, a novel spacing policy using extremum-seeking control (ESC) estimated ABS has been proposed in this paper. An optimal slip ratio of that particular road type can be tracked and found using ESC estimation to maintain the maximum friction in ABS for all road conditions to have a minimal SD. The primary objective is to minimize the inter-gap distance while incorporating the ABS features. The simulation and experimentation of ABS for the set of non-linear vehicles on different road conditions have been carried out and numerical results have been compared with conventional ABS systems. The results show that the ABS with ESC estimation reduces the SD by seeking the optimal slip ratio and a new spacing policy for the platoon has been expressed using regression analysis. The entire simulated scenario has been implemented in hardware to validate the proposed model as a quarter-car model.
{"title":"A new spacing policy in a platoon using extremum-seeking controller on an anti-lock braking system","authors":"Nandhini M, Mohamed Rabik M","doi":"10.1177/01423312241266680","DOIUrl":"https://doi.org/10.1177/01423312241266680","url":null,"abstract":"Enhancing road capacity, safety, and energy efficiency is a potential outcome of vehicle platooning. Since platooning involves driving close to each other, it is essential to have minimal stopping distance (SD) during emergency braking. However, the anti-lock braking system (ABS) in a vehicle and unknown road type would further increase the SD. For this, a novel spacing policy using extremum-seeking control (ESC) estimated ABS has been proposed in this paper. An optimal slip ratio of that particular road type can be tracked and found using ESC estimation to maintain the maximum friction in ABS for all road conditions to have a minimal SD. The primary objective is to minimize the inter-gap distance while incorporating the ABS features. The simulation and experimentation of ABS for the set of non-linear vehicles on different road conditions have been carried out and numerical results have been compared with conventional ABS systems. The results show that the ABS with ESC estimation reduces the SD by seeking the optimal slip ratio and a new spacing policy for the platoon has been expressed using regression analysis. The entire simulated scenario has been implemented in hardware to validate the proposed model as a quarter-car model.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Object detection is an important problem in the field of computer vision, and feature fusion and bounding box regression are indispensable in mainstream object detection approaches. However, some detectors adopt Feature Pyramid Network, which increases training and detection time. In terms of the regression loss function, some recent techniques based on Intersection over Union (IoU) loss have negative effects on bounding box regression. To overcome these shortcomings, we propose Selective Feature Block (SFBlock) and Joint IoU (JIoU) loss in this article. The proposed SFBlock adaptively selects the features extracted from the Backbone and fuses them into a new feature. We add a penalty term of the intersection area between the prediction box and the target box on Generalized IoU (GIoU) loss to solve the problem that GIoU loss degenerates into IoU loss when the prediction box and the target box are surrounded by each other. A large number of ablation experiments and comparative experiments are carried out to prove the effectiveness of the proposed methods on various models and datasets.
{"title":"Selective feature block and joint IoU loss for object detection","authors":"Junyi Wang, Ruzhao Hua, Xuezheng Jiang, Kechen Song, Qinggang Meng, Mohamad Saada","doi":"10.1177/01423312241261087","DOIUrl":"https://doi.org/10.1177/01423312241261087","url":null,"abstract":"Object detection is an important problem in the field of computer vision, and feature fusion and bounding box regression are indispensable in mainstream object detection approaches. However, some detectors adopt Feature Pyramid Network, which increases training and detection time. In terms of the regression loss function, some recent techniques based on Intersection over Union (IoU) loss have negative effects on bounding box regression. To overcome these shortcomings, we propose Selective Feature Block (SFBlock) and Joint IoU (JIoU) loss in this article. The proposed SFBlock adaptively selects the features extracted from the Backbone and fuses them into a new feature. We add a penalty term of the intersection area between the prediction box and the target box on Generalized IoU (GIoU) loss to solve the problem that GIoU loss degenerates into IoU loss when the prediction box and the target box are surrounded by each other. A large number of ablation experiments and comparative experiments are carried out to prove the effectiveness of the proposed methods on various models and datasets.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.1177/01423312241262229
Bingao Chai, Shaowei Wang
The statically indeterminate characteristics of arch dams highlight the temperature deformation effect, making accurate modelling of this effect a key issue in improving the performance of displacement monitoring models. In this paper, causal interpretation ability and prediction accuracy of five kinds of temperature deformation modelling factors, including seasonal harmonic function, segmented average previous air temperature, air temperature hysteresis correction factor, principal components and shape feature clustering-based principal components of measured dam temperatures, are compared. On this basis, a combination prediction model is established using the above five causal models as submodels. The combination process is conducted by three methods of dynamic mutual information coefficient, random forest and support vector machine. Research results of the Jinping-I arch dam show that the shape feature clustering-based temperature principal components can significantly improve the accuracy and adaptability of displacement monitoring models, in which the root mean square error decreases with an average rate of 52%. The combination prediction model can effectively take the advantages of different kinds of temperature deformation modelling factors into account. Compared with the hydraulic-seasonal-time model and the best submodel, prediction accuracy of the support vector machine-based combination model is improved with an average rate of 54% and 28%, respectively.
{"title":"A combination model for displacement prediction of high arch dams stacking five kinds of temperature factors","authors":"Bingao Chai, Shaowei Wang","doi":"10.1177/01423312241262229","DOIUrl":"https://doi.org/10.1177/01423312241262229","url":null,"abstract":"The statically indeterminate characteristics of arch dams highlight the temperature deformation effect, making accurate modelling of this effect a key issue in improving the performance of displacement monitoring models. In this paper, causal interpretation ability and prediction accuracy of five kinds of temperature deformation modelling factors, including seasonal harmonic function, segmented average previous air temperature, air temperature hysteresis correction factor, principal components and shape feature clustering-based principal components of measured dam temperatures, are compared. On this basis, a combination prediction model is established using the above five causal models as submodels. The combination process is conducted by three methods of dynamic mutual information coefficient, random forest and support vector machine. Research results of the Jinping-I arch dam show that the shape feature clustering-based temperature principal components can significantly improve the accuracy and adaptability of displacement monitoring models, in which the root mean square error decreases with an average rate of 52%. The combination prediction model can effectively take the advantages of different kinds of temperature deformation modelling factors into account. Compared with the hydraulic-seasonal-time model and the best submodel, prediction accuracy of the support vector machine-based combination model is improved with an average rate of 54% and 28%, respectively.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An adaptive speed coordination control method based on Dempster–Shafer (D-S) evidence synthesis theory is proposed to achieve the speed coordination of the slave manipulator under the condition of a large transmission delay in space teleoperation. First, the D-S evidence synthesis theory is applied to transform the speed coordination rule method. The model for predicting the manipulator’s future state is given to gain confidence in each state. Subsequently, performance comparison experiments of D-S evidence synthesis control theory, cascade control, fuzzy control, and adaptive fuzzy control are completed on the 3-degree-of-freedom (3-DOF) manipulator simulation platform. Finally, according to the experimental results, the accuracy of D-S evidence synthesis theory is 7.49% better than cascade control, 16.84% better than fuzzy control, and 28.45% better than adaptive fuzzy control. The adaptability of D-S evidence synthesis theory is generally superior to cascade control, slightly inferior to fuzzy control and inferior to adaptive fuzzy control.
{"title":"A speed coordination control method based on D-S evidence synthesis theory","authors":"Wei Zhang, Feng Li, Junlin Li, Qinkun Cheng, Xiaoqian Zhang, Yansong Xu","doi":"10.1177/01423312241263395","DOIUrl":"https://doi.org/10.1177/01423312241263395","url":null,"abstract":"An adaptive speed coordination control method based on Dempster–Shafer (D-S) evidence synthesis theory is proposed to achieve the speed coordination of the slave manipulator under the condition of a large transmission delay in space teleoperation. First, the D-S evidence synthesis theory is applied to transform the speed coordination rule method. The model for predicting the manipulator’s future state is given to gain confidence in each state. Subsequently, performance comparison experiments of D-S evidence synthesis control theory, cascade control, fuzzy control, and adaptive fuzzy control are completed on the 3-degree-of-freedom (3-DOF) manipulator simulation platform. Finally, according to the experimental results, the accuracy of D-S evidence synthesis theory is 7.49% better than cascade control, 16.84% better than fuzzy control, and 28.45% better than adaptive fuzzy control. The adaptability of D-S evidence synthesis theory is generally superior to cascade control, slightly inferior to fuzzy control and inferior to adaptive fuzzy control.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.1177/01423312241265774
Dongxiao Hou, Bo Zhang, Jiahui Chen, Peiming Shi
The structure of the rolling mill system is complex and the operating conditions are changeable. Therefore, the interdependence between the data needs to be fully considered in the fault diagnosis of the rolling mill. Although graph neural network (GNN) is a powerful architecture based on non-Euclidean spatial data, the current method is difficult to represent the long-range dependence of rolling mill fault vibration signals. Simply increasing the depth of GNN is not enough to expand the receptive field of the model, because the larger GNN model may have the problem of gradient disappearance or transition smoothing. In order to solve the above problems, an improved graph neural network based on Graph-Transformer is proposed to diagnose the health status of rolling mill. This method first performs sliding maximum sampling on the spectrum of the original vibration signal to improve the frequency resolution and reduce the feature dimension. Second, the relationship between fault features is characterized by constructing affinity graph. Finally, the long-range dependency between paired features is learned through the readout module and the self-attention mechanism in Graph-Transformer and the diagnostic results are output by the classifier. The experimental results on the rolling mill platform show that this method can not only adapt to the changing working conditions of the rolling mill but also achieve excellent performance in the case of sample imbalance and strong noise.
{"title":"Improved GNN based on Graph-Transformer: A new framework for rolling mill bearing fault diagnosis","authors":"Dongxiao Hou, Bo Zhang, Jiahui Chen, Peiming Shi","doi":"10.1177/01423312241265774","DOIUrl":"https://doi.org/10.1177/01423312241265774","url":null,"abstract":"The structure of the rolling mill system is complex and the operating conditions are changeable. Therefore, the interdependence between the data needs to be fully considered in the fault diagnosis of the rolling mill. Although graph neural network (GNN) is a powerful architecture based on non-Euclidean spatial data, the current method is difficult to represent the long-range dependence of rolling mill fault vibration signals. Simply increasing the depth of GNN is not enough to expand the receptive field of the model, because the larger GNN model may have the problem of gradient disappearance or transition smoothing. In order to solve the above problems, an improved graph neural network based on Graph-Transformer is proposed to diagnose the health status of rolling mill. This method first performs sliding maximum sampling on the spectrum of the original vibration signal to improve the frequency resolution and reduce the feature dimension. Second, the relationship between fault features is characterized by constructing affinity graph. Finally, the long-range dependency between paired features is learned through the readout module and the self-attention mechanism in Graph-Transformer and the diagnostic results are output by the classifier. The experimental results on the rolling mill platform show that this method can not only adapt to the changing working conditions of the rolling mill but also achieve excellent performance in the case of sample imbalance and strong noise.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.1177/01423312241262079
J. Dieulot
Long Short-Term Memory (LSTM) neural networks are well suited for representing time series as, compared to other neural networks, their structure avoids vanishing or exploding gradients. LSTM has been embedded into Model Predictive Control algorithms in order to forecast the behavior of nonlinear systems. The new algorithm presented in the paper is of a different nature, as the LSTM network approximates the inverse of the system over a receding horizon and provides a sequence of future inputs as a function of a specified output trajectory. The main advantage of the method appears when the desired output trajectory is generated from a small set of parameters, for example, a convergence rate. The Model Predictive control optimizes its criterion with respect to this small set of variables, and the LSTM supplies the corresponding future control inputs. Eventually, the modeling error of the LSTM can be compensated by feeding the control sequence to the forward model and updating the controller according to the output deviation. The algorithm allows to design Model Predictive controllers for nonlinear systems in a generic way, using a very small number of decision variables even with a long receding horizon.
{"title":"Model Predictive Control based on Long-Term Memory neural network model inversion","authors":"J. Dieulot","doi":"10.1177/01423312241262079","DOIUrl":"https://doi.org/10.1177/01423312241262079","url":null,"abstract":"Long Short-Term Memory (LSTM) neural networks are well suited for representing time series as, compared to other neural networks, their structure avoids vanishing or exploding gradients. LSTM has been embedded into Model Predictive Control algorithms in order to forecast the behavior of nonlinear systems. The new algorithm presented in the paper is of a different nature, as the LSTM network approximates the inverse of the system over a receding horizon and provides a sequence of future inputs as a function of a specified output trajectory. The main advantage of the method appears when the desired output trajectory is generated from a small set of parameters, for example, a convergence rate. The Model Predictive control optimizes its criterion with respect to this small set of variables, and the LSTM supplies the corresponding future control inputs. Eventually, the modeling error of the LSTM can be compensated by feeding the control sequence to the forward model and updating the controller according to the output deviation. The algorithm allows to design Model Predictive controllers for nonlinear systems in a generic way, using a very small number of decision variables even with a long receding horizon.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.1177/01423312241262539
Longxiang Xiao, Zhibao Song
Motion control of mechatronic systems with uncertainties and physical constraints, while ensuring robustness and achieving better performance, such as high tracking accuracy and fast response, has always been a hot topic. However, the most current related works only focus on how to guarantee system stability under constraints, and few consider comprehensive performance. This paper investigates gated recurrent unit (GRU)-based compensation adaptive robust control (ARC) for uncertain linear motor–driven stage system with state and input constraints. To achieve rapid and precise motion control, a dual-loop control structure is employed, where GRU and ARC are the outer loop and the inner loop, respectively. First, the ARC control law is used to deal with the parameters uncertainty and external disturbances in the system, which further improves the tracking accuracy. A GRU neural network is then constructed and capable of implementing precise prediction ahead of the actual system output. Through choosing suitable loss function and training model, it can effectively minimize prediction error under state and input constraints. Comparative experiment results demonstrate the superiority and validity of the proposed scheme on the basis of GRU and ARC.
{"title":"Compensation adaptive robust control for a linear motor–driven stage system with state and input constraints based on gated recurrent unit architecture","authors":"Longxiang Xiao, Zhibao Song","doi":"10.1177/01423312241262539","DOIUrl":"https://doi.org/10.1177/01423312241262539","url":null,"abstract":"Motion control of mechatronic systems with uncertainties and physical constraints, while ensuring robustness and achieving better performance, such as high tracking accuracy and fast response, has always been a hot topic. However, the most current related works only focus on how to guarantee system stability under constraints, and few consider comprehensive performance. This paper investigates gated recurrent unit (GRU)-based compensation adaptive robust control (ARC) for uncertain linear motor–driven stage system with state and input constraints. To achieve rapid and precise motion control, a dual-loop control structure is employed, where GRU and ARC are the outer loop and the inner loop, respectively. First, the ARC control law is used to deal with the parameters uncertainty and external disturbances in the system, which further improves the tracking accuracy. A GRU neural network is then constructed and capable of implementing precise prediction ahead of the actual system output. Through choosing suitable loss function and training model, it can effectively minimize prediction error under state and input constraints. Comparative experiment results demonstrate the superiority and validity of the proposed scheme on the basis of GRU and ARC.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141800761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.1177/01423312241263140
Shubham Choudhary, M. Bajpai, K. Bharti
Depression is a severe neurological disorder characterized by a loss of interest and may lead to suicide. Electroencephalography (EEG) measurement is a non-invasive tool for neural electrical activities measurement which can be further used for different neurological disorder detection such as depression. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. This paper proposes a fisher score–based method for electrode ranking. This paper selects only those electrodes whose fisher score is greater than the mean of fisher scores of all electrodes. It results in a reduced set of electrodes. A deep learning–based model has been proposed which uses the reduced set of electrodes for depression detection. The performance of the proposed model is evaluated on two benchmark data sets having varying numbers of electrodes. The proposed model significantly reduces the number of electrodes to 68.42% and 60.93% for data sets 1 and 2, respectively. The accuracy of 98.73%, precision of 98.50%, recall of 98.75%, F1 score of 98.62% and AUC of 99.91% are obtained for data set 1 and accuracy of 95.48%, precision of 91.93%, recall of 96.11%, F1 score of 93.97% and AUC of 99.49% are obtained for data set 2.
{"title":"Electrode subset selection to lessen the complexity of brain activity measurement using EEG for depression detection","authors":"Shubham Choudhary, M. Bajpai, K. Bharti","doi":"10.1177/01423312241263140","DOIUrl":"https://doi.org/10.1177/01423312241263140","url":null,"abstract":"Depression is a severe neurological disorder characterized by a loss of interest and may lead to suicide. Electroencephalography (EEG) measurement is a non-invasive tool for neural electrical activities measurement which can be further used for different neurological disorder detection such as depression. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. This paper proposes a fisher score–based method for electrode ranking. This paper selects only those electrodes whose fisher score is greater than the mean of fisher scores of all electrodes. It results in a reduced set of electrodes. A deep learning–based model has been proposed which uses the reduced set of electrodes for depression detection. The performance of the proposed model is evaluated on two benchmark data sets having varying numbers of electrodes. The proposed model significantly reduces the number of electrodes to 68.42% and 60.93% for data sets 1 and 2, respectively. The accuracy of 98.73%, precision of 98.50%, recall of 98.75%, F1 score of 98.62% and AUC of 99.91% are obtained for data set 1 and accuracy of 95.48%, precision of 91.93%, recall of 96.11%, F1 score of 93.97% and AUC of 99.49% are obtained for data set 2.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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