Pub Date : 2024-02-16DOI: 10.1177/01423312241229492
Zhu Ma, Tianhong Pan
In the semiconductor manufacturing process, extended state observer (ESO)-based run-to-run (RtR) control is an intriguing solution. Although an ESO-RtR control strategy can effectively compensate for the lumped disturbance, appropriate gains are required. In this article, a cutting-edge deep reinforcement learning (DRL) technique is integrated into ESO-RtR, and a composite control framework of DRL-ESO-RtR is developed. In particular, the well-trained DRL agent serves as an assisted controller, which produces appropriate gains of ESO. The optimized ESO then presents a preferable control recipe for the manufacturing process. Under the RtR framework, the gain adjustment problem of ESO is formulated as a Markov decision process. An efficient state space and reward function are wisely designed using the system’s observable information. Correspondingly, the gain of the ESO is adaptively adjusted to cope with changing environmental disturbances. Finally, a twin-delayed deep deterministic policy gradient algorithm is employed to implement the suggested scheme. The feasibility and superiority of the developed method are validated in a deep reactive ion etching process. Comparative results demonstrate that the presented scheme outperforms the ordinary ESO-RtR controller in terms of disturbance rejection.
{"title":"Deep reinforcement learning-assisted extended state observer for run-to-run control in the semiconductor manufacturing process","authors":"Zhu Ma, Tianhong Pan","doi":"10.1177/01423312241229492","DOIUrl":"https://doi.org/10.1177/01423312241229492","url":null,"abstract":"In the semiconductor manufacturing process, extended state observer (ESO)-based run-to-run (RtR) control is an intriguing solution. Although an ESO-RtR control strategy can effectively compensate for the lumped disturbance, appropriate gains are required. In this article, a cutting-edge deep reinforcement learning (DRL) technique is integrated into ESO-RtR, and a composite control framework of DRL-ESO-RtR is developed. In particular, the well-trained DRL agent serves as an assisted controller, which produces appropriate gains of ESO. The optimized ESO then presents a preferable control recipe for the manufacturing process. Under the RtR framework, the gain adjustment problem of ESO is formulated as a Markov decision process. An efficient state space and reward function are wisely designed using the system’s observable information. Correspondingly, the gain of the ESO is adaptively adjusted to cope with changing environmental disturbances. Finally, a twin-delayed deep deterministic policy gradient algorithm is employed to implement the suggested scheme. The feasibility and superiority of the developed method are validated in a deep reactive ion etching process. Comparative results demonstrate that the presented scheme outperforms the ordinary ESO-RtR controller in terms of disturbance rejection.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"42 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139961639","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 : 2024-02-16DOI: 10.1177/01423312241229965
Jikun Zhu, Weili Xiong
For multiphase batch process, the characteristics of process data under various batches differ. Consequently, the soft sensor model built for a particular working condition is inapplicable to other working conditions. Besides, each batch can be divided into several phases whose characteristics are probably different. To address the above problems, a soft sensor model based on phase division and transfer learning strategy is proposed. First, transfer learning strategy is adopted to construct a soft sensor model applicable to various working conditions. Specifically, geodesic flow kernel based on linear local tangent space alignment (LLTSA-GFK) algorithm is designed. By projecting process data to the common manifold subspace, the distribution difference of data between various batches is reduced and the performance of the soft sensor model is enhanced. In addition, sequence-based fuzzy clustering and just-in-time learning (JITL) are adopted to solve the multistage characteristic for batch process. The root-mean-square error ( RMSE), coefficient of determination [Formula: see text], and mean absolute error ( MAE) are adopted to compare the conventional soft sensing approach (i.e., partial least-square regression based on JITL, support vector regression, and back propagation neural network) with the proposed approach. The superiority of the proposed model is verified by a fed-batch penicillin fermentation process.
{"title":"An improved transfer learning approach based on geodesic flow kernel for multiphase batch process soft sensor modeling","authors":"Jikun Zhu, Weili Xiong","doi":"10.1177/01423312241229965","DOIUrl":"https://doi.org/10.1177/01423312241229965","url":null,"abstract":"For multiphase batch process, the characteristics of process data under various batches differ. Consequently, the soft sensor model built for a particular working condition is inapplicable to other working conditions. Besides, each batch can be divided into several phases whose characteristics are probably different. To address the above problems, a soft sensor model based on phase division and transfer learning strategy is proposed. First, transfer learning strategy is adopted to construct a soft sensor model applicable to various working conditions. Specifically, geodesic flow kernel based on linear local tangent space alignment (LLTSA-GFK) algorithm is designed. By projecting process data to the common manifold subspace, the distribution difference of data between various batches is reduced and the performance of the soft sensor model is enhanced. In addition, sequence-based fuzzy clustering and just-in-time learning (JITL) are adopted to solve the multistage characteristic for batch process. The root-mean-square error ( RMSE), coefficient of determination [Formula: see text], and mean absolute error ( MAE) are adopted to compare the conventional soft sensing approach (i.e., partial least-square regression based on JITL, support vector regression, and back propagation neural network) with the proposed approach. The superiority of the proposed model is verified by a fed-batch penicillin fermentation process.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"48 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139961255","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 : 2024-02-14DOI: 10.1177/01423312241229039
Peng Gu, Zhongliang Jing, Liangbin Wu
In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness.
{"title":"A novel adaptive maximum correntropy cubature Kalman filter based on multiple fading factors","authors":"Peng Gu, Zhongliang Jing, Liangbin Wu","doi":"10.1177/01423312241229039","DOIUrl":"https://doi.org/10.1177/01423312241229039","url":null,"abstract":"In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"96 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139776890","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 : 2024-02-14DOI: 10.1177/01423312241229039
Peng Gu, Zhongliang Jing, Liangbin Wu
In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness.
{"title":"A novel adaptive maximum correntropy cubature Kalman filter based on multiple fading factors","authors":"Peng Gu, Zhongliang Jing, Liangbin Wu","doi":"10.1177/01423312241229039","DOIUrl":"https://doi.org/10.1177/01423312241229039","url":null,"abstract":"In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"40 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836394","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}
According to the robot’s walking motion characteristics, the position/force switching control is studied to realize the segmental control of the robot stroke. This stroke is controlled by position when the foot end of the robot descends from the suspension to the ground. To avoid excessive contact force when the robot touches the ground, force control is carried out when the foot touches the ground. Due to the force and position control methods and control parameters of the hydraulic quadruped robots are different, the precise mathematical model for the joint position control and joint force control of the leg joints of the hydraulic quadruped robot is established using the system identification method. A fuzzy multi-model switching algorithm is proposed to solve the problem of jumping and jitter of system parameters in the process of force/position switching. Through simulation and prototype experiments, fuzzy multi-model switching is compared with direct switching and multi-model switching, and the switching effect of the algorithm is verified.
{"title":"Research on force/position switching control of servo actuator for hydraulically driven joint robot","authors":"Bing-Tuan Gao, Yongkang Wang, Wenlong Han, Shilong Xue","doi":"10.1177/01423312241227096","DOIUrl":"https://doi.org/10.1177/01423312241227096","url":null,"abstract":"According to the robot’s walking motion characteristics, the position/force switching control is studied to realize the segmental control of the robot stroke. This stroke is controlled by position when the foot end of the robot descends from the suspension to the ground. To avoid excessive contact force when the robot touches the ground, force control is carried out when the foot touches the ground. Due to the force and position control methods and control parameters of the hydraulic quadruped robots are different, the precise mathematical model for the joint position control and joint force control of the leg joints of the hydraulic quadruped robot is established using the system identification method. A fuzzy multi-model switching algorithm is proposed to solve the problem of jumping and jitter of system parameters in the process of force/position switching. Through simulation and prototype experiments, fuzzy multi-model switching is compared with direct switching and multi-model switching, and the switching effect of the algorithm is verified.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"202 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139843153","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 : 2024-02-12DOI: 10.1177/01423312241227539
Donglei Dong, Xianbo Xiang, Jinjiang Li, Shaolong Yang
Due to the nonlinearity, strong coupling, and uncertain parameters of autonomous underwater vehicle (AUV), it is difficult to build an accurate dynamic model, which makes precise control of AUV extremely challenging. To handle the precise heading-following problem of AUV, this paper proposes an iterative learning-based redefine model-free adaptive heading control (IL-RMFAC) method for the underactuated AUV with unknown disturbances based on data driven. The control scheme consists of a learning control algorithm, a parameter iterative update algorithm, and a parameter reset algorithm. It is designed using only the input and output (I/O) data of the controlled system and is a data-driven control method. The pseudo partial derivative (PPD) can be iteratively calculated through the parameter iterative update algorithm and reset algorithm to adjust the learning gain, solving the problem of strictly limited initial position of the traditional fixed learning gain iterative learning control (ILC). A linear combination of angle and angular velocity is introduced in the kinematic layer to avoid overshooting of the expected following target, and an iterative learning method is introduced in the dynamics to improve the accuracy. As the number of iterations increases, the steady-state error is gradually decreased. Finally, by comparing traditional proportional–integral–derivative (PID) simulations, the proposed algorithm’s effectiveness and outstanding performance for the AUV heading tracking are confirmed.
{"title":"Iterative learning–based model-free adaptive precise heading following of an autonomous underwater vehicle with unknown disturbances","authors":"Donglei Dong, Xianbo Xiang, Jinjiang Li, Shaolong Yang","doi":"10.1177/01423312241227539","DOIUrl":"https://doi.org/10.1177/01423312241227539","url":null,"abstract":"Due to the nonlinearity, strong coupling, and uncertain parameters of autonomous underwater vehicle (AUV), it is difficult to build an accurate dynamic model, which makes precise control of AUV extremely challenging. To handle the precise heading-following problem of AUV, this paper proposes an iterative learning-based redefine model-free adaptive heading control (IL-RMFAC) method for the underactuated AUV with unknown disturbances based on data driven. The control scheme consists of a learning control algorithm, a parameter iterative update algorithm, and a parameter reset algorithm. It is designed using only the input and output (I/O) data of the controlled system and is a data-driven control method. The pseudo partial derivative (PPD) can be iteratively calculated through the parameter iterative update algorithm and reset algorithm to adjust the learning gain, solving the problem of strictly limited initial position of the traditional fixed learning gain iterative learning control (ILC). A linear combination of angle and angular velocity is introduced in the kinematic layer to avoid overshooting of the expected following target, and an iterative learning method is introduced in the dynamics to improve the accuracy. As the number of iterations increases, the steady-state error is gradually decreased. Finally, by comparing traditional proportional–integral–derivative (PID) simulations, the proposed algorithm’s effectiveness and outstanding performance for the AUV heading tracking are confirmed.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139783125","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 : 2024-02-12DOI: 10.1177/01423312241228886
Lintao Shi, Baoquan Li, Wuxi Shi
An adaptive image-based visual servoing (IBVS) controller is designed for a quadrotor unmanned aerial vehicle (UAV) to achieve robust tracking for moving targets, under underactuation and tight coupling constraints of UAV kinematics. Specifically, image features are selected from perspective image moments of a planar target to obtain virtual feature dynamics regarding UAV kinematics and dynamics. By constructing an auxiliary variable, a translational velocity observer for moving target is constructed by using virtual image features. An IBVS tracking controller is designed without target geometric information by combining UAV and visual feature dynamics. Designed controller and observer make the UAV robustly reach desired height and track the moving target, despite uncertainty of target movement. The controller has asymptotical convergence performance, and the target velocity is observed according to Lyapunov stability analysis. Simulation and experimental results show that the proposed method has smoother and more accurate performance in motion tracking and target velocity prediction under system uncertainty.
{"title":"Vision-based UAV adaptive tracking control for moving targets with velocity observation","authors":"Lintao Shi, Baoquan Li, Wuxi Shi","doi":"10.1177/01423312241228886","DOIUrl":"https://doi.org/10.1177/01423312241228886","url":null,"abstract":"An adaptive image-based visual servoing (IBVS) controller is designed for a quadrotor unmanned aerial vehicle (UAV) to achieve robust tracking for moving targets, under underactuation and tight coupling constraints of UAV kinematics. Specifically, image features are selected from perspective image moments of a planar target to obtain virtual feature dynamics regarding UAV kinematics and dynamics. By constructing an auxiliary variable, a translational velocity observer for moving target is constructed by using virtual image features. An IBVS tracking controller is designed without target geometric information by combining UAV and visual feature dynamics. Designed controller and observer make the UAV robustly reach desired height and track the moving target, despite uncertainty of target movement. The controller has asymptotical convergence performance, and the target velocity is observed according to Lyapunov stability analysis. Simulation and experimental results show that the proposed method has smoother and more accurate performance in motion tracking and target velocity prediction under system uncertainty.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"16 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139783679","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 : 2024-02-12DOI: 10.1177/01423312241228886
Lintao Shi, Baoquan Li, Wuxi Shi
An adaptive image-based visual servoing (IBVS) controller is designed for a quadrotor unmanned aerial vehicle (UAV) to achieve robust tracking for moving targets, under underactuation and tight coupling constraints of UAV kinematics. Specifically, image features are selected from perspective image moments of a planar target to obtain virtual feature dynamics regarding UAV kinematics and dynamics. By constructing an auxiliary variable, a translational velocity observer for moving target is constructed by using virtual image features. An IBVS tracking controller is designed without target geometric information by combining UAV and visual feature dynamics. Designed controller and observer make the UAV robustly reach desired height and track the moving target, despite uncertainty of target movement. The controller has asymptotical convergence performance, and the target velocity is observed according to Lyapunov stability analysis. Simulation and experimental results show that the proposed method has smoother and more accurate performance in motion tracking and target velocity prediction under system uncertainty.
{"title":"Vision-based UAV adaptive tracking control for moving targets with velocity observation","authors":"Lintao Shi, Baoquan Li, Wuxi Shi","doi":"10.1177/01423312241228886","DOIUrl":"https://doi.org/10.1177/01423312241228886","url":null,"abstract":"An adaptive image-based visual servoing (IBVS) controller is designed for a quadrotor unmanned aerial vehicle (UAV) to achieve robust tracking for moving targets, under underactuation and tight coupling constraints of UAV kinematics. Specifically, image features are selected from perspective image moments of a planar target to obtain virtual feature dynamics regarding UAV kinematics and dynamics. By constructing an auxiliary variable, a translational velocity observer for moving target is constructed by using virtual image features. An IBVS tracking controller is designed without target geometric information by combining UAV and visual feature dynamics. Designed controller and observer make the UAV robustly reach desired height and track the moving target, despite uncertainty of target movement. The controller has asymptotical convergence performance, and the target velocity is observed according to Lyapunov stability analysis. Simulation and experimental results show that the proposed method has smoother and more accurate performance in motion tracking and target velocity prediction under system uncertainty.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"147 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139843386","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 : 2024-02-12DOI: 10.1177/01423312241227252
Mehmet Fatih Çelebi, Asli Tuncay Atalay, Ozgur Atalay, Veysel Gazi
This work proposes a textile-dominated wearable actuating system utilizing textile force sensor feedback. The study explores the liquid/gas phase transition behaviour of low boiling point liquids to develop a thermally driven fluidic soft actuator. The research also focuses on obtaining feedback through capacitive textile force sensors and developing a feedback control law for a single actuator as well as sequential actuation of multiple actuators. The findings demonstrate that the proposed actuators produce the desired pressure level utilized in mechanotherapy applications. Moreover, high accuracy is achieved by the capacitive textile force sensors specifically designed for detecting the applied force exerted by the textile-based actuators. The developed system constitutes a comprehensive textile-based system encompassing heating, actuation and sensing capabilities. Following the calibration of the developed system in conjunction with its sensor, a pilot-scale implementation of sequential massage application was conducted to showcase the system’s capabilities and potential. Considering its pressure and heating properties, the developed system exhibits a great potential for utilization in mechanotherapy as well as in thermotherapy applications.
{"title":"Design, modelling and control of a textile-based wearable actuating system with sensor feedback for therapeutic applications","authors":"Mehmet Fatih Çelebi, Asli Tuncay Atalay, Ozgur Atalay, Veysel Gazi","doi":"10.1177/01423312241227252","DOIUrl":"https://doi.org/10.1177/01423312241227252","url":null,"abstract":"This work proposes a textile-dominated wearable actuating system utilizing textile force sensor feedback. The study explores the liquid/gas phase transition behaviour of low boiling point liquids to develop a thermally driven fluidic soft actuator. The research also focuses on obtaining feedback through capacitive textile force sensors and developing a feedback control law for a single actuator as well as sequential actuation of multiple actuators. The findings demonstrate that the proposed actuators produce the desired pressure level utilized in mechanotherapy applications. Moreover, high accuracy is achieved by the capacitive textile force sensors specifically designed for detecting the applied force exerted by the textile-based actuators. The developed system constitutes a comprehensive textile-based system encompassing heating, actuation and sensing capabilities. Following the calibration of the developed system in conjunction with its sensor, a pilot-scale implementation of sequential massage application was conducted to showcase the system’s capabilities and potential. Considering its pressure and heating properties, the developed system exhibits a great potential for utilization in mechanotherapy as well as in thermotherapy applications.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"58 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139784749","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 : 2024-02-12DOI: 10.1177/01423312241227252
Mehmet Fatih Çelebi, Asli Tuncay Atalay, Ozgur Atalay, Veysel Gazi
This work proposes a textile-dominated wearable actuating system utilizing textile force sensor feedback. The study explores the liquid/gas phase transition behaviour of low boiling point liquids to develop a thermally driven fluidic soft actuator. The research also focuses on obtaining feedback through capacitive textile force sensors and developing a feedback control law for a single actuator as well as sequential actuation of multiple actuators. The findings demonstrate that the proposed actuators produce the desired pressure level utilized in mechanotherapy applications. Moreover, high accuracy is achieved by the capacitive textile force sensors specifically designed for detecting the applied force exerted by the textile-based actuators. The developed system constitutes a comprehensive textile-based system encompassing heating, actuation and sensing capabilities. Following the calibration of the developed system in conjunction with its sensor, a pilot-scale implementation of sequential massage application was conducted to showcase the system’s capabilities and potential. Considering its pressure and heating properties, the developed system exhibits a great potential for utilization in mechanotherapy as well as in thermotherapy applications.
{"title":"Design, modelling and control of a textile-based wearable actuating system with sensor feedback for therapeutic applications","authors":"Mehmet Fatih Çelebi, Asli Tuncay Atalay, Ozgur Atalay, Veysel Gazi","doi":"10.1177/01423312241227252","DOIUrl":"https://doi.org/10.1177/01423312241227252","url":null,"abstract":"This work proposes a textile-dominated wearable actuating system utilizing textile force sensor feedback. The study explores the liquid/gas phase transition behaviour of low boiling point liquids to develop a thermally driven fluidic soft actuator. The research also focuses on obtaining feedback through capacitive textile force sensors and developing a feedback control law for a single actuator as well as sequential actuation of multiple actuators. The findings demonstrate that the proposed actuators produce the desired pressure level utilized in mechanotherapy applications. Moreover, high accuracy is achieved by the capacitive textile force sensors specifically designed for detecting the applied force exerted by the textile-based actuators. The developed system constitutes a comprehensive textile-based system encompassing heating, actuation and sensing capabilities. Following the calibration of the developed system in conjunction with its sensor, a pilot-scale implementation of sequential massage application was conducted to showcase the system’s capabilities and potential. Considering its pressure and heating properties, the developed system exhibits a great potential for utilization in mechanotherapy as well as in thermotherapy applications.","PeriodicalId":507087,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":"59 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139844749","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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