Pub Date : 2023-07-25DOI: 10.1177/01423312231184277
Yan-Tao Jiao, Li Ding, Zheng-Min Kong, Chaoyang Chen
In this paper, a distributed observer-based resilient control scheme is proposed to solve the voltage regulation and current sharing problems of islanded direct current (DC) microgrids under actuator faults. First, an observer-based distributed fault estimation design is presented to estimate fault signals with high accuracy. Then, based on the estimated information, a primary decentralized fault-tolerant control layer is designed to ensure accurate voltage reference tracking. A secondary cooperative control layer is employed to achieve the goal of current sharing. Besides, both the fault estimation observers and the fault-tolerant controllers are designed in a plug-and-play fashion, which guarantees the scalability of the DC microgrid. The simulation and experimental results show that the proposed control strategy can efficiently improve the reliability and resilience of the DC microgrid.
{"title":"Distributed observer-based resilient control of islanded DC microgrids under actuator faults","authors":"Yan-Tao Jiao, Li Ding, Zheng-Min Kong, Chaoyang Chen","doi":"10.1177/01423312231184277","DOIUrl":"https://doi.org/10.1177/01423312231184277","url":null,"abstract":"In this paper, a distributed observer-based resilient control scheme is proposed to solve the voltage regulation and current sharing problems of islanded direct current (DC) microgrids under actuator faults. First, an observer-based distributed fault estimation design is presented to estimate fault signals with high accuracy. Then, based on the estimated information, a primary decentralized fault-tolerant control layer is designed to ensure accurate voltage reference tracking. A secondary cooperative control layer is employed to achieve the goal of current sharing. Besides, both the fault estimation observers and the fault-tolerant controllers are designed in a plug-and-play fashion, which guarantees the scalability of the DC microgrid. The simulation and experimental results show that the proposed control strategy can efficiently improve the reliability and resilience of the DC microgrid.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49222987","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}
To enable autonomous vehicles to generate smooth and collision-free trajectories and improve their driving performance on structured roads, this paper proposes a hierarchical trajectory planning algorithm based on an improved artificial potential field method. To improve the applicability of the algorithm to complex scenarios, the Frenet coordinate system was established to address these limitations. First, the safety distance model is applied to the risk assessment of the improved artificial potential field method. Then, the hierarchical solution is carried out, and the road solvable convex space and the rough path solution are solved by combining the artificial potential field method. On this basis, the potential field term and the smoothing term cost function are established, and the sequential quadratic programming (SQP) algorithm is used to solve the exact path that meets the requirements of safety and smoothness. Hierarchical planning shortens the solution time by quickly determining the bounds of the convex space. Finally, in the speed planning, in order to take into account the comfort and safety of the occupants, the speed curve is solved by considering the dynamic constraints of the vehicle. The obstacle avoidance effects of the algorithm on static and dynamic obstacles are tested in different simulation scenarios. The results of the simulation experiment show that the proposed algorithm can successfully achieve obstacle avoidance on complex structured roads.
{"title":"Hierarchical collision-free trajectory planning for autonomous vehicles based on improved artificial potential field method","authors":"Ping Qin, Fei Liu, Zhizhong Guo, Zhe Li, Yuze Shang","doi":"10.1177/01423312231186684","DOIUrl":"https://doi.org/10.1177/01423312231186684","url":null,"abstract":"To enable autonomous vehicles to generate smooth and collision-free trajectories and improve their driving performance on structured roads, this paper proposes a hierarchical trajectory planning algorithm based on an improved artificial potential field method. To improve the applicability of the algorithm to complex scenarios, the Frenet coordinate system was established to address these limitations. First, the safety distance model is applied to the risk assessment of the improved artificial potential field method. Then, the hierarchical solution is carried out, and the road solvable convex space and the rough path solution are solved by combining the artificial potential field method. On this basis, the potential field term and the smoothing term cost function are established, and the sequential quadratic programming (SQP) algorithm is used to solve the exact path that meets the requirements of safety and smoothness. Hierarchical planning shortens the solution time by quickly determining the bounds of the convex space. Finally, in the speed planning, in order to take into account the comfort and safety of the occupants, the speed curve is solved by considering the dynamic constraints of the vehicle. The obstacle avoidance effects of the algorithm on static and dynamic obstacles are tested in different simulation scenarios. The results of the simulation experiment show that the proposed algorithm can successfully achieve obstacle avoidance on complex structured roads.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46296137","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 : 2023-07-21DOI: 10.1177/01423312231187452
Jian Zhang, Fanlin Meng, Yunjing Liu
Aiming at the oscillation in the electric vehicle powertrain systems, a control strategy based on the structural singular value [Formula: see text] is proposed. To solve the problem of robustness under parameter uncertainty, the traditional [Formula: see text]-synthesis needs to obtain all-pass characteristics through the weight function in the nominal system, which reduces the perturbation suppression ability of the system. Unlike the previous use of [Formula: see text]-analysis as a system performance criterion, this paper starts from the essence of parameter uncertainty problems and designs controllers based on the relationship between parameter uncertainty and nominal systems, using structural singular value [Formula: see text] as a performance constraint. The result shows that the [Formula: see text]-analysis not only takes into account the robustness and disturbance rejection ability but also reduces the controller order. In addition, the oscillation suppression effect in the presence of transmission backlash is verified, and the [Formula: see text]-analysis can suppress the oscillation better than the [Formula: see text]-synthesis.
{"title":"Design of the electric vehicle powertrain systems based on μ-analysis","authors":"Jian Zhang, Fanlin Meng, Yunjing Liu","doi":"10.1177/01423312231187452","DOIUrl":"https://doi.org/10.1177/01423312231187452","url":null,"abstract":"Aiming at the oscillation in the electric vehicle powertrain systems, a control strategy based on the structural singular value [Formula: see text] is proposed. To solve the problem of robustness under parameter uncertainty, the traditional [Formula: see text]-synthesis needs to obtain all-pass characteristics through the weight function in the nominal system, which reduces the perturbation suppression ability of the system. Unlike the previous use of [Formula: see text]-analysis as a system performance criterion, this paper starts from the essence of parameter uncertainty problems and designs controllers based on the relationship between parameter uncertainty and nominal systems, using structural singular value [Formula: see text] as a performance constraint. The result shows that the [Formula: see text]-analysis not only takes into account the robustness and disturbance rejection ability but also reduces the controller order. In addition, the oscillation suppression effect in the presence of transmission backlash is verified, and the [Formula: see text]-analysis can suppress the oscillation better than the [Formula: see text]-synthesis.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41989795","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}
This paper presents a sliding mode control based on particle swarm optimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degradation of a multi-joint manipulator caused by uncertainties such as external disturbances and modeling error. First, to address the problem that the precise dynamic system of the manipulator is difficult to establish, the radial basis function neural network (RBFNN) is used to approximate the uncertainty of the manipulator model, and the parameters of the neural network are optimized through the adaptive natural selection particle swarm optimization algorithm (ASelPSO) to improve the approximation ability and reduce the approximation error. Second, to eliminate chattering, adaptive reaching law is selected to improve the dynamic quality of approaching motion. Finally, a comparative simulation experiment is carried out with a 3-DOF manipulator as the research object. The results show that the control method has obvious improvements in eliminating chattering, improving tracking accuracy, and increasing convergence speed, which verifies the feasibility and superiority of the control scheme.
{"title":"Sliding mode control based on particle swarm optimization neural network and adaptive reaching law","authors":"Jiqing Chen, Haiyan Zhang, Shangtao Pan, Qingsong Tang","doi":"10.1177/01423312231186214","DOIUrl":"https://doi.org/10.1177/01423312231186214","url":null,"abstract":"This paper presents a sliding mode control based on particle swarm optimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degradation of a multi-joint manipulator caused by uncertainties such as external disturbances and modeling error. First, to address the problem that the precise dynamic system of the manipulator is difficult to establish, the radial basis function neural network (RBFNN) is used to approximate the uncertainty of the manipulator model, and the parameters of the neural network are optimized through the adaptive natural selection particle swarm optimization algorithm (ASelPSO) to improve the approximation ability and reduce the approximation error. Second, to eliminate chattering, adaptive reaching law is selected to improve the dynamic quality of approaching motion. Finally, a comparative simulation experiment is carried out with a 3-DOF manipulator as the research object. The results show that the control method has obvious improvements in eliminating chattering, improving tracking accuracy, and increasing convergence speed, which verifies the feasibility and superiority of the control scheme.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44141084","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}
In this article, a prescribed-time state-feedback stabilization design strategy is proposed for a class of p-norm stochastic nonlinear strict feedback systems. In previous work on prescribed-time stabilization of stochastic systems, only stochastic nonlinear systems with fractional power less than or equal to one are considered. To overcome this problem, we improve the existing method and discuss the issue of prescribed-time stabilization of stochastic nonlinear systems with fractional power is arbitrary positive odd rational number. First, a prescribed-time controller is designed by combining the Lyapunov function with adding a power integrator technique. It should be pointed out that the homogeneous domination approach is adopted when dealing with the nonlinear terms of the system. Then, according to the stochastic prescribed-time stability theorem, it is proved that the designed controller can ensure the closed-loop system is prescribed-time mean-square stable. Finally, three simulation examples are given to investigate the validity of the presented method, in which the last one is an electromechanical system example.
{"title":"Prescribed-time stabilization control for p-norm stochastic nonlinear systems based on homogeneous dominant technique","authors":"Mengqing Cheng, Junsheng Zhao, Zong-yao Sun, Guangming Zhuang","doi":"10.1177/01423312231182469","DOIUrl":"https://doi.org/10.1177/01423312231182469","url":null,"abstract":"In this article, a prescribed-time state-feedback stabilization design strategy is proposed for a class of p-norm stochastic nonlinear strict feedback systems. In previous work on prescribed-time stabilization of stochastic systems, only stochastic nonlinear systems with fractional power less than or equal to one are considered. To overcome this problem, we improve the existing method and discuss the issue of prescribed-time stabilization of stochastic nonlinear systems with fractional power is arbitrary positive odd rational number. First, a prescribed-time controller is designed by combining the Lyapunov function with adding a power integrator technique. It should be pointed out that the homogeneous domination approach is adopted when dealing with the nonlinear terms of the system. Then, according to the stochastic prescribed-time stability theorem, it is proved that the designed controller can ensure the closed-loop system is prescribed-time mean-square stable. Finally, three simulation examples are given to investigate the validity of the presented method, in which the last one is an electromechanical system example.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46549697","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 : 2023-07-18DOI: 10.1177/01423312231184728
Rui Wang, Chunyue Song, Sikai Chen, Jun Zhao
Uncertainties in a battery would result in unreliable state of health (SOH) estimation. Considering the greater risk after reaching the end of life (EOL), designing a suitable ensemble learning to provide early warning before reaching EOL with uncertainty measurement is desirable for confidence estimation. In this paper, a novel probabilistic ensemble learning method-Gaussian process-based neural networks is proposed for the SOH confidence estimation by describing the uncertainties in probabilistic form. First, different neural networks are built based on health features. Second, battery data are classified under the recovery of capacity and normal operation conditions to characterize the uncertainties of the data under different operation conditions. Besides, the Gaussian process-based neural networks method is constructed based on the data from different conditions for neural networks weighted ensemble with the probabilistic form of Gaussian distribution. Therefore, the uncertainties are measured in the probabilistic form considering different operation conditions which is different from other methods. With the probabilistic form, the confidence interval could be determined to ensure the real SOH within the confidence interval, which improves the estimation performance of the proposed method because of the early warning near the EOL. Finally, the effectiveness is validated by NASA data sets and our experiment with the commercial 18650 lithium-ion battery. From the results, the mean error is less than 1% and real SOH is within the confidence interval.
{"title":"State of health confidence estimation for lithium-ion battery based on probabilistic ensemble learning","authors":"Rui Wang, Chunyue Song, Sikai Chen, Jun Zhao","doi":"10.1177/01423312231184728","DOIUrl":"https://doi.org/10.1177/01423312231184728","url":null,"abstract":"Uncertainties in a battery would result in unreliable state of health (SOH) estimation. Considering the greater risk after reaching the end of life (EOL), designing a suitable ensemble learning to provide early warning before reaching EOL with uncertainty measurement is desirable for confidence estimation. In this paper, a novel probabilistic ensemble learning method-Gaussian process-based neural networks is proposed for the SOH confidence estimation by describing the uncertainties in probabilistic form. First, different neural networks are built based on health features. Second, battery data are classified under the recovery of capacity and normal operation conditions to characterize the uncertainties of the data under different operation conditions. Besides, the Gaussian process-based neural networks method is constructed based on the data from different conditions for neural networks weighted ensemble with the probabilistic form of Gaussian distribution. Therefore, the uncertainties are measured in the probabilistic form considering different operation conditions which is different from other methods. With the probabilistic form, the confidence interval could be determined to ensure the real SOH within the confidence interval, which improves the estimation performance of the proposed method because of the early warning near the EOL. Finally, the effectiveness is validated by NASA data sets and our experiment with the commercial 18650 lithium-ion battery. From the results, the mean error is less than 1% and real SOH is within the confidence interval.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42536089","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 : 2023-07-16DOI: 10.1177/01423312231185623
Xiaoming Xia, Changwei Xia, Chuanguang Sun
In this paper, a distributed formation tracking control problem is investigated for underactuated surface vessels (USVs). The uncertainties induced by model uncertainties and external disturbances are assumed to be unknown. An event-trigged disturbance observer (ETDO) is proposed to provide the estimations of the uncertainties, and an event-triggered mechanism is used to determine when measured velocity information must be sent to the observer. An event-trigged controller (ETC) is designed based on a backstepping technique, dynamic surface control, and the observer. Stability analysis of distributed formation control system is given to prove all signals are bounded. Simulations demonstrate the proposed control strategy.
{"title":"Distributed formation tracking control of underactuated surface vehicles based on event-trigged control","authors":"Xiaoming Xia, Changwei Xia, Chuanguang Sun","doi":"10.1177/01423312231185623","DOIUrl":"https://doi.org/10.1177/01423312231185623","url":null,"abstract":"In this paper, a distributed formation tracking control problem is investigated for underactuated surface vessels (USVs). The uncertainties induced by model uncertainties and external disturbances are assumed to be unknown. An event-trigged disturbance observer (ETDO) is proposed to provide the estimations of the uncertainties, and an event-triggered mechanism is used to determine when measured velocity information must be sent to the observer. An event-trigged controller (ETC) is designed based on a backstepping technique, dynamic surface control, and the observer. Stability analysis of distributed formation control system is given to prove all signals are bounded. Simulations demonstrate the proposed control strategy.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45702347","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 : 2023-07-13DOI: 10.1177/01423312231182177
Ghizlane El khaloufi, N. Chaibi, Sadek Belamfedel Alaoui, I. Boumhidi
This work addresses the problem of synchronizing pathological changes in a patient with coronary artery obstruction to the corresponding nominal coronary artery system (CAS) model. The CAS model is characterized by nonlinear terms, so the synchronization problem is transformed into an equivalent time-varying delay T-S fuzzy framework using the sector of nonlinearity approach. New bilinear matrix inequalities (BMIs) conditions for robust stability analysis and dynamic output feedback gain synthesis are derived based on a less conservative condition for stability assessment of T-S fuzzy systems. A simple change of coordinate is used to solve the cross terms of the bilinearities, allowing the problem to be formulated in terms of linear matrix inequalities (LMIs) and solved using standard semi-definite programming. The effectiveness of the controller design approach is demonstrated through extensive simulations under diverse performance criterion that the controller can achieve.
{"title":"Generalized dissipativity dynamic output feedback control for coronary artery system","authors":"Ghizlane El khaloufi, N. Chaibi, Sadek Belamfedel Alaoui, I. Boumhidi","doi":"10.1177/01423312231182177","DOIUrl":"https://doi.org/10.1177/01423312231182177","url":null,"abstract":"This work addresses the problem of synchronizing pathological changes in a patient with coronary artery obstruction to the corresponding nominal coronary artery system (CAS) model. The CAS model is characterized by nonlinear terms, so the synchronization problem is transformed into an equivalent time-varying delay T-S fuzzy framework using the sector of nonlinearity approach. New bilinear matrix inequalities (BMIs) conditions for robust stability analysis and dynamic output feedback gain synthesis are derived based on a less conservative condition for stability assessment of T-S fuzzy systems. A simple change of coordinate is used to solve the cross terms of the bilinearities, allowing the problem to be formulated in terms of linear matrix inequalities (LMIs) and solved using standard semi-definite programming. The effectiveness of the controller design approach is demonstrated through extensive simulations under diverse performance criterion that the controller can achieve.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42990735","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 : 2023-07-13DOI: 10.1177/01423312231185423
Susu Yu, Xuan Fan, Jingjing Qi, Luanfei Wan, Bingyou Liu
For the attitude control system of a quadrotor unmanned aerial vehicle (UAV), an integral backstepping active disturbance rejection control strategy is proposed to solve the tracking and disturbance rejection problems in flight control. To solve the problem of buffeting near the origin of traditional nonlinear functions, this paper designs a function [Formula: see text] with better smoothness near the origin. Second, to improve the observation performance of the extended state observer and the anti-disturbance performance of the controller, an improved expanded state observer (IESO) based on error is designed according to the working principle of expanded state observer (ESO) and the tracking law of state variables. Then, to improve the tracking accuracy of the controller, the integral term is introduced into the backstepping method and uses the idea of recursion to design the nonlinear backstepping integral control law. Combining the disturbance estimation and compensation functions of active disturbance rejection control (ADRC), an integral backstepping active disturbance rejection control is designed and applied to the attitude angle control channel of quadrotor UAV. Finally, experimental results indicate that the designed control strategy can realize high-precision tracking of the attitude angle of a quadrotor UAV, achieve fast dynamic response, and improve the anti-interference.
{"title":"Attitude control of quadrotor UAV based on integral backstepping active disturbance rejection control","authors":"Susu Yu, Xuan Fan, Jingjing Qi, Luanfei Wan, Bingyou Liu","doi":"10.1177/01423312231185423","DOIUrl":"https://doi.org/10.1177/01423312231185423","url":null,"abstract":"For the attitude control system of a quadrotor unmanned aerial vehicle (UAV), an integral backstepping active disturbance rejection control strategy is proposed to solve the tracking and disturbance rejection problems in flight control. To solve the problem of buffeting near the origin of traditional nonlinear functions, this paper designs a function [Formula: see text] with better smoothness near the origin. Second, to improve the observation performance of the extended state observer and the anti-disturbance performance of the controller, an improved expanded state observer (IESO) based on error is designed according to the working principle of expanded state observer (ESO) and the tracking law of state variables. Then, to improve the tracking accuracy of the controller, the integral term is introduced into the backstepping method and uses the idea of recursion to design the nonlinear backstepping integral control law. Combining the disturbance estimation and compensation functions of active disturbance rejection control (ADRC), an integral backstepping active disturbance rejection control is designed and applied to the attitude angle control channel of quadrotor UAV. Finally, experimental results indicate that the designed control strategy can realize high-precision tracking of the attitude angle of a quadrotor UAV, achieve fast dynamic response, and improve the anti-interference.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45554217","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 : 2023-07-13DOI: 10.1177/01423312231185702
Yunhan Ling, D. Fu, Peng Jiang, Yong Sun, Chao Yuan, Dali Huang, Jingfeng Lu, Siliang Lu
Rotating machine fault diagnosis plays a vital role in reducing maintenance costs and preventing accidents. Machine learning (ML) methods and Internet of things (IoT) technologies have been recently introduced into machine fault diagnosis and have generated inspiring results. An ML model with more trainable parameters can typically generate a higher fault diagnostic accuracy. However, the IoT nodes have limited computation and storage resources. How to design an ML model with high accuracy and computational efficiency is still a difficulty and challenge. This work develops an enhanced sparse filtering (ESF) method for mining and fusing the features of the machine signals for fault diagnosis. First, a dimension reduction algorithm is utilized for obtaining the principal components of the vibration signals that are hindered by noises. The distinct features of the principal components are then exploited by using sparse filtering (SF). To reduce the overfitting of the SF model, the L1/2 norm is applied to regularize the objective function. Finally, the obtained features are combined as the inputs of a softmax classifier for machine fault pattern recognition. The effectiveness, superiority, and robustness of the proposed ESF method are validated by the simulated signals and the practical bearing and motor fault signals compared with the other conventional methods. The lightweight and intelligent ESF algorithm is also deployed onto an edge computing node to realize online motor fault diagnosis. The designed model and the proposed method show great potential in highly accurate and efficient rotation machine fault diagnosis.
{"title":"Lightweight and intelligent model based on enhanced sparse filtering for rotating machine fault diagnosis","authors":"Yunhan Ling, D. Fu, Peng Jiang, Yong Sun, Chao Yuan, Dali Huang, Jingfeng Lu, Siliang Lu","doi":"10.1177/01423312231185702","DOIUrl":"https://doi.org/10.1177/01423312231185702","url":null,"abstract":"Rotating machine fault diagnosis plays a vital role in reducing maintenance costs and preventing accidents. Machine learning (ML) methods and Internet of things (IoT) technologies have been recently introduced into machine fault diagnosis and have generated inspiring results. An ML model with more trainable parameters can typically generate a higher fault diagnostic accuracy. However, the IoT nodes have limited computation and storage resources. How to design an ML model with high accuracy and computational efficiency is still a difficulty and challenge. This work develops an enhanced sparse filtering (ESF) method for mining and fusing the features of the machine signals for fault diagnosis. First, a dimension reduction algorithm is utilized for obtaining the principal components of the vibration signals that are hindered by noises. The distinct features of the principal components are then exploited by using sparse filtering (SF). To reduce the overfitting of the SF model, the L1/2 norm is applied to regularize the objective function. Finally, the obtained features are combined as the inputs of a softmax classifier for machine fault pattern recognition. The effectiveness, superiority, and robustness of the proposed ESF method are validated by the simulated signals and the practical bearing and motor fault signals compared with the other conventional methods. The lightweight and intelligent ESF algorithm is also deployed onto an edge computing node to realize online motor fault diagnosis. The designed model and the proposed method show great potential in highly accurate and efficient rotation machine fault diagnosis.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49467006","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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