Pub Date : 2025-02-01DOI: 10.1016/j.isatra.2024.11.054
Hyeon-Woo Na, PooGyeon Park
This study investigates the consensus problem for multi-agent systems under Markov switching topology by designing a dynamic output-feedback (DOF) controller. First, an invariant property is presented to address the Markov switching topology utilizing the eigenvalues and eigenvectors of the Laplacian matrix. Second, the eigenvalues of each Laplacian matrix are regarded as bounded uncertainties, representing the variations between the second smallest eigenvalue and the largest eigenvalue, removing the effect of variable eigenvalues. Using the elimination lemma, the equivalent consensus conditions are derived as Linear Matrix Inequalities (LMIs) for the cases both without and with external disturbances. Finally, the DOF controller for consensus is designed by solving the LMIs. Numerical examples are provided to verify the validity of the main results.
{"title":"LMI approach of H∞ consensus for multi-agent systems under Markov switching topology by dynamic output-feedback controller","authors":"Hyeon-Woo Na, PooGyeon Park","doi":"10.1016/j.isatra.2024.11.054","DOIUrl":"10.1016/j.isatra.2024.11.054","url":null,"abstract":"<div><div>This study investigates the <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> consensus problem for multi-agent systems under Markov switching topology by designing a dynamic output-feedback (DOF) controller. First, an invariant property is presented to address the Markov switching topology utilizing the eigenvalues and eigenvectors of the Laplacian matrix. Second, the eigenvalues of each Laplacian matrix are regarded as bounded uncertainties, representing the variations between the second smallest eigenvalue and the largest eigenvalue, removing the effect of variable eigenvalues. Using the elimination lemma, the equivalent consensus conditions are derived as Linear Matrix Inequalities (LMIs) for the cases both without and with external disturbances. Finally, the DOF controller for <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> consensus is designed by solving the LMIs. Numerical examples are provided to verify the validity of the main results.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 1-10"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.isatra.2024.11.047
Shaoxun Liu , Shiyu Zhou , Lei Shi , Hui Jing , Zhihua Niu , Rongrong Wang
Heavy-legged robots (HLRs), integral to optimizing efficiency in manufacturing and transportation, rely on advanced active servo fault diagnosis and fault-tolerant control (FTC) mechanisms. This study presents an FTC framework with active fault status identification, fault tolerance capability assessment, and model uncertainty handling. A key contribution is the introduction of an active servo fault state estimator (ASFSE), which enables real-time monitoring of servo status by comparing residual differences between servo and controller outputs. The system’s tolerance capability interval (TCI) is tied to the servo state, with the dual-line particle filters (DPF) algorithm predicting when the HLR exceeds the TCI under faults. Subsequently, a target trajectory modifier (TTM) and fixed-time backstepping controller (FTBC) are proposed. The TTM promptly adjusts the trajectory when the HLR surpasses the TCI, while the FTBC ensures fixed-time convergence based on the predicted failure time for precise trajectory tracking. As the HLR approaches its fault tolerance limits, the TTM and FTBC ensure a smooth stop, thus mitigating equipment damage caused by servo faults. Mathematical stability proof and simulation validations confirm the effectiveness of the FTC framework.
{"title":"Smooth and safe stop: Fixed-time fault tolerant control for heavy legged robot with active identification on tolerance capability","authors":"Shaoxun Liu , Shiyu Zhou , Lei Shi , Hui Jing , Zhihua Niu , Rongrong Wang","doi":"10.1016/j.isatra.2024.11.047","DOIUrl":"10.1016/j.isatra.2024.11.047","url":null,"abstract":"<div><div>Heavy-legged robots (HLRs), integral to optimizing efficiency in manufacturing and transportation, rely on advanced active servo fault diagnosis and fault-tolerant control (FTC) mechanisms. This study presents an FTC framework with active fault status identification, fault tolerance capability assessment, and model uncertainty handling. A key contribution is the introduction of an active servo fault state estimator (ASFSE), which enables real-time monitoring of servo status by comparing residual differences between servo and controller outputs. The system’s tolerance capability interval (TCI) is tied to the servo state, with the dual-line particle filters (DPF) algorithm predicting when the HLR exceeds the TCI under faults. Subsequently, a target trajectory modifier (TTM) and fixed-time backstepping controller (FTBC) are proposed. The TTM promptly adjusts the trajectory when the HLR surpasses the TCI, while the FTBC ensures fixed-time convergence based on the predicted failure time for precise trajectory tracking. As the HLR approaches its fault tolerance limits, the TTM and FTBC ensure a smooth stop, thus mitigating equipment damage caused by servo faults. Mathematical stability proof and simulation validations confirm the effectiveness of the FTC framework.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 107-123"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.isatra.2024.11.051
Xin-Tong Gao, Yuan-Yi Shen, Chun-Qing Huang
In general, auto-tuning implementation of PID controllers relies on dual controllers, exciting/identification experiments or some prior knowledge on the process and hence the considerable cost on auto-tuning implementation occurs. To deal with such a problem, a new auto-tuning scheme of the FPID controller is developed for minimum variance tasks under routine operating conditions in which the closed-loop system is running without any external excitation other than natural disturbances. This paper reveals that the stochastic disturbance model can be uniquely determined from the first several terms of the impulse response coefficients of the closed-loop system when the precondition on the time delay and the order of the disturbance model is satisfied. Based on the closed-loop non-parametric model (in terms of impulse response coefficients) that is estimated online by utilizing the one-shot closed-loop output data, the disturbance model is estimated by solving an optimization problem and hence the plant model is obtained. Subsequently, the new parameter set of the FPID controller is updated online by solving an optimization problem with respect to the H2 norm of the resulting closed-loop transfer function. The benefit of the proposed scheme over the existing auto-tuning methods is the cost saving of the auto-tuning implementation due to the following facts: i) it does not rely on dual controllers or identification/exciting experiments; ii) it does not require prior knowledge of the process. The effectiveness of the proposed scheme is illustrated in terms of output variance index by numerical cases and industrial examples.
{"title":"Auto-tuning of filtered proportional-integral-derivative controller for industrial processes under routine operating conditions","authors":"Xin-Tong Gao, Yuan-Yi Shen, Chun-Qing Huang","doi":"10.1016/j.isatra.2024.11.051","DOIUrl":"10.1016/j.isatra.2024.11.051","url":null,"abstract":"<div><div>In general, auto-tuning implementation of PID controllers relies on dual controllers, exciting/identification experiments or some prior knowledge on the process and hence the considerable cost on auto-tuning implementation occurs. To deal with such a problem, a new auto-tuning scheme of the FPID controller is developed for minimum variance tasks under routine operating conditions in which the closed-loop system is running without any external excitation other than natural disturbances. This paper reveals that the stochastic disturbance model can be uniquely determined from the first several terms of the impulse response coefficients of the closed-loop system when the precondition on the time delay and the order of the disturbance model is satisfied. Based on the closed-loop non-parametric model (in terms of impulse response coefficients) that is estimated online by utilizing the one-shot closed-loop output data, the disturbance model is estimated by solving an optimization problem and hence the plant model is obtained. Subsequently, the new parameter set of the FPID controller is updated online by solving an optimization problem with respect to the H<sub>2</sub> norm of the resulting closed-loop transfer function. The benefit of the proposed scheme over the existing auto-tuning methods is the cost saving of the auto-tuning implementation due to the following facts: i) it does not rely on dual controllers or identification/exciting experiments; ii) it does not require prior knowledge of the process. The effectiveness of the proposed scheme is illustrated in terms of output variance index by numerical cases and industrial examples.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 186-198"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Redundancy and maintainability-supported fault-tolerant machining systems are used in many industries to achieve pre-specified reliability and system capability. In this investigation, a non-Markov model for the machining system has been developed by involving the concepts of server vacation, server breakdown, and reboot process. The server may fail and undergo primary repair which may be unsuccessful in recovering the server. In case of imperfect server repair, an additional repair is also performed to bring the server back into functional mode. By using the supplementary variable for the residual repair, we obtain the analytic solution of the finite population M/G/1 queueing model for the performance prediction of FTMS. The method of parametric non-linear programming has been implemented to evaluate the performance measures in both crisp and fuzzy environments. The meta-heuristic approaches PSO, GA and classical optimization technique quasi-Newton method are employed to determine the optimal design descriptors by minimizing the total cost. The sensitivity of performance indices with respect to system parameters has been examined for the specific repair time distributions by taking illustrations.
{"title":"Fuzzy modelling and cost optimization of fault-tolerant system with service interruption","authors":"Vijay Pratap Singh , Madhu Jain , Rakesh Kumar Meena , Pankaj Kumar","doi":"10.1016/j.isatra.2024.12.006","DOIUrl":"10.1016/j.isatra.2024.12.006","url":null,"abstract":"<div><div>Redundancy and maintainability-supported fault-tolerant machining systems are used in many industries to achieve pre-specified reliability and system capability. In this investigation, a non-Markov model for the machining system has been developed by involving the concepts of server vacation, server breakdown, and reboot process. The server may fail and undergo primary repair which may be unsuccessful in recovering the server. In case of imperfect server repair, an additional repair is also performed to bring the server back into functional mode. By using the supplementary variable for the residual repair, we obtain the analytic solution of the finite population M/G/1 queueing model for the performance prediction of FTMS. The method of parametric non-linear programming has been implemented to evaluate the performance measures in both crisp and fuzzy environments. The meta-heuristic approaches PSO, GA and classical optimization technique quasi-Newton method are employed to determine the optimal design descriptors by minimizing the total cost. The sensitivity of performance indices with respect to system parameters has been examined for the specific repair time distributions by taking illustrations.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 89-106"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The prediction of the remaining useful life (RUL) holds significant importance within the field of prognostics and health management (PHM), which may provide lifetime information about the system. The foundation for effectively estimating RUL is constructing an applicable degradation model for the system. However, the majority of existing degradation models only consider the issue of age dependence and disregard state dependence. In addition, variations to the system’s operating environment will cause degradation state jumps, which will impact degradation paths and the precision of RUL estimation. Nevertheless, existing studies have only accounted for some of the influencing factors, neglecting to simultaneously consider age dependence, state dependence, and jumps. To account for both age-state dependent (ASD) and jump, a generalized age-state dependent jump-diffusion (ASDJD) model is proposed for RUL estimation in this paper. Approximate analytic expressions for the RUL distribution are derived based on first hitting time (FHT). Expectation conditional maximization (ECM) and maximum likelihood estimate (MLE) are used to estimate the model’s unknown parameters. The simulation dataset and the Xi’an Jiaotong University bearing dataset validate the proposed model, demonstrating that state dependence and jumps should be considered in the RUL estimation process.
{"title":"Remaining useful life prognostic for degrading systems with age- and state-dependent jump-diffusion processes","authors":"Bincheng Wen , Mingqing Xiao , Xilang Tang , Yawei Ge , Xin Zhao , Haizhen Zhu","doi":"10.1016/j.isatra.2024.12.019","DOIUrl":"10.1016/j.isatra.2024.12.019","url":null,"abstract":"<div><div>The prediction of the remaining useful life (RUL) holds significant importance within the field of prognostics and health management (PHM), which may provide lifetime information about the system. The foundation for effectively estimating RUL is constructing an applicable degradation model for the system. However, the majority of existing degradation models only consider the issue of age dependence and disregard state dependence. In addition, variations to the system’s operating environment will cause degradation state jumps, which will impact degradation paths and the precision of RUL estimation. Nevertheless, existing studies have only accounted for some of the influencing factors, neglecting to simultaneously consider age dependence, state dependence, and jumps. To account for both age-state dependent (ASD) and jump, a generalized age-state dependent jump-diffusion (ASDJD) model is proposed for RUL estimation in this paper. Approximate analytic expressions for the RUL distribution are derived based on first hitting time (FHT). Expectation conditional maximization (ECM) and maximum likelihood estimate (MLE) are used to estimate the model’s unknown parameters. The simulation dataset and the Xi’an Jiaotong University bearing dataset validate the proposed model, demonstrating that state dependence and jumps should be considered in the RUL estimation process.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 142-152"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.isatra.2024.11.059
Mingliang Yang , Peisong Dai , Yingqi Yin , Dayi Wang , Yawen Wang , Haibo Huang
Pure electric vehicles (PEVs) lack engine noise; thus, the overall noise level within vehicle cabins are reduced. However, due to the absence of engine noise, previously overlooked noise sources are accentuated and detrimentally affect interior noise quality. Road noise is a predominant PEV noise source and significantly contributes to middle- and low-frequency interior noise levels. A novel approach combining data-driven methodologies and uncertainty analysis to predict and optimize vehicle road noise is proposed. To predict the frequency-domain characteristics of road noise, a refined attention mechanism based on the transformer model with a locality-sensitive hashing algorithm is introduced to enhance efficiency and ensure high accuracy. An interval vector optimization method using interval representations of parameter uncertainty is devised to strengthen the robustness and efficacy of the road noise optimization results. The proposed method is validated through a PEV road test, and the optimized noise conditions demonstrates an improvement exceeding 2 dB.
{"title":"Predicting and optimizing pure electric vehicle road noise via a locality-sensitive hashing transformer and interval analysis","authors":"Mingliang Yang , Peisong Dai , Yingqi Yin , Dayi Wang , Yawen Wang , Haibo Huang","doi":"10.1016/j.isatra.2024.11.059","DOIUrl":"10.1016/j.isatra.2024.11.059","url":null,"abstract":"<div><div>Pure electric vehicles (PEVs) lack engine noise; thus, the overall noise level within vehicle cabins are reduced. However, due to the absence of engine noise, previously overlooked noise sources are accentuated and detrimentally affect interior noise quality. Road noise is a predominant PEV noise source and significantly contributes to middle- and low-frequency interior noise levels. A novel approach combining data-driven methodologies and uncertainty analysis to predict and optimize vehicle road noise is proposed. To predict the frequency-domain characteristics of road noise, a refined attention mechanism based on the transformer model with a locality-sensitive hashing algorithm is introduced to enhance efficiency and ensure high accuracy. An interval vector optimization method using interval representations of parameter uncertainty is devised to strengthen the robustness and efficacy of the road noise optimization results. The proposed method is validated through a PEV road test, and the optimized noise conditions demonstrates an improvement exceeding 2 dB.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 556-572"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.isatra.2024.11.049
Xiaobin Lian , Suyi Liu , Xuyang Cao , Hongyan Wang , Wudong Deng , Xin Ning
Agile control after the release of test mass is related to the success or failure of China's space gravitational wave detection program, such as TianQin and Taiji. In the release process, the test mass’s motion state is complex and susceptible to collisions with the satellite cavity. In addition, the release capture control of the test mass uses electrostatic force, which is extremely small. These factors pose a significant challenge to the control system design. For this purpose, this paper proposes a real-time predictive control method for PINN-DDPG based on Physical Information Neural Network (PINN), Long Short-Term Memory (LSTM), and Deep Deterministic Policy Gradient (DDPG) to solve the problem of agile capture control under weak electrostatic force. First, a PINN-LSTM network for real-time state prediction is designed based on PINN and LSTM to solve the problems of interpretability and time-dependent state prediction. Subsequently, a DDPG controller was designed to solve the reinforcement learning control problem in continuous action space. Finally, simulation results demonstrate that, in comparison to the traditional PINN, the PINN-LSTM markedly hastens the training convergence, cutting the time by 60 %. Compared to traditional DDPG control, the PINN-DDPG diminish the stabilization time of position and velocity errors by 70 %.
{"title":"Agile control of test mass based on PINN-DDPG for drag-free satellite","authors":"Xiaobin Lian , Suyi Liu , Xuyang Cao , Hongyan Wang , Wudong Deng , Xin Ning","doi":"10.1016/j.isatra.2024.11.049","DOIUrl":"10.1016/j.isatra.2024.11.049","url":null,"abstract":"<div><div>Agile control after the release of test mass is related to the success or failure of China's space gravitational wave detection program, such as TianQin and Taiji. In the release process, the test mass’s motion state is complex and susceptible to collisions with the satellite cavity. In addition, the release capture control of the test mass uses electrostatic force, which is extremely small. These factors pose a significant challenge to the control system design. For this purpose, this paper proposes a real-time predictive control method for PINN-DDPG based on Physical Information Neural Network (PINN), Long Short-Term Memory (LSTM), and Deep Deterministic Policy Gradient (DDPG) to solve the problem of agile capture control under weak electrostatic force. First, a PINN-LSTM network for real-time state prediction is designed based on PINN and LSTM to solve the problems of interpretability and time-dependent state prediction. Subsequently, a DDPG controller was designed to solve the reinforcement learning control problem in continuous action space. Finally, simulation results demonstrate that, in comparison to the traditional PINN, the PINN-LSTM markedly hastens the training convergence, cutting the time by 60 %. Compared to traditional DDPG control, the PINN-DDPG diminish the stabilization time of position and velocity errors by 70 %.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 306-317"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.isatra.2024.12.016
Ji Zhao , Wenyue Li , Qiang Li , Hongbin Zhang
The quadratic cost functions, exemplified by mean-square-error, often exhibit limited robustness and flexibility when confronted with impulsive noise contamination. In contrast, the generalized maximum correntropy (GMC) criterion, serving as a robust nonlinear similarity measure, offers superior performance in such scenarios. In this paper, we develop a recursive constrained adaptive filtering algorithm named recursive generalized maximum correntropy with a forgetting factor (FF-RCGMC). This algorithm integrates the exponential weighted GMC criterion with a linear constraint framework based on least-squares. However, the lack of constraint information during the learning process may lead to divergence or malfunctioning of FF-RCGMC after a certain number of iterations because of round-off errors. To rectify this deficiency, we introduce a constraint-forcing strategy into FF-RCGMC, resulting in a more stable variant termed robust type constraint-forcing FF-RCGMC (CFFF-RCGMC). In the context of CFFF-RCGMC, we embark on a thorough examination of its computational burden, encompassing both mean and mean-square stability analyses, along with an in-depth exploration of its transient and steady-state filtering characteristics under a set of plausible assumptions. Our simulation-based evaluations, specifically tailored for system identification tasks within non-Gaussian noisy environments, unequivocally underscore the excellent performance of CFFF-RCGMC when against its relevant algorithmic counterparts.
{"title":"A generalized maximum correntropy based constraint adaptive filtering: Constraint-forcing and performance analyses","authors":"Ji Zhao , Wenyue Li , Qiang Li , Hongbin Zhang","doi":"10.1016/j.isatra.2024.12.016","DOIUrl":"10.1016/j.isatra.2024.12.016","url":null,"abstract":"<div><div>The quadratic cost functions, exemplified by mean-square-error, often exhibit limited robustness and flexibility when confronted with impulsive noise contamination. In contrast, the generalized maximum correntropy (GMC) criterion, serving as a robust nonlinear similarity measure, offers superior performance in such scenarios. In this paper, we develop a recursive constrained adaptive filtering algorithm named recursive generalized maximum correntropy with a forgetting factor (FF-RCGMC). This algorithm integrates the exponential weighted GMC criterion with a linear constraint framework based on least-squares. However, the lack of constraint information during the learning process may lead to divergence or malfunctioning of FF-RCGMC after a certain number of iterations because of round-off errors. To rectify this deficiency, we introduce a constraint-forcing strategy into FF-RCGMC, resulting in a more stable variant termed robust type constraint-forcing FF-RCGMC (CFFF-RCGMC). In the context of CFFF-RCGMC, we embark on a thorough examination of its computational burden, encompassing both mean and mean-square stability analyses, along with an in-depth exploration of its transient and steady-state filtering characteristics under a set of plausible assumptions. Our simulation-based evaluations, specifically tailored for system identification tasks within non-Gaussian noisy environments, unequivocally underscore the excellent performance of CFFF-RCGMC when against its relevant algorithmic counterparts.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 199-212"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.isatra.2024.12.020
Lei Guo , Hongyu Lin , Yuan Song , Yufeng Zhuang , Dongming Gan
This paper investigates the safety control problem of a bicycle robot with front-wheel drive and without a trail or mechanical regulator during circular motion. Constraints on the drive angular speed necessary for the bicycle to achieve circular motion are proposed. In practical robot systems, bounded input disturbances are inevitable. To address this, we propose a safe controller that integrates the control Lyapunov function (CLF) constraints for input-to-state stability (ISS) and the control barrier function (CBF) constraints for input-to-state safety (ISSf), implemented using quadratic programming (QP). Our controller achieves enhanced safety in control while reducing control effort. The effectiveness of this controller is verified through simulation comparative experiments. Furthermore, circular motion is achieved through physical experiments with a real robot, and the effectiveness of the ISS-CLF-ISSf-CBF-QP controller is validated through comparative experiments.
{"title":"Safe controller design for circular motion of a bicycle robot using control Lyapunov function and control barrier function","authors":"Lei Guo , Hongyu Lin , Yuan Song , Yufeng Zhuang , Dongming Gan","doi":"10.1016/j.isatra.2024.12.020","DOIUrl":"10.1016/j.isatra.2024.12.020","url":null,"abstract":"<div><div>This paper investigates the safety control problem of a bicycle robot with front-wheel drive and without a trail or mechanical regulator during circular motion. Constraints on the drive angular speed necessary for the bicycle to achieve circular motion are proposed. In practical robot systems, bounded input disturbances are inevitable. To address this, we propose a safe controller that integrates the control Lyapunov function (CLF) constraints for input-to-state stability (ISS) and the control barrier function (CBF) constraints for input-to-state safety (ISSf), implemented using quadratic programming (QP). Our controller achieves enhanced safety in control while reducing control effort. The effectiveness of this controller is verified through simulation comparative experiments. Furthermore, circular motion is achieved through physical experiments with a real robot, and the effectiveness of the ISS-CLF-ISSf-CBF-QP controller is validated through comparative experiments.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 543-555"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.isatra.2024.11.044
Kehan Chen , Ruoqi Zhang , Lin Meng , Xingyuan Zheng , Kun Wang , Huiqi Wang
From the noise-assisted perspective of stochastic resonance (SR), fractional system has been adopted to enhance the diagnostic performance of mechanical faults by utilizing the previous state information in mechanical degradation process, but the computation is extremely time-consuming. To address this challenge, we develop a fast diagnosis method leveraging the mechanism of generalized SR (GSR)-based active energy conversion in fluctuating-damping fractional oscillator (FDFO). Through the analysis of system stationary response, we propose a theoretical index known as fault feature amplification (FFA), which effectively replaces the time-consuming numerical solution in multi-parameter optimization, leading to a remarkable reduction in the time complexity of the adaptive diagnosis algorithm. This improvement brings about significant benefits, notably simplifying the diagnosis flow. Based on the results of performance evaluation in diagnosing simulated bearing signals, the proposed method exhibits a comprehensive superiority in identifying ability and diagnosis efficiency. Finally, this method has been further validated in experimental diagnosis, especially for some challenging cases, providing strong support for engineering applications, particularly in the fast diagnosis of complex operating environments.
{"title":"The fast bearing diagnosis based on adaptive GSR of fault feature amplification in scale-transformed fractional oscillator","authors":"Kehan Chen , Ruoqi Zhang , Lin Meng , Xingyuan Zheng , Kun Wang , Huiqi Wang","doi":"10.1016/j.isatra.2024.11.044","DOIUrl":"10.1016/j.isatra.2024.11.044","url":null,"abstract":"<div><div>From the noise-assisted perspective of stochastic resonance (SR), fractional system has been adopted to enhance the diagnostic performance of mechanical faults by utilizing the previous state information in mechanical degradation process, but the computation is extremely time-consuming. To address this challenge, we develop a fast diagnosis method leveraging the mechanism of generalized SR (GSR)-based active energy conversion in fluctuating-damping fractional oscillator (FDFO). Through the analysis of system stationary response, we propose a theoretical index known as fault feature amplification (FFA), which effectively replaces the time-consuming numerical solution in multi-parameter optimization, leading to a remarkable reduction in the time complexity of the adaptive diagnosis algorithm. This improvement brings about significant benefits, notably simplifying the diagnosis flow. Based on the results of performance evaluation in diagnosing simulated bearing signals, the proposed method exhibits a comprehensive superiority in identifying ability and diagnosis efficiency. Finally, this method has been further validated in experimental diagnosis, especially for some challenging cases, providing strong support for engineering applications, particularly in the fast diagnosis of complex operating environments.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 124-141"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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