Pub Date : 2025-02-01DOI: 10.1016/j.isatra.2024.11.045
Hao Zhou, Jianzhong Yang, Qian Zhu, Jihong Chen
The vibration signal of the spindle motor contains complicated mixed modulation harmonics and background noise when computer numerical control (CNC) machine tools perform machining tasks. Additionally, frequent changes in the running speed of the spindle motor cause significant variations in the signal feature distribution, making fault diagnosis challenging. The adaptive sinusoidal fusion convolutional neural networks (ASFCNN) is proposed to achieve cross-speed spindle motor bearings fault diagnosis. The ASFCNN extracts multi-spatial and variable-scale fault features through the multi-spatial variable-scale adaptive sinusoidal filter (MVASF) for noise reduction. And a multi-level feedforward hybrid strategy (MFHS) is designed to fuse multi-layer features of the convolutional neural network (CNN) and time sequence information for fault feature enhancement. The proposed method is evaluated on a multi-source spindle motor dataset under real working conditions. Experimental results show that the ASFCNN model significantly outperforms the compared classical models in terms of diagnosis accuracy, the effectiveness and interpretability are validated through the visualization methods.
{"title":"Cross-speed spindle motor bearings fault diagnosis combined with multi-space variable scale adaptive filter and feedforward hybrid strategy","authors":"Hao Zhou, Jianzhong Yang, Qian Zhu, Jihong Chen","doi":"10.1016/j.isatra.2024.11.045","DOIUrl":"10.1016/j.isatra.2024.11.045","url":null,"abstract":"<div><div>The vibration signal of the spindle motor contains complicated mixed modulation harmonics and background noise when computer numerical control (CNC) machine tools perform machining tasks. Additionally, frequent changes in the running speed of the spindle motor cause significant variations in the signal feature distribution, making fault diagnosis challenging. The adaptive sinusoidal fusion convolutional neural networks (ASFCNN) is proposed to achieve cross-speed spindle motor bearings fault diagnosis. The ASFCNN extracts multi-spatial and variable-scale fault features through the multi-spatial variable-scale adaptive sinusoidal filter (MVASF) for noise reduction. And a multi-level feedforward hybrid strategy (MFHS) is designed to fuse multi-layer features of the convolutional neural network (CNN) and time sequence information for fault feature enhancement. The proposed method is evaluated on a multi-source spindle motor dataset under real working conditions. Experimental results show that the ASFCNN model significantly outperforms the compared classical models in terms of diagnosis accuracy, the effectiveness and interpretability are validated through the visualization methods.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 368-380"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796552","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.2024.12.001
Mingyan Xie, Ti Chen, Shihao Ni, Chenlu Feng
This study focuses on the dynamics and cooperative control for two space manipulators transporting the flexible payload. The assumed mode method is used to discretize the flexible component. Based on the Lagrange’s equations of second kind and Lagrange multiplier method, the dynamics model of system is built. To compensate for the disturbances from the payload acting on the manipulators, the boundary forces and torques of the payload are estimated based on the statics analysis. A radial basis function neural network (RBF NN) is adopted to approximate some unknown terms. A NN-based cooperative controller with statics compensation is proposed for such a space manipulation system to drive the manipulators and beam to the desired states. The stability of the controller is proven through Lyapunov theory. Numerical simulations via the constant-step generalized-α integrator and some experiments based on QArm platforms are performed to show the efficiency of the designed controller.
{"title":"Flexible payload transportation using cooperative space manipulators with statics compensation","authors":"Mingyan Xie, Ti Chen, Shihao Ni, Chenlu Feng","doi":"10.1016/j.isatra.2024.12.001","DOIUrl":"10.1016/j.isatra.2024.12.001","url":null,"abstract":"<div><div>This study focuses on the dynamics and cooperative control for two space manipulators transporting the flexible payload. The assumed mode method is used to discretize the flexible component. Based on the Lagrange’s equations of second kind and Lagrange multiplier method, the dynamics model of system is built. To compensate for the disturbances from the payload acting on the manipulators, the boundary forces and torques of the payload are estimated based on the statics analysis. A radial basis function neural network (RBF NN) is adopted to approximate some unknown terms. A NN-based cooperative controller with statics compensation is proposed for such a space manipulation system to drive the manipulators and beam to the desired states. The stability of the controller is proven through Lyapunov theory. Numerical simulations via the constant-step generalized-α integrator and some experiments based on QArm platforms are performed to show the efficiency of the designed controller.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 329-339"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815311","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.2024.12.018
Yuhang Ying , Xin Li , Zhikai Xu , Yang Yu , Junming Xu , Feiyun Xiao
The capability to achieve fast motion in varying road conditions is a crucial research aspect in the dynamic control of quadruped robot. In this study, a gait parameters planning system for quadruped robot based on virtual model controller (VMC) and fuzzy neural network controller (FNNC) is proposed. According to the expert knowledge, the FNNC is designed to help optimize the parameters in the central pattern generator and virtual model controller (CPG-VMC). This affect the performance of the fast motion indicated by the attitude plantar force and a weight adaptive law is designed and implemented to improve the capability of traversing unprecedented road conditions. To better analyze controller efficiency, the concept called cost of transport (CoT) is introduced to serve as the evaluation criteria for the performance of controller. Both the simulation and prototype test are implemented to validate the effect of the proposed method. Experimental results show that the FNNC-based gait parameters planning system can accurately detect the flaws in the parameters, help adjusting the parameters in real-time regarding the different road conditions, and reducing the CoT and the vibration.
在不同道路条件下实现快速运动的能力是四足机器人动态控制的一个重要研究方向。提出了一种基于虚拟模型控制器（VMC）和模糊神经网络控制器（FNNC）的四足机器人步态参数规划系统。根据专家知识，设计了FNNC来帮助优化中央模式发生器和虚拟模型控制器（CPG-VMC）的参数。设计并实现了一种权重自适应律，以提高车辆在未知路况下的行驶能力。为了更好地分析控制器的效率，引入了运输成本（cost of transport, CoT）的概念作为控制器性能的评价标准。通过仿真和样机试验验证了所提方法的有效性。实验结果表明，基于fnnc的步态参数规划系统能够准确地检测出步态参数中的缺陷，并根据不同的路况实时调整步态参数，降低步态的CoT和振动。
{"title":"Design of trot gait parameters planning system for parallel quadruped robot based on virtual model controller and fuzzy neural network","authors":"Yuhang Ying , Xin Li , Zhikai Xu , Yang Yu , Junming Xu , Feiyun Xiao","doi":"10.1016/j.isatra.2024.12.018","DOIUrl":"10.1016/j.isatra.2024.12.018","url":null,"abstract":"<div><div>The capability to achieve fast motion in varying road conditions is a crucial research aspect in the dynamic control of quadruped robot. In this study, a gait parameters planning system for quadruped robot based on virtual model controller (VMC) and fuzzy neural network controller (FNNC) is proposed. According to the expert knowledge, the FNNC is designed to help optimize the parameters in the central pattern generator and virtual model controller (CPG-VMC). This affect the performance of the fast motion indicated by the attitude plantar force and a weight adaptive law is designed and implemented to improve the capability of traversing unprecedented road conditions. To better analyze controller efficiency, the concept called cost of transport (CoT) is introduced to serve as the evaluation criteria for the performance of controller. Both the simulation and prototype test are implemented to validate the effect of the proposed method. Experimental results show that the FNNC-based gait parameters planning system can accurately detect the flaws in the parameters, help adjusting the parameters in real-time regarding the different road conditions, and reducing the CoT and the vibration.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 510-529"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823006","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.2024.12.002
Priyadarshini Mahalingam , D. Kalpana , T. Thyagarajan
Predicting the Remaining Useful Life (RUL) of an industrial pneumatic actuator is crucial for enhancing maintenance strategies, reducing downtime and optimizing resource allocation. However, estimation becomes challenging when no historical RUL data is available for modeling. In this paper, a novel hybrid prognostic approach that combines Dynamic Time Warping (DTW), Exponential Degradation Model (EDM) and Random Forest Regressor (RFR) is proposed to estimate the RUL of pneumatic actuators under the absence of apriori RUL history. The DTW technique is employed to identify the onset of potential degradation. By aligning the healthy and faulty data, DTW provides a robust measure of distance and time at the point of deviation as the threshold value. Subsequently, the EDM is introduced to capture the degradation pattern in the actuator behavior. The EDM accounts for the relationship between threshold value, operating conditions, degradation rate and exponential coefficients through curve fitting methods. To further enhance prediction accuracy, RFR is employed to predict the RUL based on input features of aligned data from DTW and the derived degradation rates from EDM. In the simulation studies, the proposed methodology is applied to a synthetic dataset and benchmark DAMADICS dataset of the industrial pneumatic actuator in sugar processing unit to estimate RUL. The estimated RUL for each health indicator is quantified and the severity of each fault is discussed. The proposed method is implemented on a real time laboratory setup. The results are also validated on the benchmark NASA turbo-engine dataset by comparing the actual and estimated RULs, achieving 82.5 % range-based accuracy.
{"title":"Estimation of remaining useful life (RUL) for pneumatic actuator without apriori RUL history: A hybrid prognostic approach","authors":"Priyadarshini Mahalingam , D. Kalpana , T. Thyagarajan","doi":"10.1016/j.isatra.2024.12.002","DOIUrl":"10.1016/j.isatra.2024.12.002","url":null,"abstract":"<div><div>Predicting the Remaining Useful Life (RUL) of an industrial pneumatic actuator is crucial for enhancing maintenance strategies, reducing downtime and optimizing resource allocation. However, estimation becomes challenging when no historical RUL data is available for modeling. In this paper, a novel hybrid prognostic approach that combines Dynamic Time Warping (DTW), Exponential Degradation Model (EDM) and Random Forest Regressor (RFR) is proposed to estimate the RUL of pneumatic actuators under the absence of apriori RUL history. The DTW technique is employed to identify the onset of potential degradation. By aligning the healthy and faulty data, DTW provides a robust measure of distance and time at the point of deviation as the threshold value. Subsequently, the EDM is introduced to capture the degradation pattern in the actuator behavior. The EDM accounts for the relationship between threshold value, operating conditions, degradation rate and exponential coefficients through curve fitting methods. To further enhance prediction accuracy, RFR is employed to predict the RUL based on input features of aligned data from DTW and the derived degradation rates from EDM. In the simulation studies, the proposed methodology is applied to a synthetic dataset and benchmark DAMADICS dataset of the industrial pneumatic actuator in sugar processing unit to estimate RUL. The estimated RUL for each health indicator is quantified and the severity of each fault is discussed. The proposed method is implemented on a real time laboratory setup. The results are also validated on the benchmark NASA turbo-engine dataset by comparing the actual and estimated RULs, achieving 82.5 % range-based accuracy.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 434-450"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823014","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.2024.12.003
Armando Miranda-Moya , Herman Castañeda , Hesheng Wang
This paper presents the design of a disturbance rejection-based control strategy for a quadrotor unmanned aerial vehicle subject to model uncertainties and external disturbances described by turbulent wind gusts of severe intensity. First, an extended state observer is introduced to supply full-state and total disturbance estimations within a fixed time regardless of initial estimation errors. Then, an adaptive non-singular fast terminal sliding mode controller with a single-gain structure is proposed to reduce the tuning complexity and drive the pose of the rotorcraft while providing practical finite-time convergence, robustness to bounded external disturbances, non-overestimation of its control gain, and chattering attenuation. Furthermore, the stability of the closed-loop system is guaranteed through homogeneity and Lyapunov theory. Simulation results obtained through the ROS/Gazebo framework demonstrate graphically and quantitatively that the proposed observer-based controller reduces the influence of perturbations and requires less torque effort than existing methods in the presence of sensor noise.
{"title":"Disturbance rejection-based adaptive non-singular fast terminal sliding mode control for a quadrotor under severe turbulent wind","authors":"Armando Miranda-Moya , Herman Castañeda , Hesheng Wang","doi":"10.1016/j.isatra.2024.12.003","DOIUrl":"10.1016/j.isatra.2024.12.003","url":null,"abstract":"<div><div>This paper presents the design of a disturbance rejection-based control strategy for a quadrotor unmanned aerial vehicle subject to model uncertainties and external disturbances described by turbulent wind gusts of severe intensity. First, an extended state observer is introduced to supply full-state and total disturbance estimations within a fixed time regardless of initial estimation errors. Then, an adaptive non-singular fast terminal sliding mode controller with a single-gain structure is proposed to reduce the tuning complexity and drive the pose of the rotorcraft while providing practical finite-time convergence, robustness to bounded external disturbances, non-overestimation of its control gain, and chattering attenuation. Furthermore, the stability of the closed-loop system is guaranteed through homogeneity and Lyapunov theory. Simulation results obtained through the ROS/Gazebo framework demonstrate graphically and quantitatively that the proposed observer-based controller reduces the influence of perturbations and requires less torque effort than existing methods in the presence of sensor noise.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 248-257"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866893","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}
Inverse kinematics, crucial in robotics, involves computing joint configurations to achieve specific end-effector positions and orientations. This task is particularly complex for six-degree-of-freedom (six-DoF) anthropomorphic robots due to complicated mathematical equations, nonlinear behaviours, multiple valid solutions, physical constraints, non-generalizability and computational demands. The primary contribution of this work is to address the complex inverse kinematics problem for six-DoF anthropomorphic robots through the systematic exploration of AI models. This study involves rigorous evaluation and Bayesian optimization for hyperparameter tuning to identify the optimal regressor, balancing both accuracy and computational efficiency. Utilizing five-fold cross-validation on a publicly available dataset, the selected model demonstrates exceptional performance in predicting six joint angles for end effector configuration, yielding an average mean square error of 1.934 × 10−3 to 3.522 × 10−3. Its computational efficiency, with a prediction time of approximately 1.25 ms per sample, makes it a practical choice. Additionally, the study employs Explainable AI, using SHAP (SHapley Additive exPlanations) analysis to gain an understanding of feature importance. This analysis not only enhances model interpretability but also reaffirms the efficacy in this challenging multi-input multi-output predictive task. This research advances state-of-the-art models and neural networks by prioritizing computational efficiency alongside accuracy—a critical yet often overlooked factor. Pioneering a significant advancement in anthropomorphic robot kinematics, it balances accuracy and efficiency, offering practical robotic automation solutions.
{"title":"Optimized inverse kinematics modeling and joint angle prediction for six-degree-of-freedom anthropomorphic robots with Explainable AI","authors":"Rakesh Chandra Joshi , Jaynendra Kumar Rai , Radim Burget , Malay Kishore Dutta","doi":"10.1016/j.isatra.2024.12.008","DOIUrl":"10.1016/j.isatra.2024.12.008","url":null,"abstract":"<div><div>Inverse kinematics, crucial in robotics, involves computing joint configurations to achieve specific end-effector positions and orientations. This task is particularly complex for six-degree-of-freedom (six-DoF) anthropomorphic robots due to complicated mathematical equations, nonlinear behaviours, multiple valid solutions, physical constraints, non-generalizability and computational demands. The primary contribution of this work is to address the complex inverse kinematics problem for six-DoF anthropomorphic robots through the systematic exploration of AI models. This study involves rigorous evaluation and Bayesian optimization for hyperparameter tuning to identify the optimal regressor, balancing both accuracy and computational efficiency. Utilizing five-fold cross-validation on a publicly available dataset, the selected model demonstrates exceptional performance in predicting six joint angles for end effector configuration, yielding an average mean square error of 1.934 × 10<sup>−3</sup> to 3.522 × 10<sup>−3</sup>. Its computational efficiency, with a prediction time of approximately 1.25 ms per sample, makes it a practical choice. Additionally, the study employs Explainable AI, using SHAP (SHapley Additive exPlanations) analysis to gain an understanding of feature importance. This analysis not only enhances model interpretability but also reaffirms the efficacy in this challenging multi-input multi-output predictive task. This research advances state-of-the-art models and neural networks by prioritizing computational efficiency alongside accuracy—a critical yet often overlooked factor. Pioneering a significant advancement in anthropomorphic robot kinematics, it balances accuracy and efficiency, offering practical robotic automation solutions.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 340-356"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873705","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.2024.12.004
Shubo Li , Tao Feng , Jilie Zhang , Fei Yan
In this paper, we propose a suboptimal distributed cooperative control scheme for the continuous-time linear multi-agent system (MAS) with a specified global quadratic cost functional over both undirected and directed graph scenarios. For undirected graphs, we first derive the cost functional for a given strictly linear feedback distributed protocol. It is shown that the cost functional is upper bounded by a quadratic form of the MAS’s initial state, and the minimum upper bound can be derived by solving a parametric algebraic Riccati equation (PARE) depends solely on the algebraic connectivity of the graph and is independent of the largest eigenvalue compared with the existing work. Based on this, a suboptimal distributed design method is proposed, where the resulting cost functional is less than a specified positive scalar. Then, we extend the theoretical results and design method to the directed graph scenario by introducing the row and column Laplacian matrices associated with the directed graph. Finally, numerical examples are provided to verify the effectiveness of the obtained results.
{"title":"Suboptimal distributed cooperative control for linear multi-agent system via Riccati design","authors":"Shubo Li , Tao Feng , Jilie Zhang , Fei Yan","doi":"10.1016/j.isatra.2024.12.004","DOIUrl":"10.1016/j.isatra.2024.12.004","url":null,"abstract":"<div><div>In this paper, we propose a suboptimal distributed cooperative control scheme for the continuous-time linear multi-agent system (MAS) with a specified global quadratic cost functional over both undirected and directed graph scenarios. For undirected graphs, we first derive the cost functional for a given strictly linear feedback distributed protocol. It is shown that the cost functional is upper bounded by a quadratic form of the MAS’s initial state, and the minimum upper bound can be derived by solving a parametric algebraic Riccati equation (PARE) depends solely on the algebraic connectivity of the graph and is independent of the largest eigenvalue compared with the existing work. Based on this, a suboptimal distributed design method is proposed, where the resulting cost functional is less than a specified positive scalar. Then, we extend the theoretical results and design method to the directed graph scenario by introducing the row and column Laplacian matrices associated with the directed graph. Finally, numerical examples are provided to verify the effectiveness of the obtained results.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 46-55"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824884","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.2024.12.017
Meng Yang , Junguo Wang , Bin Liu
In this paper, the full state dependent event-triggered aperiodic intermittent control (FE-AIC) strategy based on input constraints is introduced to minimize energy consumption and enhance speed tracking accuracy in the high-speed train (HST) operation. Firstly, a dynamic model based on multi-mass-point (MMP) for HST has been established, transforming the cruise control problem into an error asymptotic convergence problem. Secondly, restricted FE-AIC (RFE-AIC) controller is designed separately in the presence and absence of external disturbances to realize tracking objects. The proposed control scheme is not only based on control input constraints, but also intermittent control with full state event dependence. The RFE-AIC scheme and the conditions for determining parameters are given, which ensures the stability of the ideal tracking speed and coupler deviation at the equilibrium point. Eventually, the availability of the proposed method in cruise control is confirmed through numerical simulations. It is proved that the RFE-AIC has better performance compared with the self-triggered and guaranteed optimal cruise control methods.
{"title":"The restricted intermittent control for high-speed train movement via the full state dependent event-triggered method","authors":"Meng Yang , Junguo Wang , Bin Liu","doi":"10.1016/j.isatra.2024.12.017","DOIUrl":"10.1016/j.isatra.2024.12.017","url":null,"abstract":"<div><div>In this paper, the full state dependent event-triggered aperiodic intermittent control (FE-AIC) strategy based on input constraints is introduced to minimize energy consumption and enhance speed tracking accuracy in the high-speed train (HST) operation. Firstly, a dynamic model based on multi-mass-point (MMP) for HST has been established, transforming the cruise control problem into an error asymptotic convergence problem. Secondly, restricted FE-AIC (RFE-AIC) controller is designed separately in the presence and absence of external disturbances to realize tracking objects. The proposed control scheme is not only based on control input constraints, but also intermittent control with full state event dependence. The RFE-AIC scheme and the conditions for determining parameters are given, which ensures the stability of the ideal tracking speed and coupler deviation at the equilibrium point. Eventually, the availability of the proposed method in cruise control is confirmed through numerical simulations. It is proved that the RFE-AIC has better performance compared with the self-triggered and guaranteed optimal cruise control methods.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 481-495"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824978","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.2024.11.053
Hongwei Fang , Peng Yi
This work investigates a game-theoretic path planning algorithm with online objective function parameter estimation for a multiplayer intrusion-defense game, where the defenders aim to prevent intruders from entering the protected area. At first, an intruder is assigned to each defender to perform a one-to-one interception by solving an integer optimization problem. Then, the intrusion-defense game is formulated in a receding horizon manner by designing the objective function and constraints for the defenders and intruders, respectively. Their objective functions are coupled because they both consider the predicted interactions between the intruders and defenders. Therefore, a distributed proximal iterative best response scheme is designed for the group of defenders to cooperatively compute the Nash equilibrium. Each defender iteratively solves its own and its interception target’s optimization problems, and shares information within the defender group. Since the defenders cannot know the parameters of the intruders’ objective functions, an unscented Kalman filter-based estimator is constructed to online estimate the opponent’s unknown parameters. Extensive simulation experiments verify the effectiveness of the proposed method.
{"title":"Game-theoretic planning for multiplayer defense task with online objective function parameter estimation","authors":"Hongwei Fang , Peng Yi","doi":"10.1016/j.isatra.2024.11.053","DOIUrl":"10.1016/j.isatra.2024.11.053","url":null,"abstract":"<div><div>This work investigates a game-theoretic path planning algorithm with online objective function parameter estimation for a multiplayer intrusion-defense game, where the defenders aim to prevent intruders from entering the protected area. At first, an intruder is assigned to each defender to perform a one-to-one interception by solving an integer optimization problem. Then, the intrusion-defense game is formulated in a receding horizon manner by designing the objective function and constraints for the defenders and intruders, respectively. Their objective functions are coupled because they both consider the predicted interactions between the intruders and defenders. Therefore, a distributed proximal iterative best response scheme is designed for the group of defenders to cooperatively compute the Nash equilibrium. Each defender iteratively solves its own and its interception target’s optimization problems, and shares information within the defender group. Since the defenders cannot know the parameters of the intruders’ objective functions, an unscented Kalman filter-based estimator is constructed to online estimate the opponent’s unknown parameters. Extensive simulation experiments verify the effectiveness of the proposed method.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 318-328"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142923865","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.2024.12.010
Tao Chen , Liang Guo , Hongli Gao , Dong Wang , Tingting Feng , Yaoxiang Yu
Sparsity measures are commonly utilized as health indicators for machine condition monitoring. Recently, with the assistance of Box-Cox transformation, kurtosis and negative entropy have been smoothly extended to form Box-Cox sparsity measures (BCSMs). However, traditional BCSMs do not generate sparsity measures that outperform negative entropy, which means it is meaningless to some extent. Therefore, in this paper, traditional BCSMs are further extended to develop more robust sparsity measures. Firstly, inspired by the limited weight range of the Gini index, the traditional BCSMs are extended to the case of by a two-parameter Box-Cox transformation. Then, by examining the decomposition forms of norm and Hoyer measure, the advantage of directly applying the Box-Cox transformation to the sparsity measure is discovered. Thus, the improved BCSMs (IBCSMs) are naturally proposed by performing the classical Box-Cox transformation on the BCSMs with . Subsequently, three key properties of the proposed sparsity measures are analyzed through three numerical experiments. Finally, the proposed sparsity measures are deployed as health indicators to characterize the degradation process of three bearings. Numerical and experimental results demonstrate the salient advantages of the proposed IBCSMs in incipient fault detection.
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