In the last years, our cities become more and more crowded due to the increasing number of cars into old city planes. So, even small/medium cities experience a travel time comparable with the bigger ones. To improve mobility management in modern cities, specific simulation tools can be used to analyze the impact of different mobility plans on mobility and, therefore to find the most suitable solution for each city. However, these tools are often hard to be used by city traffic managers without advanced computer skills. In this article, we used a multiagent transport simulation (MATSim) to provide a simple tool that can be easily used by end-users to better plan mobility strategies for both private and public transportation. In particular, starting from the open data provided by the city of Messina, we have implemented a software tool able to process MATSim events. Moreover, we propose a metric to estimate the safety of roads for cyclists. From the experimental results provided by the proposed software, we are able to discover the most overloaded links and estimate the travel time distribution by hour of departure time.
{"title":"Design and Analysis of a MATSim Scenario From Open Data: The Messina City Use Case","authors":"Annamaria Ficara;Maria Fazio;Antonio Celesti;Massimo Villari","doi":"10.1109/TSMC.2024.3490854","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3490854","url":null,"abstract":"In the last years, our cities become more and more crowded due to the increasing number of cars into old city planes. So, even small/medium cities experience a travel time comparable with the bigger ones. To improve mobility management in modern cities, specific simulation tools can be used to analyze the impact of different mobility plans on mobility and, therefore to find the most suitable solution for each city. However, these tools are often hard to be used by city traffic managers without advanced computer skills. In this article, we used a multiagent transport simulation (MATSim) to provide a simple tool that can be easily used by end-users to better plan mobility strategies for both private and public transportation. In particular, starting from the open data provided by the city of Messina, we have implemented a software tool able to process MATSim events. Moreover, we propose a metric to estimate the safety of roads for cyclists. From the experimental results provided by the proposed software, we are able to discover the most overloaded links and estimate the travel time distribution by hour of departure time.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"768-779"},"PeriodicalIF":8.6,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 censored regression (CR)-based normalized subband adaptive algorithm (CR-NSAF) model has been recently introduced for processing signals with censored data. However, the effectiveness of this algorithm declines when dealing with noisy input signals in impulsive noise environments. To resolve this challenge, we propose a robust bias-compensated CR-NSAF algorithm (RBC-CRNSAF). This algorithm alleviates the negative impacts of the CR system and improves robustness by employing a logarithmic cost function approach. It also minimizes estimation bias from input noise by incorporating new compensation terms into the weights update function. Additionally, we analyze the computational complexity, convergence characteristics, and stability conditions of the algorithm. Finally, computer simulations indicate that RBC-CRNSAF considerably outperforms other similar algorithms in impulsive noise environments, validating its enhanced performance.
{"title":"Robust Bias-Compensated CR-NSAF Algorithm: Design and Performance Analysis","authors":"Pengwei Wen;Bolin Wang;Boyang Qu;Sheng Zhang;Haiquan Zhao;Jing Liang","doi":"10.1109/TSMC.2024.3491188","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3491188","url":null,"abstract":"The censored regression (CR)-based normalized subband adaptive algorithm (CR-NSAF) model has been recently introduced for processing signals with censored data. However, the effectiveness of this algorithm declines when dealing with noisy input signals in impulsive noise environments. To resolve this challenge, we propose a robust bias-compensated CR-NSAF algorithm (RBC-CRNSAF). This algorithm alleviates the negative impacts of the CR system and improves robustness by employing a logarithmic cost function approach. It also minimizes estimation bias from input noise by incorporating new compensation terms into the weights update function. Additionally, we analyze the computational complexity, convergence characteristics, and stability conditions of the algorithm. Finally, computer simulations indicate that RBC-CRNSAF considerably outperforms other similar algorithms in impulsive noise environments, validating its enhanced performance.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"674-684"},"PeriodicalIF":8.6,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1109/TSMC.2024.3491317
Xinyang Deng;Wen Jiang
Quantifying the epistemic uncertainty for static information and dynamic fusion or reasoning processes is still unsolved for various epistemic uncertainty theories. This study focuses on the Dempster-Shafer evidence theory, which is of great ability in representing and fusing uncertain information with imprecision and ignorance on the basis of basic probability assignment (BPA) and Dempster combination rule (DCR). In order to effectively measure and infer the epistemic uncertainty for both static BPAs and dynamic fusion processes, a solution based on plausibility entropy is proposed in this study. At first, four new properties, called grouping, splitting, weighted additivity, and weighted subadditivity, are proved for the first time in this study to strengthen the theoretical foundation of plausibility entropy in measuring the uncertainty associated with a given BPA. Second, the upper bounds of uncertainty are derived for typical BPA-based multisource information fusion systems, including standard DCR, weighted DCR, discounted DCR fusion systems for evidence defined on the same frame of discernment (FOD), and the DCR fusion system for evidence defined on multiple distinct FODs. Several examples are given to illustrate these results.
{"title":"Upper Bounds of Uncertainty for Dempster Combination Rule-Based Evidence Fusion Systems","authors":"Xinyang Deng;Wen Jiang","doi":"10.1109/TSMC.2024.3491317","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3491317","url":null,"abstract":"Quantifying the epistemic uncertainty for static information and dynamic fusion or reasoning processes is still unsolved for various epistemic uncertainty theories. This study focuses on the Dempster-Shafer evidence theory, which is of great ability in representing and fusing uncertain information with imprecision and ignorance on the basis of basic probability assignment (BPA) and Dempster combination rule (DCR). In order to effectively measure and infer the epistemic uncertainty for both static BPAs and dynamic fusion processes, a solution based on plausibility entropy is proposed in this study. At first, four new properties, called grouping, splitting, weighted additivity, and weighted subadditivity, are proved for the first time in this study to strengthen the theoretical foundation of plausibility entropy in measuring the uncertainty associated with a given BPA. Second, the upper bounds of uncertainty are derived for typical BPA-based multisource information fusion systems, including standard DCR, weighted DCR, discounted DCR fusion systems for evidence defined on the same frame of discernment (FOD), and the DCR fusion system for evidence defined on multiple distinct FODs. Several examples are given to illustrate these results.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"817-828"},"PeriodicalIF":8.6,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1109/TSMC.2024.3490553
Dinh Hoa Nguyen
In this research, a battery control method is proposed to handle the repetitive but nonidentical daily state-of-charge (SoC) profiles of electric vehicle (EV) batteries. The proposed method employs an iterative learning control (ILC) framework having a quadratic performance index with iteration-varying weighting matrices. This results in iteration-varying ILC control gains to better cope with iteration-varying SoC profiles. Moreover, input constraints representing the limits on the ranges of the charge and discharge currents are considered, leading to an iteration-varying constrained convex optimization problem. This optimization problem is solved to obtain the ILC control input update via resolving its Lagrange dual problem. Next, a data-driven method based on the dynamic mode decomposition (DMD) approach is proposed to predict the SoC profile in the next weekday based on the SoC profiles in the current and previous weekdays. The predicted SoC profile is then served as the reference for the ILC tracking controller. Finally, the proposed methods are verified through extensive numerical simulations for a synthetic case and for a realistic, benchmark driving pattern. In the simulations, different ways of selecting the iteration-varying weighting matrices are introduced and their control performances are compared. It is also shown that the proposed ILC control design outperforms conventional P-type and adaptive ILC controllers as well as the classical proportional-integral-derivative controller on the tracking of the SoC profile based on the considered realistic driving pattern.
{"title":"Iterative Learning Control Design for Iteration-Varying State-of-Charge Profiles of Electric Vehicle Batteries","authors":"Dinh Hoa Nguyen","doi":"10.1109/TSMC.2024.3490553","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3490553","url":null,"abstract":"In this research, a battery control method is proposed to handle the repetitive but nonidentical daily state-of-charge (SoC) profiles of electric vehicle (EV) batteries. The proposed method employs an iterative learning control (ILC) framework having a quadratic performance index with iteration-varying weighting matrices. This results in iteration-varying ILC control gains to better cope with iteration-varying SoC profiles. Moreover, input constraints representing the limits on the ranges of the charge and discharge currents are considered, leading to an iteration-varying constrained convex optimization problem. This optimization problem is solved to obtain the ILC control input update via resolving its Lagrange dual problem. Next, a data-driven method based on the dynamic mode decomposition (DMD) approach is proposed to predict the SoC profile in the next weekday based on the SoC profiles in the current and previous weekdays. The predicted SoC profile is then served as the reference for the ILC tracking controller. Finally, the proposed methods are verified through extensive numerical simulations for a synthetic case and for a realistic, benchmark driving pattern. In the simulations, different ways of selecting the iteration-varying weighting matrices are introduced and their control performances are compared. It is also shown that the proposed ILC control design outperforms conventional P-type and adaptive ILC controllers as well as the classical proportional-integral-derivative controller on the tracking of the SoC profile based on the considered realistic driving pattern.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"805-816"},"PeriodicalIF":8.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Photovoltaic (PV) power forecasting is important for promoting the integration of renewable energy sources. However, neural network-based methods, particularly deep learning for PV power forecasting, face challenges with computational and memory requirements when dealing with industry-scale datasets. To address this, we introduce Rec-PF, a robust computational framework employing the tensor-train (TT) technique. This framework aims to streamline the training process of massive deep learning recommendation models (DLRMs) on constrained resources. Rec-PF employs a high-performance compressed embedding table, enhancing TT decomposition using key computing primitives. It serves as a drop-in replacement for the PyTorch API. Additionally, Rec-PF utilizes an index reordering technique to leverage local and global information from training inputs, thereby enhancing performance. Furthermore, Rec-PF adopts a pipeline training model, eliminating the need for communication between training workers and host memory. We are pioneers in applying DLRM to PV power prediction to reduce training time without compromising accuracy. Our approach demonstrates a twofold improvement in training time compared to methods that do not incorporate our approach. To better demonstrate the enhanced performance of the algorithm, we specifically compare its efficiency with other frameworks using datasets commonly employed in recommender systems. Comprehensive experiments indicate that Rec-PF is capable of processing the largest publicly accessible DLRM and PV datasets on a single GPU, offering a threefold acceleration compared to state-of-the-art DLRM and PV frameworks.
{"title":"Rec-PF: Data-Driven Large-Scale Deep Learning Recommendation Model Training Optimization Based on Tensor-Train Embedding Table With Photovoltaic Forecast","authors":"Yunfeng Li;Zheng Wang;Chenhao Ren;Xiaoming Hou;Shengli Zhang","doi":"10.1109/TSMC.2024.3485960","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3485960","url":null,"abstract":"Photovoltaic (PV) power forecasting is important for promoting the integration of renewable energy sources. However, neural network-based methods, particularly deep learning for PV power forecasting, face challenges with computational and memory requirements when dealing with industry-scale datasets. To address this, we introduce Rec-PF, a robust computational framework employing the tensor-train (TT) technique. This framework aims to streamline the training process of massive deep learning recommendation models (DLRMs) on constrained resources. Rec-PF employs a high-performance compressed embedding table, enhancing TT decomposition using key computing primitives. It serves as a drop-in replacement for the PyTorch API. Additionally, Rec-PF utilizes an index reordering technique to leverage local and global information from training inputs, thereby enhancing performance. Furthermore, Rec-PF adopts a pipeline training model, eliminating the need for communication between training workers and host memory. We are pioneers in applying DLRM to PV power prediction to reduce training time without compromising accuracy. Our approach demonstrates a twofold improvement in training time compared to methods that do not incorporate our approach. To better demonstrate the enhanced performance of the algorithm, we specifically compare its efficiency with other frameworks using datasets commonly employed in recommender systems. Comprehensive experiments indicate that Rec-PF is capable of processing the largest publicly accessible DLRM and PV datasets on a single GPU, offering a threefold acceleration compared to state-of-the-art DLRM and PV frameworks.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"573-586"},"PeriodicalIF":8.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1109/TSMC.2024.3488961
Zihan Li;Dong Shen;Daniel W. C. Ho
Finite- and fixed-time parameter estimation and adaptive control have been extensively investigated in recent years. This study proposes a finite- and fixed-time learning control framework to achieve simultaneous finite/fixed-time parameter estimation and control. The proposed learning control method first estimates unknown parameters and then uses these estimates to improve the control performance. Therefore, we first consider the convergence condition of finite/fixed-time parameter estimation. Next, a novel learning-based finite/fixed control law is designed. Unlike most existing adaptation laws, the estimate is updated to improve the understanding of the system rather than eliminate the influence of uncertainties. The finite/fixed-time convergence of the system states is analyzed using a direct dynamic analysis method that differs from the long-used Lyapunov method. We show that the proposed control input satisfies the excitation condition of the finite/fixed-time estimation, indicating simultaneous estimation and control. Finally, numerical simulations are performed to verify the theoretical results.
{"title":"Finite- and Fixed-Time Learning Control for Continuous-Time Nonlinear Systems","authors":"Zihan Li;Dong Shen;Daniel W. C. Ho","doi":"10.1109/TSMC.2024.3488961","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3488961","url":null,"abstract":"Finite- and fixed-time parameter estimation and adaptive control have been extensively investigated in recent years. This study proposes a finite- and fixed-time learning control framework to achieve simultaneous finite/fixed-time parameter estimation and control. The proposed learning control method first estimates unknown parameters and then uses these estimates to improve the control performance. Therefore, we first consider the convergence condition of finite/fixed-time parameter estimation. Next, a novel learning-based finite/fixed control law is designed. Unlike most existing adaptation laws, the estimate is updated to improve the understanding of the system rather than eliminate the influence of uncertainties. The finite/fixed-time convergence of the system states is analyzed using a direct dynamic analysis method that differs from the long-used Lyapunov method. We show that the proposed control input satisfies the excitation condition of the finite/fixed-time estimation, indicating simultaneous estimation and control. Finally, numerical simulations are performed to verify the theoretical results.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"792-804"},"PeriodicalIF":8.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 the face of environmental deterioration and global climate change, the concept of carbon neutrality and carbon peaking has gained prominence as a means to balance development and environmental preservation worldwide. Energy-aware scheduling is becoming the key scenario for environment conservation in manufacturing. This study focuses on addressing the energy-aware distributed no-wait flow-shop scheduling problem in a heterogeneous factory system (EDNWFSP-HFS) to minimize total energy consumption (TEC) and total tardiness (TTDs). A mixed-integer linear programming (MILP) model is formulated and a policy-based meta-heuristic algorithm (MHA-PG) is specifically designed to solve EDNWFSP-HFS. First, the optimal allocation rules based on random sequence (OAR-RS) are designed to initialize the population. Second, a policy-based method is employed to guide the algorithm toward making a better decision. Third, the energy-saving strategy considering specific knowledge of EDNWFSP-HFS is summarized to further optimize the feasible solution. Extensive simulations are conducted, comparing the performance of MHA-PG against several state-of-the-art algorithms. The results demonstrate that the proposed algorithm outperforms the competing approaches in solving EDNWFSP-HFS, indicating its superior performance and effectiveness.
{"title":"A Policy-Based Meta-Heuristic Algorithm for Energy-Aware Distributed No-Wait Flow-Shop Scheduling in Heterogeneous Factory Systems","authors":"Fuqing Zhao;Lisi Song;Tao Jiang;Ling Wang;Chenxin Dong","doi":"10.1109/TSMC.2024.3488205","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3488205","url":null,"abstract":"In the face of environmental deterioration and global climate change, the concept of carbon neutrality and carbon peaking has gained prominence as a means to balance development and environmental preservation worldwide. Energy-aware scheduling is becoming the key scenario for environment conservation in manufacturing. This study focuses on addressing the energy-aware distributed no-wait flow-shop scheduling problem in a heterogeneous factory system (EDNWFSP-HFS) to minimize total energy consumption (TEC) and total tardiness (TTDs). A mixed-integer linear programming (MILP) model is formulated and a policy-based meta-heuristic algorithm (MHA-PG) is specifically designed to solve EDNWFSP-HFS. First, the optimal allocation rules based on random sequence (OAR-RS) are designed to initialize the population. Second, a policy-based method is employed to guide the algorithm toward making a better decision. Third, the energy-saving strategy considering specific knowledge of EDNWFSP-HFS is summarized to further optimize the feasible solution. Extensive simulations are conducted, comparing the performance of MHA-PG against several state-of-the-art algorithms. The results demonstrate that the proposed algorithm outperforms the competing approaches in solving EDNWFSP-HFS, indicating its superior performance and effectiveness.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"620-634"},"PeriodicalIF":8.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1109/TSMC.2024.3485652
Haoze Li;Guannan Li;Sitian Qin
In this article, a neurodynamic approach based on event-triggered mechanism for solving Lyapunov matrix equation is established. First, employing matrix decomposition technique, the Lyapunov matrix equation is reformulated as a distributed optimization problem. Then, a distributed neurodynamic approach is constructed to solve the corresponding distributed optimization problem owing to its better-parallel computing ability. In order to protect the privacy of agents and fulfill the distributed communication, a primal-dual method with auxiliary variables is introduced. Agents collaborate to solve distributed optimization problem by interacting with auxiliary variables rather than decision variables. Besides, to reduce the communication cost and frequency between agents, the neurodynamic approach incorporates an event-triggered mechanism for Lyapunov matrix equation for the first time. Through theoretical analysis, it is proved that the state solution of the proposed neurodynamic approach converges exponentially and no Zeno behavior occurs. Finally, a numerical example is given to show the feasibility and effectiveness of the proposed event-triggered neurodynamic approach.
{"title":"A Distributed Event-Triggered Neurodynamic Approach for Lyapunov Matrix Equation","authors":"Haoze Li;Guannan Li;Sitian Qin","doi":"10.1109/TSMC.2024.3485652","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3485652","url":null,"abstract":"In this article, a neurodynamic approach based on event-triggered mechanism for solving Lyapunov matrix equation is established. First, employing matrix decomposition technique, the Lyapunov matrix equation is reformulated as a distributed optimization problem. Then, a distributed neurodynamic approach is constructed to solve the corresponding distributed optimization problem owing to its better-parallel computing ability. In order to protect the privacy of agents and fulfill the distributed communication, a primal-dual method with auxiliary variables is introduced. Agents collaborate to solve distributed optimization problem by interacting with auxiliary variables rather than decision variables. Besides, to reduce the communication cost and frequency between agents, the neurodynamic approach incorporates an event-triggered mechanism for Lyapunov matrix equation for the first time. Through theoretical analysis, it is proved that the state solution of the proposed neurodynamic approach converges exponentially and no Zeno behavior occurs. Finally, a numerical example is given to show the feasibility and effectiveness of the proposed event-triggered neurodynamic approach.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"563-572"},"PeriodicalIF":8.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1109/TSMC.2024.3490372
Chidentree Treesatayapun
This article presents a novel adaptive control approach for a class of unknown discrete-time systems using piecewise derivatives derived from experimentally obtained input-output characteristics of the controlled plant. The control law is formulated using a multi-input fuzzy rules emulated network (MiFREN). The learning law is developed to address the issue of catastrophic forgetting, in alignment with the proposed information matrix. Closed-loop analysis demonstrates convergence of the tracking error and weight parameters under feasible conditions. Validation through experiments with a DC-motor torque control system, alongside comparative controllers, demonstrates the superior tracking performance of the proposed method and its effective mitigation of forgetting during tracking tasks.
{"title":"Equivalent Piecewise Derivative Adaptive Control With Fuzzy Rules Emulated Network and Mitigation of Catastrophic Forgetting Learning","authors":"Chidentree Treesatayapun","doi":"10.1109/TSMC.2024.3490372","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3490372","url":null,"abstract":"This article presents a novel adaptive control approach for a class of unknown discrete-time systems using piecewise derivatives derived from experimentally obtained input-output characteristics of the controlled plant. The control law is formulated using a multi-input fuzzy rules emulated network (MiFREN). The learning law is developed to address the issue of catastrophic forgetting, in alignment with the proposed information matrix. Closed-loop analysis demonstrates convergence of the tracking error and weight parameters under feasible conditions. Validation through experiments with a DC-motor torque control system, alongside comparative controllers, demonstrates the superior tracking performance of the proposed method and its effective mitigation of forgetting during tracking tasks.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"758-767"},"PeriodicalIF":8.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1109/TSMC.2024.3489587
Wen Yang;Yulian Jiang;Yanzheng Zhu;Hongjing Liang;Shenquan Wang
It is greatly desirable to carry out the secure practical prescribed-time human-robot co-transportation task. The implementation of such application becomes even more theoretical and practical challenge if uncertainties in the robot model, unmeasured velocity vector and multiple-dynamic-obstacles environment are involved, yet certain behavior indices are also pursued. In this work, a settling time regulator is introduced and it is integrated with the dynamic surface-based backstepping design embedded with specific system transformation. This results in a solution that both constrained and unconstrained cases can be accommodated uniformly, concurrently, the settling time and tracking precision can be preset by user as required. Furthermore, a fuzzy velocity observer is designed with aid of the fuzzy logic technique, which is nontrivial to perform a control design of robot dynamics with unmeasured velocity vector and modeling uncertainties. In particular, benefiting from integral multiplicative barrier-Lyapunov function, an improved adaptive obstacle-avoiding controller is designed, which, without control singular issue, is capable of achieving desired tracking while avoiding obstacles encountered. The validity and benefits of the resultant control strategy are eventually substantiated via the simulation results of a two-DOF robotic manipulator.
{"title":"Practical Prescribed-Time Control for Constrained Human–Robot Co-Transportation With Velocity Observer and Obstacle Avoidance","authors":"Wen Yang;Yulian Jiang;Yanzheng Zhu;Hongjing Liang;Shenquan Wang","doi":"10.1109/TSMC.2024.3489587","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3489587","url":null,"abstract":"It is greatly desirable to carry out the secure practical prescribed-time human-robot co-transportation task. The implementation of such application becomes even more theoretical and practical challenge if uncertainties in the robot model, unmeasured velocity vector and multiple-dynamic-obstacles environment are involved, yet certain behavior indices are also pursued. In this work, a settling time regulator is introduced and it is integrated with the dynamic surface-based backstepping design embedded with specific system transformation. This results in a solution that both constrained and unconstrained cases can be accommodated uniformly, concurrently, the settling time and tracking precision can be preset by user as required. Furthermore, a fuzzy velocity observer is designed with aid of the fuzzy logic technique, which is nontrivial to perform a control design of robot dynamics with unmeasured velocity vector and modeling uncertainties. In particular, benefiting from integral multiplicative barrier-Lyapunov function, an improved adaptive obstacle-avoiding controller is designed, which, without control singular issue, is capable of achieving desired tracking while avoiding obstacles encountered. The validity and benefits of the resultant control strategy are eventually substantiated via the simulation results of a two-DOF robotic manipulator.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"747-757"},"PeriodicalIF":8.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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