Pub Date : 2024-08-13DOI: 10.1109/TSMC.2024.3426923
Dongdong Li;Jiuxiang Dong
In this article, a feasible reinforcement learning (RL) scheme is proposed for partially unknown fractional-order nonlinear systems (FONSs). First, the fractional Hamilton-Jacobi–Bellman (HJB) equation containing the dynamics of FONSs is proposed by constructing an auxiliary system and equivalent transformation. Then, the optimal solution of FONSs optimal control under a performance constraint is obtained. It is proved that the optimal cost function and optimal control policy can be approximated gradually by the policy iteration. By using the backstepping control, RL, and identifier-actor-critic neural networks (NNs), the unknown dynamics functions are approximated and the approximate optimal controllers are obtained. A Lyapunov function based on optimality error is constructed, then the fractional-order update laws of NNs weights are designed to ensure that the weights converge to the optimum. Thus, the use of the gradient descent algorithm in the context of the fractional-order calculus to train the NNs is avoided. Finally, the error signals are proved to be bounded and the effectiveness of the proposed algorithm is verified by the simulation of two practical examples.
{"title":"Approximate Optimized Backstepping Control of Uncertain Fractional-Order Nonlinear Systems Based on Reinforcement Learning","authors":"Dongdong Li;Jiuxiang Dong","doi":"10.1109/TSMC.2024.3426923","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3426923","url":null,"abstract":"In this article, a feasible reinforcement learning (RL) scheme is proposed for partially unknown fractional-order nonlinear systems (FONSs). First, the fractional Hamilton-Jacobi–Bellman (HJB) equation containing the dynamics of FONSs is proposed by constructing an auxiliary system and equivalent transformation. Then, the optimal solution of FONSs optimal control under a performance constraint is obtained. It is proved that the optimal cost function and optimal control policy can be approximated gradually by the policy iteration. By using the backstepping control, RL, and identifier-actor-critic neural networks (NNs), the unknown dynamics functions are approximated and the approximate optimal controllers are obtained. A Lyapunov function based on optimality error is constructed, then the fractional-order update laws of NNs weights are designed to ensure that the weights converge to the optimum. Thus, the use of the gradient descent algorithm in the context of the fractional-order calculus to train the NNs is avoided. Finally, the error signals are proved to be bounded and the effectiveness of the proposed algorithm is verified by the simulation of two practical examples.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443082","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 : 2024-08-09DOI: 10.1109/TSMC.2024.3426931
Jun Zhang;Yi Zuo;Shaocheng Tong
In this article, the adaptive fuzzy dynamic event-triggered output feedback resilient consensus control issue is investigated for nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks. Fuzzy logic systems (FLSs) are employed to model uncertain agents, and a state observer is constructed to estimate unmeasurable states. An event-triggered distributed resilient observer is designed to save the communication resources between agents, and estimate the unknown leader and its high-order derivatives in case of the communication topology being interrupted by DoS attacks. By the designed state observer and distributed resilient observer, a dynamic event-triggered resilient consensus control method is presented. It is proved that the controlled MASs are stable, and the followers can track the leader under DoS attacks. Moreover, the Zeno behavior can be excluded. Finally, we apply the developed resilient consensus control algorithm to multiple unmanned surface vehicles (USVs), the simulation results verify its effectiveness.
本文研究了受拒绝服务（DoS）攻击的非线性多代理系统（MAS）的自适应模糊动态事件触发输出反馈弹性共识控制问题。研究采用模糊逻辑系统（FLS）来模拟不确定的代理，并构建了一个状态观测器来估计不可测量的状态。设计了一个事件触发的分布式弹性观测器，以节省代理之间的通信资源，并在通信拓扑被 DoS 攻击中断时估计未知领导者及其高阶导数。通过所设计的状态观测器和分布式弹性观测器，提出了一种动态事件触发弹性共识控制方法。实验证明，受控的 MAS 是稳定的，在 DoS 攻击下，跟随者可以跟踪领导者。此外，还可以排除 Zeno 行为。最后，我们将所开发的弹性共识控制算法应用于多个无人水面飞行器（USV），仿真结果验证了该算法的有效性。
{"title":"Dynamic Event-Triggered Fuzzy Adaptive Resilient Consensus Control for Nonlinear MASs Under DoS Attacks","authors":"Jun Zhang;Yi Zuo;Shaocheng Tong","doi":"10.1109/TSMC.2024.3426931","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3426931","url":null,"abstract":"In this article, the adaptive fuzzy dynamic event-triggered output feedback resilient consensus control issue is investigated for nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks. Fuzzy logic systems (FLSs) are employed to model uncertain agents, and a state observer is constructed to estimate unmeasurable states. An event-triggered distributed resilient observer is designed to save the communication resources between agents, and estimate the unknown leader and its high-order derivatives in case of the communication topology being interrupted by DoS attacks. By the designed state observer and distributed resilient observer, a dynamic event-triggered resilient consensus control method is presented. It is proved that the controlled MASs are stable, and the followers can track the leader under DoS attacks. Moreover, the Zeno behavior can be excluded. Finally, we apply the developed resilient consensus control algorithm to multiple unmanned surface vehicles (USVs), the simulation results verify its effectiveness.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443057","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 : 2024-08-09DOI: 10.1109/TSMC.2024.3426083
Giulio Ferro;Michela Robba;Roberto Sacile
Shortly, power distribution grids will incorporate large amounts of distributed energy resources and flexible loads, allowing the operation of a portion of the network in islanded mode to increase the reliability and resilience of the whole power system. A fully distributed robust model predictive control (MPC) strategy for voltage and frequency regulation in interconnected distribution grids is stated. Each grid node represents a collection of prosumers with a large active and reactive power regulation capacity. The advantages of this approach rely on the capability to afford any type of uncertainties, without making any assumption on the probability density function, on distributed generation and load nowcasting. We propose a two-stage architecture: at the first stage, an MPC approach, based on the distributed alternating direction method of multipliers (dADMM), is performed, considering the data nowcasting; instead, the second stage (based on robust distributed team decision theory) takes as input the trajectory of the first stage to compensate the noise that affects the system. The developed architecture has been tested on a modified IEEE5 bus system, considering multiple loads and renewable generation.
{"title":"A Fully Distributed Robust MPC Approach for Frequency and Voltage Regulation in Smart Grids With Active and Reactive Power Constraints","authors":"Giulio Ferro;Michela Robba;Roberto Sacile","doi":"10.1109/TSMC.2024.3426083","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3426083","url":null,"abstract":"Shortly, power distribution grids will incorporate large amounts of distributed energy resources and flexible loads, allowing the operation of a portion of the network in islanded mode to increase the reliability and resilience of the whole power system. A fully distributed robust model predictive control (MPC) strategy for voltage and frequency regulation in interconnected distribution grids is stated. Each grid node represents a collection of prosumers with a large active and reactive power regulation capacity. The advantages of this approach rely on the capability to afford any type of uncertainties, without making any assumption on the probability density function, on distributed generation and load nowcasting. We propose a two-stage architecture: at the first stage, an MPC approach, based on the distributed alternating direction method of multipliers (dADMM), is performed, considering the data nowcasting; instead, the second stage (based on robust distributed team decision theory) takes as input the trajectory of the first stage to compensate the noise that affects the system. The developed architecture has been tested on a modified IEEE5 bus system, considering multiple loads and renewable generation.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443017","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 : 2024-08-06DOI: 10.1109/TSMC.2024.3432615
Lien-Wu Chen;Chi-Ren Chen
This study proposes a centimeter-level indoor positioning (CLIP) framework to achieve highly accurate localization with facing direction detection for the microlocation-aware Internet of Things (IoT). The CLIP framework can provide accurate centimeter-level positioning information to people indoors by integrating installed surveillance cameras with the IoT, where the efficient operation of microlocation-aware IoT applications and services can be enabled for smart spaces. CLIP can be used to accurately determine the position and facing direction of an individual. According to our review of relevant research, CLIP is the first indoor positioning framework that includes the following features: 1) centimeter-level positioning accuracy for the microlocation-aware IoT that can detect the facing direction of individuals; 2) employment of existing surveillance cameras with low-additional installation cost; and 3) innovative infrastructure for microlocation-aware IoT applications that can enable accurate centimeter-level path planning for individuals, emergency evacuation for groups of people, and geofencing with microlocation awareness. An Android-based system was implemented to verify the feasibility and effectiveness of the CLIP framework, and experimental results indicate that CLIP outperforms existing indoor positioning methods and can achieve centimeter-level accuracy with the improvement ratio of 94.6% over Sextant.
{"title":"Centimeter-Level Indoor Positioning With Facing Direction Detection for Microlocation-Aware Services","authors":"Lien-Wu Chen;Chi-Ren Chen","doi":"10.1109/TSMC.2024.3432615","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3432615","url":null,"abstract":"This study proposes a centimeter-level indoor positioning (CLIP) framework to achieve highly accurate localization with facing direction detection for the microlocation-aware Internet of Things (IoT). The CLIP framework can provide accurate centimeter-level positioning information to people indoors by integrating installed surveillance cameras with the IoT, where the efficient operation of microlocation-aware IoT applications and services can be enabled for smart spaces. CLIP can be used to accurately determine the position and facing direction of an individual. According to our review of relevant research, CLIP is the first indoor positioning framework that includes the following features: 1) centimeter-level positioning accuracy for the microlocation-aware IoT that can detect the facing direction of individuals; 2) employment of existing surveillance cameras with low-additional installation cost; and 3) innovative infrastructure for microlocation-aware IoT applications that can enable accurate centimeter-level path planning for individuals, emergency evacuation for groups of people, and geofencing with microlocation awareness. An Android-based system was implemented to verify the feasibility and effectiveness of the CLIP framework, and experimental results indicate that CLIP outperforms existing indoor positioning methods and can achieve centimeter-level accuracy with the improvement ratio of 94.6% over Sextant.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275005","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 : 2024-08-06DOI: 10.1109/TSMC.2024.3431222
Zixiang Nie;Kwang-Cheng Chen
Smart factories employ intelligent transportaton systems such as autonomous mobile robots (AMRs) to support real-time adjusted production flows for agile and flexible production. While decentralized transportation task execution provides a scalable multirobot system (MRS) for a smart factory, new coordination challenges arise in implementing such a system. Transportation-MRS collaborates with production-MRS to accommodate just-in-time (JIT) production, leading to nonstationary transportation tasks that transportation-MRS must learn and adapt to. Also, decentralized operation on a shared shop floor means that one robot cannot factor in peer robots’ task execution planning, leading to competitive collisions. Meanwhile, predictive coordination with communication among multiple learning and adapting intelligent robots is still an open problem. On top of identifying the aforementioned challenges, this article first proposes a multifloor transportation graph model to discretize transportation task execution and allow real-time adjustment of transportation paths toward collision-free. We introduce a unique collaborative multi-intelligent robot system approach taking each robot as a cyber–physical agent with automated artificial intelligence (AI) workflow. First, it includes a novel multiagent reinforcement learning (MARL) algorithm, where each robot predictively plans collision-avoidant paths. Second, we introduce a token-passing mechanism to resolve inevitable competitive collisions due to nonstationary tasks. The proposed approach innovatively uses the multifloor model as a domain model for planning. By allowing competitive collision to occur and resolve, a robot only needs to learn and adapt to uncertain parts of the environment—nonstationary tasks and peer robots’ paths. Computational experiments show that our approach is both sample-efficient and computationally efficient. The transportation-MRS quickly reaches near-optimal performance levels, which are empirically shown to scale with the number of robots involved.
{"title":"Predictive Path Coordination of Collaborative Transportation Multirobot System in a Smart Factory","authors":"Zixiang Nie;Kwang-Cheng Chen","doi":"10.1109/TSMC.2024.3431222","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3431222","url":null,"abstract":"Smart factories employ intelligent transportaton systems such as autonomous mobile robots (AMRs) to support real-time adjusted production flows for agile and flexible production. While decentralized transportation task execution provides a scalable multirobot system (MRS) for a smart factory, new coordination challenges arise in implementing such a system. Transportation-MRS collaborates with production-MRS to accommodate just-in-time (JIT) production, leading to nonstationary transportation tasks that transportation-MRS must learn and adapt to. Also, decentralized operation on a shared shop floor means that one robot cannot factor in peer robots’ task execution planning, leading to competitive collisions. Meanwhile, predictive coordination with communication among multiple learning and adapting intelligent robots is still an open problem. On top of identifying the aforementioned challenges, this article first proposes a multifloor transportation graph model to discretize transportation task execution and allow real-time adjustment of transportation paths toward collision-free. We introduce a unique collaborative multi-intelligent robot system approach taking each robot as a cyber–physical agent with automated artificial intelligence (AI) workflow. First, it includes a novel multiagent reinforcement learning (MARL) algorithm, where each robot predictively plans collision-avoidant paths. Second, we introduce a token-passing mechanism to resolve inevitable competitive collisions due to nonstationary tasks. The proposed approach innovatively uses the multifloor model as a domain model for planning. By allowing competitive collision to occur and resolve, a robot only needs to learn and adapt to uncertain parts of the environment—nonstationary tasks and peer robots’ paths. Computational experiments show that our approach is both sample-efficient and computationally efficient. The transportation-MRS quickly reaches near-optimal performance levels, which are empirically shown to scale with the number of robots involved.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274908","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 : 2024-08-06DOI: 10.1109/TSMC.2024.3430550
Shuo Li;Jinfeng Zhang;Yu-Ping Tian
This article is concerned with $L_{-}$ fault detection functional observer design for the switched positive systems (SPSs) with unknown external input under mode-dependent minimum dwell time (MDMDT) constraint. First, a necessary and sufficient condition is presented, such that the designed p-order functional observer provides a positive estimation with an $L_{-}$ fault sensitivity performance. Then, a new discretized linear co-positive Lyapunov function method is proposed to derive the asymptotic tracking condition of the $L_{-}$ fault detection functional observer under the MDMDT constraint. Further, the matrix decomposition technology is applied to solve the bilinear inequality problem in the observer gain matrix design, and an effective mode-dependent piece-wise design scheme of the $L_{-}$ fault detection functional observer in the linear programming form is proposed. The designed $L_{-}$ functional observer is not only sensitive to fault but also robust against any form of unknown input. The results are also extended to the cases of mode-dependent constant dwell time (MDCDT) constraint, mode-independent dwell time constraint, and non-SPSs. Finally, the proposed method is illustrated in detail by constructing a fault detection observer for a traffic signal system. Simulation results verify the effectiveness of the theoretical results.
{"title":"Fault Detection for Switched Positive Systems With Application to Traffic Signal Systems","authors":"Shuo Li;Jinfeng Zhang;Yu-Ping Tian","doi":"10.1109/TSMC.2024.3430550","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3430550","url":null,"abstract":"This article is concerned with \u0000<inline-formula> <tex-math>$L_{-}$ </tex-math></inline-formula>\u0000 fault detection functional observer design for the switched positive systems (SPSs) with unknown external input under mode-dependent minimum dwell time (MDMDT) constraint. First, a necessary and sufficient condition is presented, such that the designed p-order functional observer provides a positive estimation with an \u0000<inline-formula> <tex-math>$L_{-}$ </tex-math></inline-formula>\u0000 fault sensitivity performance. Then, a new discretized linear co-positive Lyapunov function method is proposed to derive the asymptotic tracking condition of the \u0000<inline-formula> <tex-math>$L_{-}$ </tex-math></inline-formula>\u0000 fault detection functional observer under the MDMDT constraint. Further, the matrix decomposition technology is applied to solve the bilinear inequality problem in the observer gain matrix design, and an effective mode-dependent piece-wise design scheme of the \u0000<inline-formula> <tex-math>$L_{-}$ </tex-math></inline-formula>\u0000 fault detection functional observer in the linear programming form is proposed. The designed \u0000<inline-formula> <tex-math>$L_{-}$ </tex-math></inline-formula>\u0000 functional observer is not only sensitive to fault but also robust against any form of unknown input. The results are also extended to the cases of mode-dependent constant dwell time (MDCDT) constraint, mode-independent dwell time constraint, and non-SPSs. Finally, the proposed method is illustrated in detail by constructing a fault detection observer for a traffic signal system. Simulation results verify the effectiveness of the theoretical results.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235878","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 : 2024-08-05DOI: 10.1109/TSMC.2024.3431453
Linwei Ye;Zhonggai Zhao;Fei Liu
This article considers the finite-horizon H $_{infty }$ tracking problem for a class of discrete linear time-varying systems. Two reinforcement learning (RL) methods—policy iteration (PI) and Q-learning—are proposed to solve this problem. The latter can obtain the H $_{infty }$ controller without system dynamics. In the field of RL control, most studies focus on infinite-horizon control and time-invariant systems, and few studies have investigated finite-horizon control or time-varying systems. In contrast to infinite-horizon H $_{infty }$ tracking control, finite-horizon H $_{infty }$ tracking control involves a time-varying value function. While this introduces challenges, it empowers the algorithm to effectively handle time-varying problems. Within the finite-horizon framework, the value function is bounded, allowing the removal of the discount factor, thereby enhancing control performance. Additionally, there is no longer a need for an admissible control law for initialization, providing the proposed algorithms with the combined advantages of both PI and value iteration (VI). Two simulation examples are used to verify the effectiveness of the proposed algorithms.
本文研究了一类离散线性时变系统的有限视距 H $_{infty }$ 跟踪问题。提出了两种强化学习（RL）方法--策略迭代（PI）和 Q 学习--来解决这个问题。后者可以在不考虑系统动态的情况下获得 H $_{infty }$ 控制器。在 RL 控制领域，大多数研究集中于无限视距控制和时变系统，很少有研究涉及有限视距控制或时变系统。与无限视距 H $_{infty }$ 跟踪控制不同，有限视距 H $_{infty }$ 跟踪控制涉及时变值函数。虽然这带来了挑战，但却使算法能够有效处理时变问题。在有限视距框架内，价值函数是有界的，因此可以去除贴现因子，从而提高控制性能。此外，初始化时不再需要可接受的控制律，从而使所提出的算法兼具 PI 和值迭代 (VI) 的优点。两个仿真实例用于验证所提算法的有效性。
{"title":"Reinforcement Learning for Finite-Horizon H∞ Tracking Control of Unknown Discrete Linear Time-Varying System","authors":"Linwei Ye;Zhonggai Zhao;Fei Liu","doi":"10.1109/TSMC.2024.3431453","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3431453","url":null,"abstract":"This article considers the finite-horizon H\u0000<inline-formula> <tex-math>$_{infty }$ </tex-math></inline-formula>\u0000 tracking problem for a class of discrete linear time-varying systems. Two reinforcement learning (RL) methods—policy iteration (PI) and Q-learning—are proposed to solve this problem. The latter can obtain the H\u0000<inline-formula> <tex-math>$_{infty }$ </tex-math></inline-formula>\u0000 controller without system dynamics. In the field of RL control, most studies focus on infinite-horizon control and time-invariant systems, and few studies have investigated finite-horizon control or time-varying systems. In contrast to infinite-horizon H\u0000<inline-formula> <tex-math>$_{infty }$ </tex-math></inline-formula>\u0000 tracking control, finite-horizon H\u0000<inline-formula> <tex-math>$_{infty }$ </tex-math></inline-formula>\u0000 tracking control involves a time-varying value function. While this introduces challenges, it empowers the algorithm to effectively handle time-varying problems. Within the finite-horizon framework, the value function is bounded, allowing the removal of the discount factor, thereby enhancing control performance. Additionally, there is no longer a need for an admissible control law for initialization, providing the proposed algorithms with the combined advantages of both PI and value iteration (VI). Two simulation examples are used to verify the effectiveness of the proposed algorithms.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274917","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 : 2024-08-05DOI: 10.1109/TSMC.2024.3428393
Jiejun Hu-Bolz;Katayoun Farrahi;Manuel Cebrian
Information-enabled technology, such as digital contact tracing (DCT), is designed to assist decision-making. However, during the pandemic, DCT also contributes additional factors to consider for individuals and policymakers. In this article, we conceptualise the interactions between individuals and policymakers and propose a leader-followers mean-field Game model to analyse their decision-making processes by integrating key factors, such as individuals’ health state, self-control effort, privacy, and social activities, under the influence of interventions and subsidies from policymakers. The simulation demonstrates that moderate subsidies are sufficient to induce early control efforts and higher collective efforts even with privacy concerns and the negative influence of engaging activities; Equilibrium and system stability can be reached when healthy or the summation of healthy and mediocre healthy populations dominates; The model highlights the crucial elements for future data analysis and collection, such as DCT is vital to indicate population health states.
{"title":"Beyond the Surface of Digital Contact Tracing: Delving into the Interconnected World of Technology, Individuals, and Society","authors":"Jiejun Hu-Bolz;Katayoun Farrahi;Manuel Cebrian","doi":"10.1109/TSMC.2024.3428393","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3428393","url":null,"abstract":"Information-enabled technology, such as digital contact tracing (DCT), is designed to assist decision-making. However, during the pandemic, DCT also contributes additional factors to consider for individuals and policymakers. In this article, we conceptualise the interactions between individuals and policymakers and propose a leader-followers mean-field Game model to analyse their decision-making processes by integrating key factors, such as individuals’ health state, self-control effort, privacy, and social activities, under the influence of interventions and subsidies from policymakers. The simulation demonstrates that moderate subsidies are sufficient to induce early control efforts and higher collective efforts even with privacy concerns and the negative influence of engaging activities; Equilibrium and system stability can be reached when healthy or the summation of healthy and mediocre healthy populations dominates; The model highlights the crucial elements for future data analysis and collection, such as DCT is vital to indicate population health states.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275011","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}
Deep networks can usually depend on extracting more structural information to improve denoising results. However, they may ignore correlation between pixels from an image to pursue better-denoising performance. Window Transformer can use long- and short-distance modeling to interact pixels to address mentioned problem. To make a tradeoff between distance modeling and denoising time, we propose a heterogeneous window Transformer (HWformer) for image denoising. HWformer first designs heterogeneous global windows to capture global context information for improving denoising effects. To build a bridge between long and short-distance modeling, global windows are horizontally and vertically shifted to facilitate diversified information without increasing denoising time. To prevent the information loss phenomenon of independent patches, sparse idea is guided a feed-forward network to extract local information of neighboring patches. The proposed HWformer only takes 30% of popular restoration Transformer in terms of denoising time. Its codes can be obtained at https://github.com/hellloxiaotian/HWformer .
{"title":"Heterogeneous Window Transformer for Image Denoising","authors":"Chunwei Tian;Menghua Zheng;Chia-Wen Lin;Zhiwu Li;David Zhang","doi":"10.1109/TSMC.2024.3429345","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3429345","url":null,"abstract":"Deep networks can usually depend on extracting more structural information to improve denoising results. However, they may ignore correlation between pixels from an image to pursue better-denoising performance. Window Transformer can use long- and short-distance modeling to interact pixels to address mentioned problem. To make a tradeoff between distance modeling and denoising time, we propose a heterogeneous window Transformer (HWformer) for image denoising. HWformer first designs heterogeneous global windows to capture global context information for improving denoising effects. To build a bridge between long and short-distance modeling, global windows are horizontally and vertically shifted to facilitate diversified information without increasing denoising time. To prevent the information loss phenomenon of independent patches, sparse idea is guided a feed-forward network to extract local information of neighboring patches. The proposed HWformer only takes 30% of popular restoration Transformer in terms of denoising time. Its codes can be obtained at \u0000<uri>https://github.com/hellloxiaotian/HWformer</uri>\u0000.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443058","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 : 2024-08-02DOI: 10.1109/TSMC.2024.3428314
Zicheng Zhu;Han Zhao;Yuanjie Xian;Ye-Hwa Chen;Hao Sun;Jun Ma
A Stackelberg game-based design for an adaptive robust control for the fuzzy uncertain underactuated mechanical systems (UMSs) is proposed. The emphasis is on fuzzy-based uncertainty and inequality constraint. The uncertainty is time varying and bounded within a prescribed fuzzy set. For the inequality constraint, we creatively have it merge into constraint-following performance by a diffeomorphism technique. An adaptive robust control strategy is then proposed. Deterministic performance is guaranteed provided the control design parameters are within feasible regions. To further enhance the performance, we introduce a two-player Stackelberg game setting. The optimal choice of design parameters can be solved. The feasibility of this design is demonstrated on an autonomous wheeled mobile robot (AWMR), which is confined in a bounded space.
{"title":"Stackelberg Game-Based Control Design for Fuzzy Underactuated Mechanical Systems With Inequality Constraints","authors":"Zicheng Zhu;Han Zhao;Yuanjie Xian;Ye-Hwa Chen;Hao Sun;Jun Ma","doi":"10.1109/TSMC.2024.3428314","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3428314","url":null,"abstract":"A Stackelberg game-based design for an adaptive robust control for the fuzzy uncertain underactuated mechanical systems (UMSs) is proposed. The emphasis is on fuzzy-based uncertainty and inequality constraint. The uncertainty is time varying and bounded within a prescribed fuzzy set. For the inequality constraint, we creatively have it merge into constraint-following performance by a diffeomorphism technique. An adaptive robust control strategy is then proposed. Deterministic performance is guaranteed provided the control design parameters are within feasible regions. To further enhance the performance, we introduce a two-player Stackelberg game setting. The optimal choice of design parameters can be solved. The feasibility of this design is demonstrated on an autonomous wheeled mobile robot (AWMR), which is confined in a bounded space.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274971","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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