Pub Date : 2024-09-26DOI: 10.1109/TSMC.2024.3460369
Kunting Yu;Yongming Li;Maolong Lv;Shaocheng Tong
This article introduces an adaptive fuzzy control methodology employing dynamic event-triggered communication for underactuated multiple unmanned surface vehicles (USVs) with modeling uncertainties. The key innovations of the proposed formation control strategy can be summarized as follows: 1) each USV is equipped with a dynamic event-triggered mechanism, ensuring that the controller and neighboring USVs receive position and yaw angle information only when this mechanism is triggered, enhancing communication efficiency; 2) distributed filters are implemented to continuous the event-triggered information; and 3) by employing the fuzzy logical systems (FLSs) to identify the unknown modeling uncertainties, local observers are designed to estimate unavailable velocity and yaw rate. Based on the dynamic event-triggered mechanism, distributed filters and local observers, a nondifferentiable-free backstepping procedure is proposed. The closed-loop stability is proven through Lyapunov stability theory, and Zeno behavior of the dynamic event-triggered mechanism is demonstrated through reductio. Simulation results are presented to validate the effectiveness of the proposed control strategy.
{"title":"Adaptive Fuzzy Formation Control for Underactuated Multi-USVs With Dynamic Event-Triggered Communication","authors":"Kunting Yu;Yongming Li;Maolong Lv;Shaocheng Tong","doi":"10.1109/TSMC.2024.3460369","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3460369","url":null,"abstract":"This article introduces an adaptive fuzzy control methodology employing dynamic event-triggered communication for underactuated multiple unmanned surface vehicles (USVs) with modeling uncertainties. The key innovations of the proposed formation control strategy can be summarized as follows: 1) each USV is equipped with a dynamic event-triggered mechanism, ensuring that the controller and neighboring USVs receive position and yaw angle information only when this mechanism is triggered, enhancing communication efficiency; 2) distributed filters are implemented to continuous the event-triggered information; and 3) by employing the fuzzy logical systems (FLSs) to identify the unknown modeling uncertainties, local observers are designed to estimate unavailable velocity and yaw rate. Based on the dynamic event-triggered mechanism, distributed filters and local observers, a nondifferentiable-free backstepping procedure is proposed. The closed-loop stability is proven through Lyapunov stability theory, and Zeno behavior of the dynamic event-triggered mechanism is demonstrated through reductio. Simulation results are presented to validate the effectiveness of the proposed control strategy.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7783-7793"},"PeriodicalIF":8.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672172","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 : 2024-09-26DOI: 10.1109/TSMC.2024.3459633
Yifu Ren;Jinhai Liu;He Zhao;Huaguang Zhang
The problem of limited samples with missing information is an open challenge in data-driven fault diagnosis. Existing work has limited application in this field, since the reconstructed missing samples participating in sample generation may hurt the quality of the generated samples. To address this issue, the joint modeling of sample reconstruction and sample generation is proposed. First, the differentiated evaluation and reconstruction strategies are designed, which make reconstructed samples more reasonable and realistic, so that they can be employed to participate in sample generation. Second, the adaptive fusion mechanism is presented to introduce the knowledge of actual fault samples into the laboratory simulation samples, by which the quality and diversity of generated samples are guaranteed. By doing so, limited samples with missing information are enhanced to enable reliable fault diagnosis modeling. The proposed method is applied to the actual industrial process and benchmark simulated process. The experimental results highlight the superiority of the proposed method.
{"title":"An Industrial Fault Sample Reconstruction and Generation Method Under Limited Samples With Missing Information","authors":"Yifu Ren;Jinhai Liu;He Zhao;Huaguang Zhang","doi":"10.1109/TSMC.2024.3459633","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3459633","url":null,"abstract":"The problem of limited samples with missing information is an open challenge in data-driven fault diagnosis. Existing work has limited application in this field, since the reconstructed missing samples participating in sample generation may hurt the quality of the generated samples. To address this issue, the joint modeling of sample reconstruction and sample generation is proposed. First, the differentiated evaluation and reconstruction strategies are designed, which make reconstructed samples more reasonable and realistic, so that they can be employed to participate in sample generation. Second, the adaptive fusion mechanism is presented to introduce the knowledge of actual fault samples into the laboratory simulation samples, by which the quality and diversity of generated samples are guaranteed. By doing so, limited samples with missing information are enhanced to enable reliable fault diagnosis modeling. The proposed method is applied to the actual industrial process and benchmark simulated process. The experimental results highlight the superiority of the proposed method.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7821-7833"},"PeriodicalIF":8.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679369","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}
Electroencephalogram (EEG) brain network embodies the brain’s coordination and interaction mechanism, and the transformations of emotional states are usually accompanied with changes in brain network spatial topologies. To effectively characterize emotions, in this work, we propose a cognition-inspired graph embedding model in the L1-norm space (L1-CGE) to learn an optimal low-dimensional embedded manifold for emotional brain networks. In the L1-CGE, the original brain networks are first encoded in the affinity space with the proposed cognition-inspired metric to construct the latent geometry manifold structure of emotional brain networks, and then the graph learning objective function is defined in the L1-norm space to obtain the optimal low-dimensional representations of brain networks. Essentially, the modularized community structures of emotional brain networks can be effectively emphasized by the L1-CGE to realize an effective depiction for emotions. Compared with existing methods, the L1-CGE model has achieved state-of-the-art performance on three public emotional EEG datasets in off-line conditions. Besides, the robust real-time experimental results have been achieved with the on-line emotion decoding system designed with L1-CGE. Both off- and on-line experimental results consistently demonstrate that the proposed L1-CGE is promising to provide a potential solution for the real-time affective brain-computer interface (aBCI) system.
{"title":"Brain Network Manifold Learned by Cognition-Inspired Graph Embedding Model for Emotion Recognition","authors":"Cunbo Li;Peiyang Li;Zhaojin Chen;Lei Yang;Fali Li;Feng Wan;Zehong Cao;Dezhong Yao;Bao-Liang Lu;Peng Xu","doi":"10.1109/TSMC.2024.3458949","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3458949","url":null,"abstract":"Electroencephalogram (EEG) brain network embodies the brain’s coordination and interaction mechanism, and the transformations of emotional states are usually accompanied with changes in brain network spatial topologies. To effectively characterize emotions, in this work, we propose a cognition-inspired graph embedding model in the L1-norm space (L1-CGE) to learn an optimal low-dimensional embedded manifold for emotional brain networks. In the L1-CGE, the original brain networks are first encoded in the affinity space with the proposed cognition-inspired metric to construct the latent geometry manifold structure of emotional brain networks, and then the graph learning objective function is defined in the L1-norm space to obtain the optimal low-dimensional representations of brain networks. Essentially, the modularized community structures of emotional brain networks can be effectively emphasized by the L1-CGE to realize an effective depiction for emotions. Compared with existing methods, the L1-CGE model has achieved state-of-the-art performance on three public emotional EEG datasets in off-line conditions. Besides, the robust real-time experimental results have been achieved with the on-line emotion decoding system designed with L1-CGE. Both off- and on-line experimental results consistently demonstrate that the proposed L1-CGE is promising to provide a potential solution for the real-time affective brain-computer interface (aBCI) system.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7794-7808"},"PeriodicalIF":8.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672070","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}
This article investigates the distributed constrained optimization problem with event-triggered communication over time-varying weight-unbalanced directed graphs. A more generalized network model is considered where the communication topology may be variable and unbalanced over time, the information flows across agents are subject to time-varying communication delays, and agents are not required to know their out-degree information accurately. To address the above challenges, we propose a novel discrete-time distributed event-triggered delay subgradient algorithm. To facilitate convergence analysis, a consensus-only “virtual” agent technique is employed, dynamically adjusting its state (active or asleep) to ensure a delay-free information flow among agents. Additionally, an augmentation approach is proposed to ensure that the augmented time-varying weight matrix is row-stochastic. It is shown that the agents’ local decision variables converge to the same optimal solution, in the case of reasonable communication delays and event-triggering thresholds. Numerical examples show the efficiency of the proposed algorithm.
{"title":"An Event-Based Delayed Projection Row-Stochastic Method for Distributed Constrained Optimization Over Time-Varying Graphs","authors":"Mingqi Xing;Dazhong Ma;Huaguang Zhang;Xiangpeng Xie","doi":"10.1109/TSMC.2024.3458972","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3458972","url":null,"abstract":"This article investigates the distributed constrained optimization problem with event-triggered communication over time-varying weight-unbalanced directed graphs. A more generalized network model is considered where the communication topology may be variable and unbalanced over time, the information flows across agents are subject to time-varying communication delays, and agents are not required to know their out-degree information accurately. To address the above challenges, we propose a novel discrete-time distributed event-triggered delay subgradient algorithm. To facilitate convergence analysis, a consensus-only “virtual” agent technique is employed, dynamically adjusting its state (active or asleep) to ensure a delay-free information flow among agents. Additionally, an augmentation approach is proposed to ensure that the augmented time-varying weight matrix is row-stochastic. It is shown that the agents’ local decision variables converge to the same optimal solution, in the case of reasonable communication delays and event-triggering thresholds. Numerical examples show the efficiency of the proposed algorithm.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7508-7520"},"PeriodicalIF":8.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672117","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 : 2024-09-26DOI: 10.1109/TSMC.2024.3460386
Shanglin Li;Yangzhou Chen;Peter Xiaoping Liu
This article investigates the problem of finite-frequency fault estimation (FE) and adaptive event-triggered fault-tolerant consensus for linear parameter-varying multiagent systems. A polytopic parameter-varying framework is introduced to represent the dynamics of each agent with internal model perturbation and parameter uncertainties. In order to reduce the conservatism brought by full-frequency domain approaches, the finite-frequency technique is employed to design a FE observer that can estimate the magnitude of faults. To eliminate/reduce the impact of faults on system performance, an adaptive event-triggered fault-tolerant consensus controller is then developed, which adjusts the consensus protocol based on the FE information. With the developed distributed fault-tolerant protocol and adaptive event-triggered control scheme, the agents can reach consensus in the presence of system faults and the transmission of unnecessary information in the control channels is avoided. The proposed triggering scheme offers certain advantages over existing results in balancing desired consensus performance and improving network utilization. By constructing a parameter-dependent Lyapunov function, a sufficient condition for designing the consensus controller gain and the adjustment matrix can be derived in the form of linear matrix inequality. Finally, two simulation examples are included to illustrate the effectiveness of the obtained theoretical results.
本文研究了线性参数变化多代理系统的有限频率故障估计（FE）和自适应事件触发容错共识问题。本文引入了一个多拓扑参数变化框架，以表示具有内部模型扰动和参数不确定性的每个代理的动态。为了减少全频域方法带来的保守性，采用了有限频率技术来设计能估计故障大小的 FE 观察器。为了消除/减少故障对系统性能的影响，还开发了一种自适应事件触发容错共识控制器，它能根据 FE 信息调整共识协议。利用所开发的分布式容错协议和自适应事件触发控制方案，代理可以在系统出现故障时达成共识，并避免在控制信道中传输不必要的信息。与现有成果相比，所提出的触发方案在平衡所需的共识性能和提高网络利用率方面具有一定优势。通过构建与参数相关的 Lyapunov 函数，可以以线性矩阵不等式的形式推导出设计共识控制器增益和调整矩阵的充分条件。最后，通过两个仿真实例说明了所获理论结果的有效性。
{"title":"Finite-Frequency Fault Estimation and Adaptive Event-Triggered Fault-Tolerant Consensus for LPV Multiagent Systems","authors":"Shanglin Li;Yangzhou Chen;Peter Xiaoping Liu","doi":"10.1109/TSMC.2024.3460386","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3460386","url":null,"abstract":"This article investigates the problem of finite-frequency fault estimation (FE) and adaptive event-triggered fault-tolerant consensus for linear parameter-varying multiagent systems. A polytopic parameter-varying framework is introduced to represent the dynamics of each agent with internal model perturbation and parameter uncertainties. In order to reduce the conservatism brought by full-frequency domain approaches, the finite-frequency technique is employed to design a FE observer that can estimate the magnitude of faults. To eliminate/reduce the impact of faults on system performance, an adaptive event-triggered fault-tolerant consensus controller is then developed, which adjusts the consensus protocol based on the FE information. With the developed distributed fault-tolerant protocol and adaptive event-triggered control scheme, the agents can reach consensus in the presence of system faults and the transmission of unnecessary information in the control channels is avoided. The proposed triggering scheme offers certain advantages over existing results in balancing desired consensus performance and improving network utilization. By constructing a parameter-dependent Lyapunov function, a sufficient condition for designing the consensus controller gain and the adjustment matrix can be derived in the form of linear matrix inequality. Finally, two simulation examples are included to illustrate the effectiveness of the obtained theoretical results.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7871-7883"},"PeriodicalIF":8.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679365","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 : 2024-09-25DOI: 10.1109/TSMC.2024.3462728
Panagiotis S. Trakas;Charalampos P. Bechlioulis
In this work, we propose an approximation-free adaptive performance control scheme for unknown high-relative degree, multi-input-multioutput (MIMO) nonlinear systems with saturation on the control input signal. We introduce a novel reconciling adaptive modification of the predefined performance specifications based on the input constraints of the controlled plant, providing the best-feasible output performance. The automatic gain tuning in combination with the simplicity of the proposed controller enhance its robustness and enable its easy deployment in practical scenarios. Notably, the introduced control methodology ensures the necessary compromise between input-output constraints on a semi-global sense for ISS systems. However, the stability attributes for general nonlinear systems are inevitably limited to compact domains due to the inherent conflict between performance demand and actuation capability. In this context, we provide a sufficient closed-loop stability criterion through Lyapunov analysis. Finally, illustrative simulation studies and experimental results clarify and verify the efficacy of the proposed controller.
在这项研究中，我们针对控制输入信号饱和的未知高相对度、多输入多输出（MIMO）非线性系统提出了一种无近似自适应性能控制方案。我们根据受控工厂的输入约束条件，引入了一种新颖的调和自适应修改预定义性能指标的方法，以提供最佳可行的输出性能。自动增益调整与拟议控制器的简易性相结合，增强了控制器的鲁棒性，使其能够轻松应用于实际场景。值得注意的是，引入的控制方法确保了 ISS 系统在半全局意义上的输入输出约束之间的必要折衷。然而，由于性能需求与执行能力之间的内在冲突，一般非线性系统的稳定性属性不可避免地局限于紧凑域。在这种情况下，我们通过 Lyapunov 分析提供了充分的闭环稳定性标准。最后，说明性仿真研究和实验结果澄清并验证了所提控制器的功效。
{"title":"Adaptive Performance Control for Input Constrained MIMO Nonlinear Systems","authors":"Panagiotis S. Trakas;Charalampos P. Bechlioulis","doi":"10.1109/TSMC.2024.3462728","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3462728","url":null,"abstract":"In this work, we propose an approximation-free adaptive performance control scheme for unknown high-relative degree, multi-input-multioutput (MIMO) nonlinear systems with saturation on the control input signal. We introduce a novel reconciling adaptive modification of the predefined performance specifications based on the input constraints of the controlled plant, providing the best-feasible output performance. The automatic gain tuning in combination with the simplicity of the proposed controller enhance its robustness and enable its easy deployment in practical scenarios. Notably, the introduced control methodology ensures the necessary compromise between input-output constraints on a semi-global sense for ISS systems. However, the stability attributes for general nonlinear systems are inevitably limited to compact domains due to the inherent conflict between performance demand and actuation capability. In this context, we provide a sufficient closed-loop stability criterion through Lyapunov analysis. Finally, illustrative simulation studies and experimental results clarify and verify the efficacy of the proposed controller.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7733-7745"},"PeriodicalIF":8.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693631","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1109/TSMC.2024.3461668
Ting Huang;Qiang Zhang;Xiaonong Lu;Shuangyao Zhao;Shanlin Yang
Convolutional neural networks (CNNs) have been widely used in fault diagnosis due to their superiority in feature extraction. Traditional CNNs are a type of closed-box techniques with little interpretability, and their effectiveness is greatly affected when fault mechanisms and modes are extremely complex. To cope with such issue, this article presents a way to integrate outlier-type prior knowledge into CNNs based on an attention mechanism for fault diagnosis. First, outliers of the image-like data obtained by a sliding window processing from the raw data are formally defined as prior knowledge. Then, the defined outlier-type prior knowledge is integrated into any layer of CNNs by a parameter-free attention mechanism. Compared with existing similar methods, the proposal realizes a novel and flexible definition of prior knowledge and achieves deep fusion of prior knowledge and CNNs with low computational cost. The performance of the proposal was evaluated on the Tennessee Eastman process dataset and the real wind turbine blade icing dataset, which indicates that the proposal could not only realize accurate results but also had good model interpretability in terms of achieving high accuracy. The acquisition of outlier-type prior knowledge was discussed and the results demonstrate the effectiveness of the proposed prior knowledge integration method.
{"title":"Integrating Outlier-Type Prior Knowledge Into Convolutional Neural Networks Based on an Attention Mechanism for Fault Diagnosis","authors":"Ting Huang;Qiang Zhang;Xiaonong Lu;Shuangyao Zhao;Shanlin Yang","doi":"10.1109/TSMC.2024.3461668","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3461668","url":null,"abstract":"Convolutional neural networks (CNNs) have been widely used in fault diagnosis due to their superiority in feature extraction. Traditional CNNs are a type of closed-box techniques with little interpretability, and their effectiveness is greatly affected when fault mechanisms and modes are extremely complex. To cope with such issue, this article presents a way to integrate outlier-type prior knowledge into CNNs based on an attention mechanism for fault diagnosis. First, outliers of the image-like data obtained by a sliding window processing from the raw data are formally defined as prior knowledge. Then, the defined outlier-type prior knowledge is integrated into any layer of CNNs by a parameter-free attention mechanism. Compared with existing similar methods, the proposal realizes a novel and flexible definition of prior knowledge and achieves deep fusion of prior knowledge and CNNs with low computational cost. The performance of the proposal was evaluated on the Tennessee Eastman process dataset and the real wind turbine blade icing dataset, which indicates that the proposal could not only realize accurate results but also had good model interpretability in terms of achieving high accuracy. The acquisition of outlier-type prior knowledge was discussed and the results demonstrate the effectiveness of the proposed prior knowledge integration method.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7834-7847"},"PeriodicalIF":8.6,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679366","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 : 2024-09-24DOI: 10.1109/TSMC.2024.3462790
Adolfo Perrusquía;Zhuangkun Wei;Weisi Guo
Proliferation of autonomous systems have increased the threat space and the economic risk in several national infrastructures, e.g., at airports. Therefore, reliable detection of their intention is paramount to ensure smooth operation of national services and societal safety. This article reports a data-driven trajectory intent prediction algorithm which is based on a linear model structure of the autonomous system dynamics obtained from a dynamic mode decomposition algorithm. The model computation is enhanced by two sources of physics informed knowledge associated to the energy functional. Two different prediction algorithms that consider fixed or time-varying references are designed in terms of the availability of control input measurements. Rigorous theoretical results are provided to support the approach using matrix decomposition and optimization techniques. Simulation and experimental studies are carried out to verify the effectiveness of the proposal.
{"title":"Trajectory Intent Prediction of Autonomous Systems Using Dynamic Mode Decomposition","authors":"Adolfo Perrusquía;Zhuangkun Wei;Weisi Guo","doi":"10.1109/TSMC.2024.3462790","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3462790","url":null,"abstract":"Proliferation of autonomous systems have increased the threat space and the economic risk in several national infrastructures, e.g., at airports. Therefore, reliable detection of their intention is paramount to ensure smooth operation of national services and societal safety. This article reports a data-driven trajectory intent prediction algorithm which is based on a linear model structure of the autonomous system dynamics obtained from a dynamic mode decomposition algorithm. The model computation is enhanced by two sources of physics informed knowledge associated to the energy functional. Two different prediction algorithms that consider fixed or time-varying references are designed in terms of the availability of control input measurements. Rigorous theoretical results are provided to support the approach using matrix decomposition and optimization techniques. Simulation and experimental studies are carried out to verify the effectiveness of the proposal.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7897-7908"},"PeriodicalIF":8.6,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679397","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 : 2024-09-23DOI: 10.1109/TSMC.2024.3460191
Hoai Vu Anh Truong;Wan Kyun Chung
Unstructured dynamics, un-modeled parameters, and uncertainties in electro-hydraulic servo-valve-controlled actuators (EHSAs) bring difficulty in designing controllers for output tracking performance with stability and robustness satisfactions. Therefore, this article proposes an output feedback-based control for position regulation subject to fully unknown system behavior and uncertainties. With this idea, a coordinately transformed canonical system is utilized where all mismatched/matched uncertainties are lumped into one term with unknown dynamics. Then, a radial basis function neural network (RBFNN) with a norm of weighting vector estimation combined with a time-delayed estimation (TDE) is employed to effectively compensate for the system behavior. Accordingly, a second-order sliding-mode-based output feedback control is conducted to avoid following the step-by-step backstepping control (BSC) design. Interestingly, the proposed methodology requires only the measured output for the control law implementation with only one estimated variable for the system dynamics compensation due to using the hybrid RBF-based TDE (RBF-TDE). Moreover, to lower this approximated error, a modified sliding-mode-based nonlinear disturbance observer (DOB) is extensively involved. The closed-loop system stability is mathematically proven through the Lyapunov theorem with simulation and experiment on EHSA protocols to realize the effectiveness of the proposed algorithm.
{"title":"Sliding-Mode-Based Output Feedback Neural Network Control for Electro-Hydraulic Actuator Subject to Unknown Dynamics and Uncertainties","authors":"Hoai Vu Anh Truong;Wan Kyun Chung","doi":"10.1109/TSMC.2024.3460191","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3460191","url":null,"abstract":"Unstructured dynamics, un-modeled parameters, and uncertainties in electro-hydraulic servo-valve-controlled actuators (EHSAs) bring difficulty in designing controllers for output tracking performance with stability and robustness satisfactions. Therefore, this article proposes an output feedback-based control for position regulation subject to fully unknown system behavior and uncertainties. With this idea, a coordinately transformed canonical system is utilized where all mismatched/matched uncertainties are lumped into one term with unknown dynamics. Then, a radial basis function neural network (RBFNN) with a norm of weighting vector estimation combined with a time-delayed estimation (TDE) is employed to effectively compensate for the system behavior. Accordingly, a second-order sliding-mode-based output feedback control is conducted to avoid following the step-by-step backstepping control (BSC) design. Interestingly, the proposed methodology requires only the measured output for the control law implementation with only one estimated variable for the system dynamics compensation due to using the hybrid RBF-based TDE (RBF-TDE). Moreover, to lower this approximated error, a modified sliding-mode-based nonlinear disturbance observer (DOB) is extensively involved. The closed-loop system stability is mathematically proven through the Lyapunov theorem with simulation and experiment on EHSA protocols to realize the effectiveness of the proposed algorithm.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7884-7896"},"PeriodicalIF":8.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679327","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 : 2024-09-23DOI: 10.1109/TSMC.2024.3456810
Ahmad Mohammad Saber;Amr Youssef;Davor Svetinovic;Hatem Zeineldin;Ehab F. El-Saadany
Line current differential relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-masking attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this article, we propose a two-module framework to detect FMAs. The first module is a mismatch index (MI) developed from the protected transmission line’s equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR’s local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT’s real-time simulator confirm the proposed solution’s real-time performance capability.
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