IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第10页

Reinforcement Learning H∞ Optimal Formation Control for Perturbed Multiagent Systems With Nonlinear Faults

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-12-24 DOI: 10.1109/TSMC.2024.3516048

Yuxia Wu;Hongjing Liang;Shuxing Xuan;Choon Ki Ahn

{"title":"Reinforcement Learning H∞ Optimal Formation Control for Perturbed Multiagent Systems With Nonlinear Faults","authors":"Yuxia Wu;Hongjing Liang;Shuxing Xuan;Choon Ki Ahn","doi":"10.1109/TSMC.2024.3516048","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3516048","url":null,"abstract":"This article presents an optimal formation control strategy for multiagent systems based on a reinforcement learning (RL) technique, considering prescribed performance and unknown nonlinear faults. To optimize the control performance, an RL strategy is introduced based on the identifier-critic–actor-disturbance structure and backstepping frame. The identifier, critic, actor, and disturbance neural networks (NNs) are employed to estimate unknown dynamics, assess system performance, carry out control actions, and derive the worst disturbance strategy, respectively. With the scheme, the persistent excitation requirements are removed by adopting simplified NNs updating laws, which are derived using the gradient descent method toward designed positive functions instead of the square of Bellman residual. For achieving the desired error precision within the prescribed time, a constraining function and an error transformation scheme are employed. In addition, to enhance the system’s robustness, a fault observer is utilized to compensate for the impact of the unknown nonlinear faults. The stability of the closed-loop system is assured, while the prescribed performance is realized. Finally, simulation examples validate the effectiveness of the proposed optimal control strategy.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"1935-1947"},"PeriodicalIF":8.6,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438327","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}

引用次数: 0

Differential Neural Network Identifiers for Periodic Systems, a Floquet’s Theory Approach

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-12-24 DOI: 10.1109/TSMC.2024.3511913

Grigory Bugriy;Arthur Mukhamedov;Viktor Chertopolokhov;Stepan Lemak;Isaac Chairez

{"title":"Differential Neural Network Identifiers for Periodic Systems, a Floquet’s Theory Approach","authors":"Grigory Bugriy;Arthur Mukhamedov;Viktor Chertopolokhov;Stepan Lemak;Isaac Chairez","doi":"10.1109/TSMC.2024.3511913","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3511913","url":null,"abstract":"The precise modeling of dynamic systems with periodic trajectories is required to describe diverse systems in mechanical, electrical, and many other disciplines. Nevertheless, the modeling task based on traditional methodologies could be complicated, considering the specific nature of periodic motions in actual systems. Differential neural networks (DNNs) are modeling tools for dynamic systems that can be useful for developing precise representations of periodic systems. This study presents the design of a novel family of DNN identifiers that could reproduce the trajectories of periodic systems with an uncertain mathematical model. The suggested DNN identifiers may produce an approximate model with periodic properties similar to the system under analysis exhibiting an one-period convergence of DNN weights. The fundamentals of the Floquet’s theory drive the design of the learning laws to ensure the reproduction of the periodic properties in the DNN. The design of a controlled Lyapunov function allows the learning laws to be derived for the DNN weights whose evolution depends on the positive definite solution of a periodic differential Lyapunov equation. Several numerical evaluations on periodic systems confirmed the modeling performance of the proposed identifier when the approximation performance is compared with traditional DNN identifiers using sigmoidal functions.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"1912-1923"},"PeriodicalIF":8.6,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438319","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}

引用次数: 0

Approximate Dynamic Programming for Constrained Piecewise Affine Systems With Stability and Safety Guarantees

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-12-24 DOI: 10.1109/TSMC.2024.3515645

Kanghui He;Shengling Shi;Ton van den Boom;Bart De Schutter

{"title":"Approximate Dynamic Programming for Constrained Piecewise Affine Systems With Stability and Safety Guarantees","authors":"Kanghui He;Shengling Shi;Ton van den Boom;Bart De Schutter","doi":"10.1109/TSMC.2024.3515645","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3515645","url":null,"abstract":"Infinite-horizon optimal control of constrained piecewise affine (PWA) systems has been approximately addressed by hybrid model predictive control (MPC), which, however, has computational limitations, both in offline design and online implementation. In this article, we consider an alternative approach based on approximate dynamic programming (ADP), an important class of methods in reinforcement learning. We accommodate nonconvex union-of-polyhedra state constraints and linear input constraints into ADP by designing PWA penalty functions. PWA function approximation is used, which allows for a mixed-integer encoding to implement ADP. The main advantage of the proposed ADP method is its online computational efficiency. Particularly, we propose two control policies, which lead to solving a smaller-scale mixed-integer linear program than conventional hybrid MPC, or a single convex quadratic program, depending on whether the policy is implicitly determined online or explicitly computed offline. We characterize the stability and safety properties of the closed-loop systems, as well as the suboptimality of the proposed policies, by quantifying the approximation errors of value functions and policies. We also develop an offline mixed-integer-linear-programming-based method to certify the reliability of the proposed method. Simulation results on an inverted pendulum with elastic walls and on an adaptive cruise control problem validate the control performance in terms of constraint satisfaction and CPU time.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"1722-1734"},"PeriodicalIF":8.6,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10814712","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438520","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}

引用次数: 0

Learning-Based Robust Adaptive Rapid Exponential Stabilization for a Class of Nonlinear CPSs Under DoS Attacks

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-12-24 DOI: 10.1109/TSMC.2024.3516134

Lang Zou;Xiangbin Liu;Hongye Su;Xiaoyu Zhang

{"title":"Learning-Based Robust Adaptive Rapid Exponential Stabilization for a Class of Nonlinear CPSs Under DoS Attacks","authors":"Lang Zou;Xiangbin Liu;Hongye Su;Xiaoyu Zhang","doi":"10.1109/TSMC.2024.3516134","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3516134","url":null,"abstract":"For a class of uncertain nonlinear sampled-data cyber-physical systems (CPSs) under denial-of-service (DoS) attacks with average frequency and duration constraints, a learning-based rapidly exponentially stabilizing robust adaptive controller (RESRAC) is proposed to improve the control performance in this article. In order to enhance the system robustness against DoS attacks, a rapid exponential stabilization (RES) method is leveraged in controller design to accelerate the convergence rate of the system state. Meanwhile, to take into account the performance boundary of the system state, the learning algorithms are designed to mitigate the peaking phenomenon due to the high-gain feedback in the RES method. In the adaptation law design, <inline-formula> <tex-math>$sigma $ </tex-math></inline-formula>-modification combined with G+D estimator is adopted to robustly shape the dynamics of closed-loop system and enhance the steady-state performance. Through Lyapunov stability analysis, it is proved that the CPSs under the proposed control scheme can accommodate the effect of DoS attacks of nearly arbitrary intensity, i.e., the communication is not completely blocked. Finally, a numerical simulation is carried out to illustrate the effectiveness and superiority of the proposed control scheme.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"1898-1911"},"PeriodicalIF":8.6,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438394","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}

引用次数: 0

How to Choose the Most Appropriate Centrality Measure? A Decision-Tree Approach

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-12-23 DOI: 10.1109/TSMC.2024.3510633

Pavel Chebotarev;Dmitry A. Gubanov

{"title":"How to Choose the Most Appropriate Centrality Measure? A Decision-Tree Approach","authors":"Pavel Chebotarev;Dmitry A. Gubanov","doi":"10.1109/TSMC.2024.3510633","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3510633","url":null,"abstract":"Centrality metrics play a crucial role in network analysis, while the choice of specific measures significantly influences the accuracy of conclusions as each measure represents a unique concept of node importance. Among over 400 proposed indices, selecting the most suitable ones for specific applications remains a challenge. Existing approaches—model-based, data-driven, and axiomatic—have limitations, requiring association with models, training datasets, or restrictive axioms for each specific application. To address this, we introduce the culling method, which relies on the expert concept of centrality behavior on simple graphs. The culling method involves forming a set of candidate measures, generating a list of as small graphs as possible needed to distinguish the measures from each other, constructing a decision-tree survey, and identifying the measure consistent with the expert’s concept. We apply this approach to a diverse set of 40 centralities, including novel kernel-based indices, and combine it with the axiomatic approach. Remarkably, only 13 small 1-trees are sufficient to separate all 40 measures, even for pairs of closely related ones. By adopting simple ordinal axioms like Self-consistency or Bridge axiom, the set of measures can be drastically reduced making the culling survey short. Applying the culling method provides insightful findings on some centrality indices, such as PageRank, Bridging, and dissimilarity-based Eigencentrality measures, among others. The proposed approach offers a cost-effective solution in terms of labor and time, complementing existing methods for measure selection, and providing deeper insights into the underlying mechanisms of centrality measures.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"1694-1706"},"PeriodicalIF":8.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438515","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}

引用次数: 0

Artificial Neural Dynamics for Portfolio Allocation: An Optimization Perspective

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-12-23 DOI: 10.1109/TSMC.2024.3514919

Xinwei Cao;Yiguo Yang;Shuai Li;Predrag S. Stanimirović;Vasilios N. Katsikis

{"title":"Artificial Neural Dynamics for Portfolio Allocation: An Optimization Perspective","authors":"Xinwei Cao;Yiguo Yang;Shuai Li;Predrag S. Stanimirović;Vasilios N. Katsikis","doi":"10.1109/TSMC.2024.3514919","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3514919","url":null,"abstract":"Real-time high-frequency trading poses a significant challenge to the classical portfolio allocation problem, demanding rapid computational efficiency for constructing Markowitz model-based portfolios. Building on the principles of arbitrage pricing theory (APT), this study introduces a dynamic neural network model aimed at minimizing investment risk, optimizing portfolio allocation within predefined constraints, and maximizing returns. First, a convex optimization objective function incorporating risk constraints is formulated based on APT principles. This is followed by the introduction of a novel dynamic neural network model designed to solve the convex optimization problem, accompanied by comprehensive theoretical analysis and rigorous proofs. The study uses two distinct datasets sourced from Yahoo Finance, consisting of 30 selected stocks, covering a span of 250 valid trading days to validate the proposed methodology. The results of 30 different stock market scenario experiments indicate that, when the upper limit for investment risk is set at <inline-formula> <tex-math>$3.285 times 10^{-4}$ </tex-math></inline-formula>, the expected maximum investment return exceeds the Dow Jones Industrial Average (DJIA) index by 16.2816%. These empirical findings highlight the viability, stability, and efficacy of the proposed approach and framework, demonstrating its potential applicability for real-time, high-frequency trading scenarios. Furthermore, the outcomes suggest policy implications for risk management and portfolio optimization in dynamic financial environments.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"1960-1971"},"PeriodicalIF":8.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10812177","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438398","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}

引用次数: 0

Event-Based Dynamic Quantized Control for Bipartite Consensus

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-12-23 DOI: 10.1109/TSMC.2024.3514697

Jie Wang;Yuchen Dong;Bailing Tian;Qun Zong

引用次数: 0

Optimal Tracking Control of Second-Order Multiagent Systems With Input Delay via Data-Driven Forward Reward Q-Learning Framework

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-12-23 DOI: 10.1109/TSMC.2024.3513561

Kai Rao;Huaicheng Yan;Qiwei Liu;Qingmei Dang;Kaibo Shi

{"title":"Optimal Tracking Control of Second-Order Multiagent Systems With Input Delay via Data-Driven Forward Reward Q-Learning Framework","authors":"Kai Rao;Huaicheng Yan;Qiwei Liu;Qingmei Dang;Kaibo Shi","doi":"10.1109/TSMC.2024.3513561","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3513561","url":null,"abstract":"In this article, an optimal tracking control algorithm is derived for second-order discrete-time multiagent systems (MASs) with unknown system dynamics and input delay. First, the optimal tracking problem of MASs with input delay is constructed by the tracking error and a local performance index function. By designing a new variable, the original model is converted into a model without delay while guaranteeing the equivalence of performance index and control law of each agent. Subsequently, the transformed model and reinforcement learning (RL) theory are integrated to obtain a novel data-driven distributed learning framework. This framework enables online learning of the optimal control law and ensures tracking consensus of all followers’ position and velocity states. Compared to the traditional actor–critic framework, an additional neural network (NN) is utilized to approximate the forward reward information (FRI) to improve the information learning capability of the MASs. Furthermore, the convergence analysis of system states and three NNs structures are conducted by Lyapunov theory. Finally, the proposed framework is verified to have better convergence and require fewer iteration steps than classical actor–critic framework by numerical simulation comparison experiments.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"1858-1869"},"PeriodicalIF":8.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438433","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}

引用次数: 0

UAV-Based Automatic Detection, Localization, and Cleaning of Bird Excrement on Solar Panels

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-12-23 DOI: 10.1109/TSMC.2024.3506533

Yo-Ping Huang;Satchidanand Kshetrimayum;Frode Eika Sandnes

{"title":"UAV-Based Automatic Detection, Localization, and Cleaning of Bird Excrement on Solar Panels","authors":"Yo-Ping Huang;Satchidanand Kshetrimayum;Frode Eika Sandnes","doi":"10.1109/TSMC.2024.3506533","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3506533","url":null,"abstract":"Bird excrement deposited on solar panels can lead to hotspots, significantly reducing the efficiency of solar power plants. This article presents a novel solution to this problem leveraging unmanned aerial vehicle (UAV) systems for the automated geolocation and removal of bird excrement across large-scale solar power facilities. First, a UAV executes a predefined flight path to capture sequential aerial images of the plant. These images are subsequently stitched to produce a high-definition orthomosaic of the entire facility. An advanced detection framework based on YOLOv7, enhanced with an attention module, is employed to accurately detect bird excrement by reducing background noise and highlighting key features. An additional prediction head is integrated to improve detection of smaller bird excrements. To compute precise geolocation of the detected excrement, the midpoint pixel coordinates of the excrement along with the azimuth angle and actual ground distance (AGD) relative to a ground control point (GCP) is used. This article further proposes a cleaning technique that employs a traveling salesman problem (TSP) approximation algorithm to efficiently optimize flight path of the cleaning UAV. Experimental results indicate the system achieves an average detection precision (AP) of 93.91% and GPS coordinate accuracy with an average error of 0.149 m, demonstrating the efficacy of the proposed method in both geolocation and removal of bird excrement from solar panels.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"1657-1670"},"PeriodicalIF":8.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438503","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}

引用次数: 0

A Co-Evolution Algorithm With Dueling Reinforcement Learning Mechanism for the Energy-Aware Distributed Heterogeneous Flexible Flow-Shop Scheduling Problem

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2024-12-23 DOI: 10.1109/TSMC.2024.3510384

Fuqing Zhao;Fumin Yin;Ling Wang;Yang Yu

{"title":"A Co-Evolution Algorithm With Dueling Reinforcement Learning Mechanism for the Energy-Aware Distributed Heterogeneous Flexible Flow-Shop Scheduling Problem","authors":"Fuqing Zhao;Fumin Yin;Ling Wang;Yang Yu","doi":"10.1109/TSMC.2024.3510384","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3510384","url":null,"abstract":"The production process of steelmaking continuous casting (SCC) is a typical heterogeneous distributed manufacturing system. The scheduling problem in heterogeneous distributed manufacturing systems is a complex combinatorial optimization problem. In this article, the energy-aware distributed heterogeneous flexible flow shop scheduling problem (EADHFFSP) with variable speed constraints is studied with objectives, including total tardiness (TTD) and total energy consumption (TEC). A mixed-integer linear programming (MILP) model is constructed for the EADHFFSP. A co-evolution algorithm with dueling reinforcement learning mechanism (DRLCEA) is presented to address EADHFFSP. In DRLCEA, a knowledge-based hybrid initialization operation is proposed to generate the initial population of the problem. A global search based on adversarial generative learning is designed to search the solution space. The dueling double deep Q-network (DDQN) is applied to select the operator for the local search. A speed adjustment strategy and an energy-saving strategy based on knowledge are proposed to reduce TTD and TEC of the EADHFFSP with regard to the properties of EADHFFSP. The results of experiments show that the performance of DRLCEA is superior to certain state-of-the-art comparison algorithms in solving EADHFFSP.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 3","pages":"1794-1809"},"PeriodicalIF":8.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438333","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}

引用次数: 0