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Semi-Global Leaderless Consensus of Linear Multi-agent systems with Actuator and Communication Constraints

Journal of systems Science & Complexity 2020年第4期33卷 882-902页

作者： WANG Jian SHI Liangren GUAN Xinping Department of Automation and Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Shanghai Key Laboratory of Navigation and Location-based Services and Academy of Information Technology and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

This paper studies the leaderless consensus problems of multi-agent systems with input saturation and intermittent communication over directed networks. Both the state feedback and the output feedback consensus algorithms are developed based on low gain feedback approach. The convergence of the trajectories of all agents can be achieved by these proposed algorithms, if the communication topology has a directed spanning tree, and the intermittent communication period T and time rate ρ are larger than their associated threshold values. Simulation examples are provided to verify the theoretical results.

关键词： agent directed communication

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Invariant Tensor Modeling and Functional Matrix Coding of Flight Dynamics for Nonlinear Aircraft control

Invariant Tensor Modeling and Functional Matrix Coding of Fl...

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Chinese control and Decision Conference, CCDC

作者： Tangwen Yin Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781665478977

Flight trajectory consistence is an important factor influencing mission performance and safety. However, the lack of in-situ flight dynamics with high fidelity remains a challenge to enhance stability and controllability for aircraft maneuver in space, because modeling and coding of predictive system response in control synthesis is difficult to be mathematically formulated and computationally implemented. Here we generalize the invariant tensor modeling of flight dynamics so that its functional matrix coding is adapted to the multivariable nonlinear control of aircraft with augmented stability and controllability; and we verify that the nonlinear functional parameters in control synthesis with unobservable external forces and moments can improve the consistence of flight trajectory. Results demonstrate that the planned flight trajectory can be consistently fulfilled using the nonlinear control synthesized through tensor modeling and matrix coding. In conclusion, invariant tensor modeling facilitates the functional matrix coding of flight dynamics with six degrees of freedom, which can be applied to build the feedback through dimensional analysis for augmented stability and controllability, such that collaborative optimization of nonlinear functional parameters establishes the multivariable nonlinear control to improve the consistence of flight trajectory for sustained performance and continuing safety.

关键词： Analytical models Tensors Atmospheric modeling Computational modeling controllability Encoding Stability analysis

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Efficient Frame Optimization Scheme for PSAM Assisted Transmission over Rician Fading Channel

Efficient Frame Optimization Scheme for PSAM Assisted Transm...

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information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Yuqian Yan Zhongyang Yu Mengmeng Xu Hengzhou Xu Hai Zhu Chunhua Zhu School of Computer Science Henan University of Engineering Zhengzhou China Key Laboratory of Grain Information Processing Control (Henan University of Technology) Ministry of Education Zhengzhou China

ISBN: (数字)9798350351699

ISBN: (纸本)9798350351705

In this paper, we discuss the commonly used pilot-symbol-assisted-modulation (PSAM) assisted transmission and its corresponding frame optimization problem over Rician fading channel. Specifically, we design a generalized PSAM (G-PSAM) format based on the standard PSAM (S-PSAM) format and derive its approximate joint data-aided & non-data-aided Cramér-Rao bound (DA&NDA CRB) when the pilot-aided channel estimation is considered. Then, with the approximate DA&NDA CRB and the classical control-variate method (CVM), an efficient optimization scheme of the G-PSAM format is proposed, which is asymptotically optimal in terms of the mean-square error (MSE) estimation performance. Simulation results reveal the superiority of the optimized G-PSAM (OG-PSAM) format over the S-PSAM format for both short-packet and long-packet transmissions. Also, a practical example of the joint DA&NDA aided carrier synchronization is provided.

关键词： Fading channels Simulation Rician channels Estimation Channel estimation Mean square error methods Synchronization

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A New Hybrid Adaptive Self-Loading Fxlms and Cnn-Gru Net for Active Time-Varying Noise control

SSRN

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SSRN 2024年

作者： Zhu, Wenzhao Luo, Lei Key Laboratory of Intelligent Control and Neural Information Processing Education Ministry of China Yanshan University Qinhuangdao066004 China Key Laboratory of Optoelectronic Technology and Systems Education Ministry of China Chongqing University Chongqing400044 China

Active noise control (ANC) provides a lightweight and quick-to-deploy noise attenuation solution compared to traditional passive noise control. However, time-varying noise caused by the changing acoustic environment usually occurs in moving spaces, such as a car cabin, which further deteriorates the robustness of the adaptive filter and even deep networks in the ANC system. To use a deep ANC neural network with great denoising performance in a complex time-varying noise environment and reduce the interference of uncertain noise on the stability of the network, a new hybrid adaptive self-loading filtered-x least mean square (ASL-FxLMS) and convolutional neural network-gate recurrent unit (CNN-GRU) network is proposed. First, based on the CNN-LSTM structure, an improved CNN-GRU network with a multi-head attention mechanism is proposed to enhance attenuation performance for complicated periodical components in the time-varying noise and decrease the computing load as much as possible. In addition, an improved time-delay estimator is applied to improve the real-time ANC performance of CNN-GRU. Meanwhile, an adaptive self-loading FxLMS algorithm has been developed to deal with the uncertain components of time-varying noise. Moreover, to achieve balance attenuation, robustness, and tracking performance, the ASL-FxLMS and CNN-GRU are connected by a convex combination structure. Furthermore, theoretical analysis and simulations are also conducted to show the effectiveness of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Active noise control

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Task Order Filing Generation Algorithm Based on Multidimensional Association Rules and Similarity Matching

Task Order Filing Generation Algorithm Based on Multidimensi...

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2024 International Conference Optoelectronic information and Optical Engineering, OIOE 2024

作者： Zhang, Xinyue Lv, Weimin Wei, Dingjiang Yang, Dongsheng Yang, Lin Naval Aviation University Yantai China No. 91593 of PLA Sanya China The Laboratory for Military-Civilian Integration Emergency Command and Control Jinan University Guangzhou China School of Public Management/Emergency Management Jinan University Guangzhou China Key Laboratory of Information System Engineering National University of Defense Technology Changsha China School of Information and Intelligent Engineering University of Sanya Academician Guoliang Chen Team Innovation Center University of Sanya Sanya China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

This paper examines the potential for leveraging the order dispatch algorithm in the commercial domain to enhance the intelligent allocation of military combat tasks, thereby improving the speed and accuracy of command decision-making in the context of intelligent warfare. The study proposes an "order-based" command and control model, which aims to overcome the efficiency bottleneck and information lag problem in traditional combat task allocation. The algorithmic design incorporates multi-dimensional association rules and similarity matching algorithms to guarantee the optimal matching of combat tasks and resources, thereby achieving the objectives of rapid response, efficient resource utilization, and enhanced combat effectiveness. This paper focuses on the challenges and advantages of this model in the context of naval warfare. It highlights the strategic value of order-based command and control in non-direct confrontation missions, such as reconnaissance, patrol, and intelligence gathering. The utilization of the "combat cloud," artificial intelligence, and a highly integrated command network enables the precise allocation of resources and the rapid execution of tasks. This, in turn, enhances the ISR capability, network-centered warfare effectiveness, and cross-domain synergistic warfare capability in naval warfare. The study also delineates the specific implementation process of the algorithm, which includes multi-dimensional association rule mining and similarity matching based on historical cases, with the objective of generating optimal combat scenarios and ensuring the effective execution of task orders. © 2025 SPIE.

关键词： Naval warfare

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Learning the continuous-time optimal decision law from discrete-time rewards

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National Science Open 2024年第5期3卷 130-147页

作者： Ci Chen Lihua Xie Kan Xie Frank Leroy Lewis Yilu Liu Shengli Xie School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou 510006China UTA Research Institute the University of Texas at ArlingtonFort Worth 76118USA Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA Oak Ridge National Laboratory Oak Ridge 37830USA Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social *** an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement *** this work,we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical *** find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral *** apply this finding to solve output-feedback design problems in power *** results reveal that our approach removes an intermediate stage of identifying dynamical *** work suggests that the discrete-time reward is efficient in search of the desired decision law,which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.

关键词： continuous-time state and action decision law learning discrete-time reward dynamical systems reinforcement learning

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Data-Driven Safe controller Synthesis for Deterministic systems: A Posteriori Method With Validation Tests

arXiv

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arXiv 2023年

作者： Chen, Yu Shang, Chao Huang, Xiaolin Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

In this work, we investigate the data-driven safe control synthesis problem for unknown dynamic systems. We first formulate the safety synthesis problem as a robust convex program (RCP) based on notion of control barrier function. To resolve the issue of unknown system dynamic, we follow the existing approach by converting the RCP to a scenario convex program (SCP) by randomly collecting finite samples of system trajectory. However, to improve the sample efficiency to achieve a desired confidence bound, we provide a new posteriori method with validation tests. Specifically, after collecting a set of data for the SCP, we further collect another set of independent validate data as posterior information to test the obtained solution. We derive a new overall confidence bound for the safety of the controller that connects the original sample data, the support constraints, and the validation data. The efficiency of the proposed approach is illustrated by a case study of room temperature control. We show that, compared with existing methods, the proposed approach can significantly reduce the required number of sample data to achieve a desired confidence bound. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Disentangle Nighttime Lens Flares: Self-supervised Generation-based Lens Flare Removal 39

Disentangle Nighttime Lens Flares: Self-supervised Generatio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： He, Yuwen Wang, Wei Wu, Wanyu Jiang, Kui Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)157735897X

Lens flares arise from light reflection and refraction within sensor arrays, whose diverse types include glow, veiling glare, reflective flare and so on. Existing methods are specialized for one specific type only, and overlook the simultaneous occurrence of multiple typed lens flares, which is common in the real-world, e.g. coexistence of glow and displacement reflections from the same light source. These co-occurring lens flares cannot be effectively resolved by the simple combination of individual flare removal methods, since these coexisting flares originates from the same light source and are generated simultaneously within the same sensor array, exhibit a complex interdependence rather than simple additive relation. To model this interdependent flares’ relationship, our Nighttime Lens Flare Formation model is the first attempt to learn the intrinsic physical relationship between flares on the imaging plane. Building on this physical model, we introduce a solution to this joint flare removal task named Self-supervised Generation-based Lens Flare Removal Network (SGLFR-Net), which is self-supervised without pre-training. Specifically, the nighttime glow is detangled in PSF Rendering Network(PSFR-Net) based on PSF Rendering Prior, while the reflective flare is modelled in Texture Prior Based Reflection Flare Removal Network (TPRR-Net). Empirical evaluations demonstrate the effectiveness of the proposed method in both joint and individual glare removal tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Luminescent devices

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Resilient Average Consensus: A Detection and Compensation Approach

arXiv

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arXiv 2022年

作者： Zheng, Wenzhe He, Zhiyu He, Jianping Zhao, Chengcheng Fang, Chongrong Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education of China China State Key Laboratory of Industrial Control Technology Zhejiang University China

We study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. To protect consensus value from being influenced by misbehaving nodes, we address this problem by detecting misbehaviors, mitigating the corresponding adverse impact and achieving the resilient average consensus. In this paper, general types of misbehaviors are considered, including deception attacks, accidental faults and link failures. We characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection-compensation-based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound. Considering scenarios where information sets are intermittently available due to link failures, a stochastic extension named stochastic detection-compensation-based consensus (S-DCC) algorithm is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient average consensus exactly and in expectation, respectively. Then, the Wasserstein distance is introduced to analyze the accuracy of S-DCC. Finally, extensive simulations are conducted to verify the effectiveness of the proposed algorithms. © 2022, CC BY.

关键词： Stochastic systems

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Multi-task Guided Blind Omnidirectional Image Quality Assessment with Feature Interaction

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Liu, Yun Li, Sifan Duan, Huiyu Zhou, Yu Fan, Daoxin Zhai, Guangtao Liaoning University Faculty of Information Shenyang110036 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai200240 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xuzhou First People’s Hospital Xuzhou221116 China

Omnidirectional image quality assessment (OIQA) has become an increasingly vital problem in recent years. Most previous blind OIQA methods only extract local features from the distorted viewports, or extract global features from the entire distorted image, lacking the interaction and fusion between local and global features. Moreover, the lack of reference information also limits their performance. Thus, we propose a blind OIQA model which consists of three novel modules, including a bidirectional pseudo-reference module, a Mamba-based global feature extraction module, and a multi-scale local-global feature aggregation module. Specifically, by considering the image distortion degradation process, a bidirectional pseudo-reference module capturing the error maps on viewports is first constructed to refine the multi-scale local visual features, which can supply rich quality degradation reference information without the reference image. To well complement the local features, the VMamba module is adopted to extract the representative multi-scale global visual features. Inspired by human hierarchical visual perception characteristics, a novel multi-scale aggregation module is built to strengthen the feature interaction and effective fusion which can extract deep semantic information. Finally, motivated by the multi-task managing mechanism of human brain, a multi-task learning module is introduced to assist the main quality assessment task by digging the hidden information in compression type and distortion degree. Extensive experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the blind OIQA task compared to other models. © 1991-2012 IEEE.

关键词： Image quality

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