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Enhanced distributed state estimation with resilience to multiple disturbances and false data injection attacks

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第2期32卷 1075-1092页

作者： Guo, Xiaoyu Cao, Songyin Ding, Zhengtao Univ Manchester Dept Elect & Elect Engn Manchester M1 3WE Lancs England Yangzhou Univ Coll Informat Engn Dept Automat Yangzhou Jiangsu Peoples R China

Disturbances and attacks co-exist widely in networked systems. distributed state estimation of networked systems in the simultaneous presence of disturbances and attacks has been widely recognized as a challenging issue. In this article, an enhanced three-stage approach for the detection and rejection of attacks is established for networked systems subject to multiple disturbances and false data injection (FDI) attacks. First, a multi-layer distributed estimator with a disturbance observer layer is proposed. Then, an optimal observer-based attack detection scheme is designed to specify an attack detection logic. After disturbance rejection and attack detection, an attack-resilient estimator with a dynamic detection-triggered structure is proposed to actively reject FDI attacks online. This three-stage approach effectively separates the influence of disturbances and attacks in the distributed estimation process, enabling multiple heterogeneous disturbances and attacks across the sensor network to be rejected. A numeric example and comparisons are provided to illustrate the effectiveness of the proposed approach.

关键词： attack rejection distributed algorithms disturbance rejection resilient estimation sensor networks

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distributed Global Optimization for a Class of Nonconvex Optimization With Coupled Constraints

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第8期67卷 4322-4329页

作者： Ren, Xiaoxing Li, Dewei Xi, Yugeng Shao, Haibin Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Engn Res Ctr Intelligent Control & Manag Key Lab Syst Control & Informat Proc Minist Educ China Shanghai 200240 Peoples R China

This article examines the distributed nonconvex optimization problem with structured nonconvex objective functions and coupled convex inequality constraints on static networks. A distributed continuous-time primal-dual algorithm is proposed to solve the problem. We use the canonical transformation and Lagrange multiplier method to reformulate the nonconvex optimization problem as a convex-concave saddle point computation problem, which is subsequently solved by employing the projected primal-dual subgradient method. Sufficient conditions that guarantee the global optimality of the solution generated by the proposed algorithm are provided. Numerical and application examples are presented to demonstrate the proposed algorithm.

关键词： Optimization Signal processing algorithms Linear programming Convergence Manganese distributed algorithms Convex functions Canonical duality distributed nonconvex optimization global optimization primal-dual method

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distributed Primal-Dual Splitting Algorithm for Multiblock Separable Optimization Problems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第8期67卷 4264-4271页

作者： Li, Huaqing Wu, Xiangzhao Wang, Zheng Huang, Tingwen Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Texas A&M Univ Sci Program Doha 23874 Qatar

This article considers the distributed structured optimization problem of collaboratively minimizing the global objective function composed of the sum of local cost functions. Each local objective function involves a Lipschitz-differentiable convex function, a nonsmooth convex function, and a linear composite nonsmooth convex function. For such problems, we derive the synchronous distributed primal-dual splitting (S-DPDS) algorithm with uncoordinated stepsizes. Meanwhile, we develop the asynchronous version of the algorithm in light of the randomized block-coordinate method (A-DPDS). Further, the convergence results show the relaxed range and concise form of the acceptable parameters, which indicates that the algorithms are conducive to the selection of parameters in practical applications. Finally, we demonstrate the efficiency of S-DPDS and A-DPDS algorithms by numerical experiments.

关键词： Optimization Convex functions distributed algorithms Convergence Linear programming Couplings Simulation Asynchronous algorithm distributed optimization primal-dual splitting algorithm uncoordinated stepsizes

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distributed Q-Learning Algorithm for Dynamic Resource Allocation With Unknown Objective Functions and Application to Microgrid

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第11期52卷 12340-12350页

作者： Dai, Pengcheng Yu, Wenwu Chen, Duxin Southeast Univ Sch Math Jiangsu Key Lab Networked Collect Intelligence Nanjing 210096 Peoples R China

Dynamic resource allocation problem (DRAP) with unknown cost functions and unknown resource transition functions is studied in this article. The goal of the agents is to minimize the sum of cost functions over given time periods in a distributed way, that is, by only exchanging information with their neighboring agents. First, we propose a distributed Q-learning algorithm for DRAP with unknown cost functions and unknown resource transition functions under discrete local feasibility constraints (DLFCs). It is theoretically proved that the joint policy of agents produced by the distributed Q-learning algorithm can always provide a feasible allocation (FA), that is, satisfying the constraints at each time period. Then, we also study the DRAP with unknown cost functions and unknown resource transition functions under continuous local feasibility constraints (CLFCs), where a novel distributed Q-learning algorithm is proposed based on function approximation and distributed optimization. It should be noted that the update rule of the local policy of each agent can also ensure that the joint policy of agents is an FA at each time period. Such property is of vital importance to execute the epsilon-greedy policy during the whole training process. Finally, simulations are presented to demonstrate the effectiveness of the proposed algorithms.

关键词： Resource management Cost function Approximation algorithms Heuristic algorithms Training distributed algorithms Smart grids distributed optimization distributed Q-learning dynamic resource allocation function approximation

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distributed Combined Acoustic Echo Cancellation and Noise Reduction in Wireless Acoustic Sensor and Actuator Networks

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2022年 30卷 534-547页

作者： Ruiz, Santiago van Waterschoot, Toon Moonen, Marc Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS B-3001 Leuven Belgium

The paper presents distributed algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor and actuator network (WASAN) where each node may have multiple microphones and multiple loudspeakers, and where the desired signal is a speech signal. A centralized integrated AEC and NR algorithm, i.e., multichannel Wiener filter (MWF), is used as starting point where echo signals are viewed as background noise signals and loudspeaker signals are used as additional input signals to the algorithm. By including prior knowledge (PK), namely that the loudspeaker signals do not contain any desired signal component, an alternative centralized cascade algorithm (PK-MWF) is obtained with an AEC stage first followed by an MWF-based NR stage which has a lower computational complexity. distributed algorithms can then be obtained from the MWF and PK-MWF algorithm, i.e., the generalized eigenvalue decomposition (GEVD)-based distributed adaptive node-specific signal estimation (DANSE) and PK-GEVD-DANSE algorithm, respectively. In the former, each node performs a reduced dimensional integrated AEC and NR algorithm and broadcasts only 1 fused signal (instead of all its signals) to the other nodes. In the PK-GEVD-DANSE algorithm, each node performs a reduced dimensional cascade AEC and NR algorithm and broadcasts only 2 fused signals (instead of all its signals) to the other nodes. The distributed algorithms achieve the same performance, upon convergence, as the corresponding centralized integrated (MWF) and centralized cascade (PK-MWF) algorithm. It is observed, however, that the communication cost in the PK-GEVD-DANSE algorithm can also be reduced, where each node then broadcasts only 1 fused signal (instead of 2 signals) to the other nodes. The resulting algorithm, referred to as the pruned PK-GEVD-DANSE (pPK-GEVD-DANSE) algorithm, then effectively combines the lowest possible communication cost (as low as in the GEVD-DANSE algorithm) wit

关键词： Loudspeakers Signal processing algorithms Microphones Acoustics distributed algorithms Echo cancellers Speech processing distributed signal processing wireless acoustic sensor and actuator networks acoustic echo cancellation noise reduction multichannel Wiener filter

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distributed Estimation of Time-Varying Biases in Relative State Measurements

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第8期67卷 4278-4284页

作者： Shi, Mingming Catholic Univ Louvain ICTEAM B-1348 Louvain la Neuve Belgium

This article focuses on the distributed estimation of time-varying bias signals in relative state measurements of sensors, where each sensor measures the relative state of neighboring sensors and the measurements contain a time-varying bias signal that is generated from a linear exo-systems. Assume that sensors can communicate with others, we propose several distributed bias estimators, by which each sensor can reconstruct its own bias signal and the exo-system's state using local information.

关键词： Sensors Sea measurements Measurement uncertainty Estimation Time measurement Matrix decomposition Sensor systems Bipartite graph observability estimation distributed algorithms wireless sensor networks

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A distributed Connectivity Maintenance Algorithm With Formal Guarantees for a Communication- Constrained Network of Unmanned Underwater Vehicles

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IEEE SYSTEMS JOURNAL 2022年第2期16卷 1774-1785页

作者： Muniraj, Devaprakash He, Hans Farhood, Mazen J. Stilwell, Daniel Virginia Polytech Inst & State Univ Kevin T Crofton Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA Virginia Polytech Inst & State Univ Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA

This article presents a distributed connectivity maintenance algorithm for a multiagent system composed of unmanned underwater vehicles (UUVs). We consider a homogeneous network of UUVs that move on a 2-D plane with bounded linear speeds. The time-division multiple-access protocol, which is a commonly used communication protocol for UUVs, is used by the agents in the network to communicate with each other. Due to the communication constraints imposed by the protocol, the agents do not have access to the current positions of their neighbors. This presents a significant challenge in developing a connectivity maintenance algorithm that can be formally verified. Using only local information from an agent's neighbors, the algorithm provides motion constraints for each agent in the network, which can then be used by each UUV's motion planner to plan a connectivity-preserving reference trajectory. In addition to providing mathematical proofs expressed in a way that is typical in the systems and control community, we give formal proofs of the results that establish network connectivity for the presented algorithm under suitable assumptions on each agent's motion planner and controller. The formal proofs are carried out using the KeYmaera X theorem prover, which is an interactive formal verification tool for hybrid systems. This article also highlights the utility of formal verification tools in identifying scenarios that could be overlooked when employing proofs that are not carried out in a formal system for complex distributed algorithms such as the one presented in this article.

关键词： Robustness Maintenance engineering distributed algorithms Safety Protocols Heuristic algorithms Cognition Connectivity maintenance formal verification time-division multiple access (TDMA) theorem proving unmanned underwater vehicles (UUVs)

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Real-Time Asynchronous Information Processing in distributed Power Systems Control

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IEEE TRANSACTIONS ON SMART GRID 2022年第1期13卷 773-782页

作者： Cintuglu, Mehmet H. Ishchenko, Dmitry ABB Inc Raleigh NC 27610 USA Hitachi ABB Power Grids AG Power Grids Res Raleigh NC 27606 USA

Centralized power system algorithms are gradually augmented and substituted with distributed counterparts, where timely updated asynchronous information processing is critical for real-time implementation. This paper presents two methods to achieve the desired real-time performance. distributed sequencing method simulates a synchronous system on an asynchronous network using a distributed logical vector clock. The second method, adaptive asynchronous processing, handles asynchrony at each iteration step through adaptive dynamics of the network agents. Necessity of the developed methods is discussed based on the specific requirements of the communication network in terms of algorithm accuracy, network size and time-varying message delays. A case study on the distributed power system state estimation for networked microgrids is presented to validate the developed methods implementing industrial publish/subscribe protocols. The results demonstrate that the developed information processing methods can be efficiently applied for real-world industrial implementation.

关键词： Clocks distributed algorithms Servers Real-time systems Synchronization Information processing Smart grids distributed algorithm asynchronous information processing publish subscribe power systems

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A Reinforcement Learning Approach for D2D Spectrum Sharing in Wireless Industrial URLLC Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第5期21卷 5410-5419页

作者： Sanusi, Idayat O. Nasr, Karim M. Univ Greenwich Fac Engn & Sci London ME4 4TB Kent England

distributed Radio Resource Management (RRM) solutions are gaining an increasing interest recently, especially when a large number of devices are present as in the case of a wireless industrial network. Self-organisation relying on distributed RRM schemes is envisioned to be one of the key pillars of 5G and beyond Ultra Reliable Low Latency Communication (URLLC) networks. Reinforcement learning is emerging as a powerful distributed technique to facilitate self-organisation. In this paper, spectrum sharing in a Device-to-Device (D2D)-enabled wireless network is investigated, targeting URLLC applications. A distributed scheme denoted as Reinforcement Learning Based Matching (RLBM) which combines reinforcement learning and matching theory, is presented with the aim of achieving an autonomous device-based resource allocation. A distributed local Q-table is used to avoid global information gathering and a stateless Q-learning approach is adopted, therefore reducing requirements for a large state-action mapping. Simulation case studies are used to verify the performance of the presented approach in comparison with other RRM techniques. The presented RLBM approach results in a good tradeoff of throughput, complexity and signalling overheads while maintaining the target Quality of Service/Experience (QoS/QoE) requirements of the different users in the network.

关键词： Quality of service Resource management Throughput Wireless communication Interference Fifth generation (5G) and beyond wireless networks radio spectrum management (RRM) distributed algorithms device-to-device communication (D2D) reinforcement learning matching theory reinforcement learning matching theory

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Diffusion Generalized Minimum Total Error Entropy Algorithm

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 751-755页

作者： Cai, Peng Lin, Dongyuan Qian, Junhui Zheng, Yunfei Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Biopercept & Intelligent Informa Chongqing 400044 Peoples R China

Both the minimum error entropy (MEE) and mixture MEE (MMEE) are extensively employed in distributed adaptive filters, exhibiting their robustness against non-Gaussian noise by capturing high-order statistical information from network data. However, the fixed shape of the Gaussian kernel function existing in MEE and MMEE restricts their flexibility, leading to reduced robustness and deteriorated performance. To address this issue, a novel diffusion generalized minimum total error entropy (DGMTE) algorithm is first proposed in this letter, using a generalized MEE criterion to significantly improve the performance of error-in-variables models-based algorithms under non-Gaussian noise. Moreover, as a special case of DGMTE, a generalized minimum total error entropy (GMTE) algorithm is also proposed, and the local convergence analysis of DGMTE is given. Finally, simulations show the superiorities of DGMTE in comparison with other representative algorithms.

关键词： Noise Signal processing algorithms Entropy Shape Adaptation models Kernel Estimation distributed algorithms Cost function Convergence Adaptive filter distributed estimation errors-in-variables model generalized minimum error entropy total least squares

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