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Distributed Optimization and Scaling design for Solving Sylvester Equations

Journal of systems Science & complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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Attention-Modulated Feature Fusion Neural Network for Inverse Modeling of Microwave Filters

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 376-379页

作者： Guo, Linwei Cao, Weihua Hu, Wenkai Lu, Zhengyang Wu, Min China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Inverse modeling is extensively applied in the design and tuning of microwave filters (MFs). Inverse models (IMs) take the features extracted from the high-dimensional electromagnetic parameters as input. How to make full use of features from multiple perspective is a critical issue for improving model accuracy. To solve it, this letter proposes an attention-modulated feature fusion neural network (AMFFNN). AMFFNN achieves multiperspective feature fusion (MPFF) at the input side and independent feature fusion (IFF) at the output side. In addition, feature fusion in AMFFNN is enhanced by attention modules to dynamically identify the importance of each feature. Statistical and comparative results of simulations demonstrate that AMFFNN outperforms existing methods in terms of accuracy, stability, and generalization. © 2023 IEEE.

关键词： Inverse problems

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A zeroth-order algorithm for distributed optimization with stochastic stripe observations

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Science China(Information Sciences) 2023年第9期66卷 297-298页

作者： Yinghui WANG Xianlin ZENG Wenxiao ZHAO Yiguang HONG School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Department of Control Science and Engineering Tongji University

Recent years have seen a rising interest in distributed optimization problems because of their widespread applications in power grids, multi-robot control, and regression *** the last few decades, many distributed algorithms have been developed for tackling distributed optimization problems. In these algorithms, agents over the network only have access to their own local functions and exchange information with their neighbors.

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A Survey on Distributed Network Localization from a Graph Laplacian Perspective

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Journal of systems Science & complexity 2024年第1期37卷 273-293页

作者： HAN Zhimin LIN Zhiyun FU Minyue Artificial Intelligence Institute School of AutomationHangzhou Dianzi UniversityHangzhou 310018China Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent ManufacturingSouthern University of Science and TechnologyShenzhen 518055China Peng Cheng Laboratory Shenzhen 518000China

Network localization serves as a fundamental component for enabling various position based operations in multi-agent systems,facilitating tasks like target searching and formation control by providing accurate position information for all nodes in the *** localization focuses on the challenge of determining the positions of nodes within a network,relying on the known positions of anchor nodes and internode relative *** the past few decades,distributed network localization has garnered significant attention from *** paper aims to provide a review of main results and advancements in the field of distributed network localization,with a particular focus on the perspective of graph *** to its favorable characteristics,graph Laplacian unifies various network localization,even when dealing with diverse types of internode relative measurements,into a unified protocol framework,which can be constructed by a linear method and ensure the global convergence.

关键词： Distributed network localization graph Laplaian sensor network

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Deep reinforcement learning for optimal denial-of-service attacks scheduling

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Science China(Information Sciences) 2022年第6期65卷 98-106页

作者： Fangyuan HOU Jian SUN Qiuling YANG Zhonghua PANG State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology

We consider an optimal denial-of-service(DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remote estimator via a communication channel that is exposed to DoS attackers. However,due to limited energy, an attacker can only attack a subset of sensors at each time step. To maximally degrade the estimation performance, a DoS attacker needs to determine which sensors to attack at each time step. In this context, a deep reinforcement learning(DRL) algorithm, which combines Q-learning with a deep neural network, is introduced to solve the Markov decision process(MDP). The DoS attack scheduling optimization problem is formulated as an MDP that is solved by the DRL algorithm. A numerical example is provided to illustrate the efficiency of the optimal DoS attack scheduling scheme using the DRL algorithm.

关键词： optimal denial-of-service attack scheduling optimization limited energy deep reinforcement learning

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Photovoltaic,thermoelectric and electromagnetic generation technologies applied in power systems for mobile unmanned systems

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Science China(Technological Sciences) 2023年第3期66卷 599-629页

作者： DING Ning WANG XiangYang XIAN XiaoYang CAI YeYun LIU DaoMing DENG Fang Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China

Unmanned systems are increasingly adopted in various fields,becoming an indispensable technology in daily *** systems are the lifeblood of unmanned systems,and affect the working time and task ***,traditional power systems,such as batteries and fuels have a fixed ***,once the power supply is exhausted and cannot be replenished in time,the unmanned systems will stop ***,researchers have increasingly begun paying attention to renewable energy generation *** principles,advantages,and limitations of renewable energy generation technologies are different,and their application effects in different unmanned systems are also *** paper presents a comprehensive study of the application and development status of photovoltaic,thermoelectric,and magnetoelectric generation technologies in four kinds of unmanned systems,including space,aviation,ground,and water,and then summarizes the adaptability and limitations of the three technologies to different ***,future development directions are predicted to enhance the reliability of renewable energy generation technologies in unmanned *** is the first study to conduct a comprehensive and detailed study of renewable energy generation technologies applied in unmanned *** present work is critical for the development of renewable energy generation technologies and power systems for unmanned systems.

关键词： unmanned system renewable energy generation technology photovoltaic generation thermoelectric generation electromagnetic generation

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Trajectory Planning Based on Spatio-Temporal Map with Collision Avoidance Guaranteed by Safety Strip

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IEEE Transactions on intelligent Transportation systems 2022年第2期23卷 1030-1043页

作者： Zhang, Ting Fu, Mengyin Song, Wenjie Yang, Yi Wang, Meiling State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

Trajectory planning for the unmanned vehicle in the complex environment has always been a challenging task. Planned trajectory with the corresponding target velocity or acceleration sequence must be collision-free guaranteed and as comfortable as possible on the premise of obeying the traffic rules and interaction with other dynamic social vehicles. To meet this requirement, this paper proposes a framework for trajectory planning based on spatio-Temporal map. Due to the time layer architecture in the map, the trajectory can be generated with velocity and acceleration simultaneously, and the whole trajectory is constrained within a 'safety strip', resulting in an efficient and safety guaranteed trajectory. The framework is composed of three sections: rough search, fine optimization and safety strip-based collision avoidance. For rough search, we propose an improved A∗ algorithm implemented in the discrete time layer to find out the suboptimal states efficiently. In fine optimization, the B-spline curve is exploited to connect the searched states into a continuous trajectory. And the optimal control points of B-spline are further grouped into several segments, forming the safety strip which is actually the distribution space of the planned trajectory. If necessary, an adjustment will be applied to keep the strip away from the collision zone, making the entire trajectory completely collision-free. Experiments on both public dataset and self-driving simulator show that the proposed framework can adapt to different kinds of complex traffic scenes well. © 2000-2011 IEEE.

关键词： Collision avoidance

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Group Hybrid Coordination control of Multi-Agent systems With Time-Delays and Additive Noises

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 737-748页

作者： Chuanjian Li Xiaofeng Zong School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China IEEE

A new kind of group coordination control problemgroup hybrid coordination control is investigated in this *** group hybrid coordination control means that in a whole multi-agent system(MAS)that consists of two subgroups with communications between them,agents in the two subgroups achieve consensus and containment,*** MASs with both time-delays and additive noises,two group control protocols are proposed to solve this problem for the containment-oriented case and consensus-oriented case,*** developing a new analysis idea,some sufficient conditions and necessary conditions related to the communication intensity betw een the two subgroups are obtained for the following two types of group hybrid coordination behavior:1)Agents in one subgroup and in another subgroup achieve weak consensus and containment,respectively;2)Agents in one subgroup and in another subgroup achieve strong consensus and containment,*** is revealed that the decay of the communication impact betw een the two subgroups is necessary for the consensus-oriented ***,the validity of the group control results is verified by several simulation examples.

关键词： Additive noises consensus control containment control group hybrid coordination control multi-agent systems(MASs) time-delays

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Multi-Agent Reinforcement Learning with Region Division and Leader-Follower Communication

Multi-Agent Reinforcement Learning with Region Division and ...

引用

2024 China Automation Congress, CAC 2024

作者： Wang, Chao Xu, Xiang Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798350368604

The integration of multi-agent reinforcement learning (MARL) into complex systems has paved new ways for collaborative problem-solving. However, traditional approaches to MARL frequently encounter the challenge of achieving efficient communication among agents, essential for coordinated action. This paper introduces a region division and leader-follower(RDLF) communication algrithm with the MARL framework. RDLF divides the environment into several regions, each managed by a leader agent that coordinates the actions of follower agents and handling inter-region communication. This hierarchical structure reduces unnecessary communication, enhancing learning efficiency. Experimental results in multi-particle environments demonstrate RDLF’s superiority over existing MARL algorithms, especially with increasing agent numbers. RDLF effectively addresses scalability and communication challenges in large-scale multi-agent systems, providing a robust foundation for its application in complex and dynamic environment. © 2024 IEEE.

关键词： Adversarial machine learning

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Dictionary-Labeled A∗: Optimal Path-and-Posture Planning for Mobile Robots with Turning Radius Constraints

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IEEE Robotics and Automation Letters 2024年第12期9卷 10740-10747页

作者： Yang, Chen Lin, Zhiyun Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen518055 China Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Shenzhen518055 China Peng Cheng Laboratory Shenzhen518066 China

In this study, we introduce an algorithm called Dictionary-Labeled A∗ (DL-A∗), which addresses the critical problem of path-and-posture planning for mobile robots with turning radius constraints. DL-A∗ utilizes a dictionary to store and process multi-element information within an enhanced topological map. The algorithm categorizes the edges in the map into forward driving and reverse driving states, and constructs sets of successive states that adhere to road segment constraints through map pre-processing. Subsequently, the A∗ algorithm is employed to find an optimal path to reach the endpoint while satisfying the end posture constraint, with the objective of minimizing the total travelling cost. Simulation results demonstrate that, in comparison to other popular algorithms, our algorithm offers the capability to adapt the robot's forward and reverse driving postures to minimize total cost, and exhibits exceptional searching efficiency. © 2016 IEEE.

关键词： Robot programming

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