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Online distributed tracking of generalized Nash equilibrium on physical networks——Closing the Loop via Measurement Feedback

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Autonomous Intelligent systems 2021年第1期1卷 69-80页

作者： Yifan Su Feng Liu Zhaojian Wang Shengwei Mei Qiang Lu The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina The Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaDepartment of AutomationShanghai Jiao Tong UniversityShanghaiChina

In generalized Nash equilibrium(GNE)seeking problems over physical networks such as power grids,the enforcement of network constraints and time-varying environment may bring high computational *** online algorithms is recognized as a promising method to cope with this challenge,where the task of computing system states is replaced by directly using measured values from the physical *** this paper,we propose an online distributed algorithm via measurement feedback to track the GNE in a time-varying networked resource sharing *** that some system states are not measurable and measurement noise always exists,a dynamic state estimator is incorporated based on a Kalman filter,rendering a closed-loop dynamics of measurement-feedback driven online *** prove that,with a fixed step size,this online algorithm converges to a neighborhood of the GNE in *** simulations validate the theoretical results.

关键词： Generalized Nash equilibrium Distributed optimization Online optimization Feedback-based optimization State estimation Sharing market

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Photo-induced frequency engineering for nonlinear photonics in chalcogenide microresonators

Photo-induced frequency engineering for nonlinear photonics ...

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CLEO: Science and Innovations, S and I 2022

作者： Xia, Di Li, Yufei Wang, Zifu Zhao, Jiaxin Wu, Jiayue Liu, Dong Luo, Liyang Zhang, Bin Li, Zhaohui Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electrical and Information Technology Sun Yat-sen University Guangzhou510275 China Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Guangzhou510275 China Zhuhai519000 China

We present a novel approach to modify the specific mode frequency in chalcogenide microring resonators and demonstrate an efficient and widely tunable optical parametric oscillation, which is independent of geometry d... 详细信息

ISBN: (纸本)9781557528209

关键词： Microresonators

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Robust Stability and Stabilization Conditions for Fractional-order systems with Distributed Delays and Polytopic Uncertainties

Robust Stability and Stabilization Conditions for Fractional...

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Chinese control and Decision Conference, CCDC

作者： Yi-Nan Chen Jun-Guo Lu Zhen Zhu Jia-Rui Zhang Qing-Hao Zhang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper investigates the robust stability and stabilization conditions for fractional-order systems with distributed delays (FOSDDs) with polytopic uncertainties. To begin with, two robust stability conditions for FOSDDs with polytopic uncertainties are formulated. Subsequently, the state feedback controllers are put forward to robustly stabilize the FOSDDs with polytopic uncertainties. The obtained results are illustrated utilizing linear matrix inequalities (LMIs). Finally, two numerical instances are provided to showcase the effectiveness of the derived conditions.

关键词： State feedback Uncertainty Robust stability Delays Linear matrix inequalities Numerical stability

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Optimal Planning of Zero-Carbon Off-Grid Ammonia-Hydrogen-Based Microgrids Considering Hybrid Types of Electrolyzer Modules

Optimal Planning of Zero-Carbon Off-Grid Ammonia-Hydrogen-Ba...

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Power System and Green Energy Conference (PSGEC)

作者： Junhao Gu Longyan Li Chao Ning Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350365573

ISBN: (纸本)9798350365580

The off-grid ammonia-hydrogen-based microgrid is recognized as a promising paradigm for industrial decarbonization. In this paper, we propose a novel planning model for off-grid ammonia-hydrogen-based microgrids, where the hydrogen is produced in a distributed manner and the ammonia is synthesized in a large-scale energy-intensive plant. In specific, the proposed planning model considers hybrid types of electrolyzer modules and systematically investigates the operational constraints of distribution networks and hydrogen storage equipment, enabling distributed flexible hydrogen production. To seamlessly bridge distributed hydrogen sources and centralized ammonia production site, the delivery of hydrogen through the transportation network is structured as a vehicle routing problem. Eventually, the effectiveness of the proposed model is validated through case studies. Simulation results show that the proposed model lowers the total system cost by 11.8% compared with the simplified scenario which uses one single type of electrolyzer.

关键词： Ammonia Costs Hydrogen Vehicle routing Production Microgrids Distribution networks Planning Numerical models Optimization

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Real-Time Routing Design for LEO Satellite Networks: An Enhanced Multi-Agent DRL Approach

Real-Time Routing Design for LEO Satellite Networks: An Enha...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Hailong Liao Xian Zhang Jiaen Zhou Xuehua Li Key Laboratory of Information and Communication Systems Ministry of Information Industry and the Key Laboratory of Modern Measurement & Control Technology School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Low Earth Orbit (LEO) satellite networks have been heralded as a promising solution for provisioning space-based Internet services. However, the inherent dynamic topology, characterized by frequent variations in inter-satellite link states, causes pronounced fluctuations in latency and exacerbated packet loss rates. To address the challenge, in this paper, we formulate a routing design and optimization problem to maximize the average throughput with considering the end-to-end latency constraint and the real-time routing demand in LEO satellite networks. To solve this problem, we design an enhanced Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, which encapsulates the intricate interplay among LEO satellite agents and their environs within a Real-Time Markov Decision Process (RTMDP) framework, and introduces the self-attention mech-anism to endow agents with a better grasp of environmental state dynamics. Simulation results demonstrate that our proposed routing design outperforms the benchmark schemes in terms of both end-to-end latency and network average throughput.

关键词： Satellites Network topology Simulation Heuristic algorithms Low earth orbit satellites Routing Throughput Real-time systems Topology Optimization

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Geometry-Aware 3D Salient Object Detection Network

arXiv

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arXiv 2025年

作者： Wang, Chen Zhang, Liyuan Hui, Le Liu, Qi Dai, Yuchao Shaanxi Key Laboratory of Information Acquisition and Processing Northwestern Polytechnical University China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology China

Point cloud salient object detection has attracted the attention of researchers in recent years. Since existing works do not fully utilize the geometry context of 3D objects, blurry boundaries are generated when segmenting objects with complex backgrounds. In this paper, we propose a geometry-aware 3D salient object detection network that explicitly clusters points into superpoints to enhance the geometric boundaries of objects, thereby segmenting complete objects with clear boundaries. Specifically, we first propose a simple yet effective superpoint partition module to cluster points into superpoints. In order to improve the quality of superpoints, we present a point cloud class-agnostic loss to learn discriminative point features for clustering superpoints from the object. After obtaining superpoints, we then propose a geometry enhancement module that utilizes superpoint-point attention to aggregate geometric information into point features for predicting the salient map of the object with clear boundaries. Extensive experiments show that our method achieves new state-of-the-art performance on the PCSOD dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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Geometry-Aware 3D Salient Object Detection Network 39

Geometry-Aware 3D Salient Object Detection Network

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

ISBN: (纸本)157735897X

关键词： Geometry

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Accelerated Simulation of Master Equation for Open Quantum systems

Accelerated Simulation of Master Equation for Open Quantum S...

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第42届中国控制会议

作者： Bo Yue Yanfang Wang Shibei Xue Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

Simulation of open quantum systems is undoubtedly critical to design of quantum devices and applications of quantum technologies since dynamics of open quantum systems cannot be solved analytically in general. However, the dimension of open quantum systems grows exponentially as the number of their subsystems increases or as involving complicated environments,which reduces the computational efficiency of the simulation. In this paper, we boost numerical simulation of master equations of open quantum systems from several aspects with which the total time and the occupied memory in the simulation are significantly reduced without losing precision. Our method can be applied to the simulation of non-Markovian quantum systems described by an augmented system model as well. We conduct numerical experiments on simulation of a non-Markovian spin *** with the algorithm in a well-known toolbox, QuTiP, our method can take 26.3% time and less memory under fair conditions.

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Online-Learning-Based Distributionally Robust Motion control with Collision Avoidance for Mobile Robots

Online-Learning-Based Distributionally Robust Motion Control...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Han Wang Chao Ning Longyan Li Weidong Zhang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Collision-free navigation is a critical issue in robotic systems as the environment is often dynamic and uncertain. This paper investigates a data-stream-driven motion control problem for mobile robots to avoid randomly moving obstacles when the probability distribution of the obstacle’s movement is partially observable through data and can be even time-varying. A data-stream-driven ambiguity set is firstly constructed from movement data by leveraging a Dirichlet process mixture model and is updated online using real-time data. Then we propose an Online-Learning-based Distributionally Robust Nonlinear Model Predictive control (OL-DR-NMPC) approach for limiting the risk of collision through considering the worst-case distribution within the ambiguity set. To facilitate solving the OL-DR-NMPC problem, we reformulate it as a finite-dimensional nonlinear optimization problem. To cope with the bilinear matrix inequality constraints in the nonlinear problem, we develop a parabolic relaxation and a sequential algorithm, by which the problem is further transformed into polynomial-time solvable surrogates. The simulations using a quadrotor model are employed to demonstrate the effectiveness and advantages of the proposed method.

关键词： Navigation Simulation Real-time systems Probability distribution Safety Mobile robots Collision avoidance

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Distributed Fixed-Time Algorithms for Time-Varying Constrained Optimization Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Xing Li, Yue Zhang, Meng Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed algorithms possess better privacy and scalability. This paper initially proposes a centralized time-varying optimisation algorithm with fixed-time convergence and certain robustness, which is based on the integration-enhanced zeroing neural network. Subsequently, the algorithm is enhanced, and two distributed algorithms are designed separately. Both of these two algorithms have a fixed convergence time and certain robustness. Additionally, this paper utilizes the penalty function approach to handle time-varying optimization problems with inequality constraints, thereby making the algorithm more widely applicable. The effectiveness of the algorithm is verified through several numerical examples, and the applicability of the algorithm is demonstrated by solving the package-level state-of-charge balancing problem. © 2020 IEEE.

关键词： Optimization algorithms

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