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Distributed generalized Nash equilibrium seeking:event-triggered coding-decoding-based secure communication

Science China(Information Sciences) 2024年第7期67卷 271-284页

作者： Shaofu YANG Wenying XU Wangli HE Jinde CAO School of Computer Science and Engineering Southeast University School of Mathematics Southeast University Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology

In this paper, we consider the distributed generalized Nash equilibrium(GNE) seeking problem in strongly monotone games. The transmission among players is implemented through a digital communication network with limited bandwidth. For improving communication efficiency or/and security, an event-triggered coding-decoding-based communication is first proposed, where the data(decision variable) are first mapped to a series of finite-level codewords and, only when an event condition is satisfied, then sent to the neighboring agents. Moreover, a distributed communication-efficient GNE seeking algorithm is constructed accordingly,and the overrelaxation scheme is further taken into consideration. Through primal-dual analysis, the proposed algorithm is proven to converge to a variational GNE with fixed step-sizes by recasting it as an inexact forward-backward iteration. Finally, numerical simulations illustrate the benefit of the proposed algorithms in terms of saving communication resources.

关键词： noncooperative games event-triggered communication coding-decoding quantization

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Remaining Useful Life Prediction of Multi-Component System Clustering with Time-Varying Kernel Density

Remaining Useful Life Prediction of Multi-Component System C...

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2023 China Automation Congress, CAC 2023

作者： Li, Ke Fan, Xingle Shi, Hui Zhang, Xiaohong Sun, Chaoli Wang, Jianan Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Electronic and Information Engineering Taiyuan China Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Economics and Management Taiyuan China Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Computer Science and Technology Taiyuan China

ISBN: (纸本)9798350303759

Aiming at the uncertainty of the degradation procedure of a complex multi-component system in a time-varying environment and the influence of the random dependence between different components, a remaining useful life prediction model for time-varying adaptive kernel density estimation based on the dependence between multiple components is established. First, the random dependence between multi-component systems is clustered. Considering that different components in the same cluster are affected by the random dependence, the components between different clusters do not affect each other. Judge the dependence characteristics between components, and use the dependence characteristics of different components to conduct clustering degradation modelling. Secondly, the parameters of the system change with time. Considering that monitoring data near the current time has a greater impact on the remaining useful life prediction compared with historical data, data in the vicinity of the current time is accorded a more significant weight, and a time-varying kernel density estimation prediction method is designed, in which the nearest-neighbour adaptive window-width selection method is used. Finally, give an example to demonstrate the effectiveness by the proposed method. © 2023 IEEE.

关键词： Forecasting

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Distributed Bilevel Optimization via Adaptive Penalization with Time-Scale Separation

arXiv

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arXiv 2024年

作者： Niu, Youcheng Xu, Jinming Sun, Ying Chai, Li Chen, Jiming State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Electrical Engineering and Computer Science Pennsylvania State University PA16802 United States

This paper studies a class of distributed bilevel optimization (DBO) problems with a coupled inner-level subproblem. Existing approaches typically rely on hypergradient estimations involving computationally expensive Hessian information. To address this, we propose an equivalent constrained reformulation by treating the inner-level subproblem as an inequality constraint, and introduce an adaptive penalty function to properly penalize both inequality and consensus constraints based on subproblem properties. Moreover, we propose a loopless distributed algorithm, AHEAD, that employs multiple-timescale updates to solve each subproblem asymptotically without requiring Hessian information. Theoretically, we establish convergence κ4 rates of O((1−ρ)2K1/3 ) for nonconvex-strongly-convex cases and κ2 O((1−ρ)2K2/3 ) for distributed min-max problems. Our analysis shows the clear dependence of convergence performance on bilevel heterogeneity, the adaptive penalty parameter, and network connectivity, with a weaker assumption on heterogeneity requiring only bounded first-order heterogeneity at the optimum. Numerical experiments validate our theoretical findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration

Autonomous Implicit Indoor Scene Reconstruction with Frontie...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jing Zeng Yanxu Li Jiahao Sun Qi Ye Yunlong Ran Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China College of Control Science and Engineering the State Key Laboratory of Industrial Control Technology Zhejiang University and the Key Key Lab of CS&AUS of Zhejiang Province

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Implicit neural representations have demonstrated significant promise for 3D scene reconstruction. Recent works have extended their applications to autonomous implicit reconstruction through the Next Best View (NBV) based method. However, the NBV method cannot guarantee complete scene coverage and often necessitates extensive viewpoint sampling, particularly in complex scenes. In the paper, we propose to 1) incorporate frontier-based exploration tasks for global coverage with implicit surface uncertainty-based reconstruction tasks to achieve high-quality reconstruction. and 2) introduce a method to achieve implicit surface uncertainty using color uncertainty, which reduces the time needed for view selection. Further with these two tasks, we propose an adaptive strategy for switching modes in view path planning, to reduce time and maintain superior reconstruction quality. Our method exhibits the highest reconstruction quality among all planning methods and superior planning efficiency in methods involving reconstruction tasks. We deploy our method on a UAV and the results show that our method can plan multi-task views and reconstruct a scene with high quality.

关键词： Surface reconstruction Uncertainty Three-dimensional displays Pose estimation Switches Path planning Planning

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Robust Nonlinear Self-Triggered control Policy for a Novel Fully Actuated UAVs

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IFAC-PapersOnLine 2023年第2期56卷 2506-2512页

作者： Zhangzhen Zhu Yongliang Lin Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

This paper proposes a self-triggered control policy for a general kind of nonlinear system, ensuring robust performance under various perturbations and alleviating communication burden simultaneously. A novel fully actuated multirotors applied this method shows great robustness to perturbations and is able of tracking six dimensional decoupled trajectories without continuous background sensor monitoring. Rigorous input-to-state stability analysis is deduced and numerical simulations confirm the validity of the policy.

关键词： UAVs fully actuated event-triggered robustness nonlinear systems

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Multi-condition modeling and optimal control of boiler based on improved FCM clustering algorithm

Multi-condition modeling and optimal control of boiler based...

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Chinese control and Decision Conference, CCDC

作者： Heping Xia Xinyu Zhang Zhongyang Han Jun Zhao Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China School of Control Science and Engineering Dalian China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Boiler is the main equipment of coal-fired power plant, which has complex and variable characteristics of multiple operating conditions due to different loads in daily production. The overall operational efficiency of the plant is directly affected by its control level, especially for the accommodation of the different operating conditions. In this paper, the improved fuzzy C-mean clustering algorithm is proposed to realize the classification of boiler conditions by extracting the features of boiler combustion history data and modeling each sub-condition using the prediction error method at first. Then a multi-model predictive control method based on T-S fuzzy theory is designed to control key variables such as active power in the boiler. In experimental study, real data is used to comparing the proposed with other commonly used methods, such as the conventional Fuzzy C-means clustering algorithm. The results show that the method proposed in this paper has obvious advantages in boiler modeling accuracy and control performance, which verifies the reasonableness for boiler condition identification as well as the practicability for real-world application.

关键词： Analytical models Clustering algorithms Production Boilers Prediction algorithms Feature extraction Data models

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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

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computer Modeling in engineering & Sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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Deep Reinforcement Learning or Lyapunov Analysis?A Preliminary Comparative Study on Event-Triggered Optimal control

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1702-1704页

作者： Jingwei Lu Lefei Li Qinglai Wei Fei-Yue Wang IEEE the Department of Industrial Engineering Tsinghua UniversityBeijing 100084China The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter develops a novel method to implement event-triggered optimal control(ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning(DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions.

关键词： Deep letter enable

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An Approach for Performance Calculation of AVS/RS in Multi-floor Workshop 3rd

An Approach for Performance Calculation of AVS/RS in Multi-f...

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3rd International Conference on Smart Manufacturing, industrial and Logistics engineering, SMILE 2023 and the 7th Asian Conference of Management Science and Applications, ACMSA 2023

作者： Liu, Xiaopeng Wu, Cuiying Zhang, Baolin Xi, An Chen, Xuanrui Ministry of Industry and Information Technology Key Laboratory of Industrial Software Engineering Application Technology China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou510619 China School of Computer and Information Engineering Hanshan Normal University Guangdong Chaozhou521041 China

ISBN: (纸本)9789819701933

Warehouse management is an important part of the production process. As a result of the development of technology, more and more companies tend to use automated logistics technologies such as AVS/RS to handle the internal logistics of the production system. Especially in multi-floor workshops, the production process on each floor are connected with each other through AVS/RS. It is becoming more and more important to study the integration process of AVS/RS and multi-floor workshop production process. In this paper, an integrated queuing network model of multi-floor workshop and AVS/RS was established, and a calculation method of system performance index was proposed. The accuracy and efficiency of the proposed method are demonstrated by comparing the results with simulations from numerical experiments. The work of this article can provide a basis for the design of autonomous vehicle storage and retrieve systems in multi-floor workshop, and the calculation method can be used as the support of WMS technology base. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Floors

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Masked Visual-Tactile Pre-training for Robot Manipulation

Masked Visual-Tactile Pre-training for Robot Manipulation

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Qingtao Liu Qi Ye Zhengnan Sun Yu Cui Gaofeng Li Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology Zhejiang University Key Key Lab of CS&AUS of Zhejiang Province

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recent works on the pretraining for robot manipulation have demonstrated that representations learning from large human manipulation data can generalize well to new manipulation tasks and environments. However, these approaches mainly focus on human vision or natural language, neglecting tactile feedback. In this article, we make an attempt to explore how to pre-train a representation model for robotic manipulation using both human manipulation visual and tactile data. We develop a system for collecting visual and tactile data, featuring a cost-effective tactile glove to capture human tactile data and Hololens2 for capturing visual data. With this system, we collect a dataset of turning bottle caps. Furthermore, we introduce a novel visual-tactile fusion network and learning strategy M 2 VTP, with one key module to tokenize 20 sparse binary tactile signals sensing touch states for the learning of tactile context and the other key module applying the attention and mask mechanism to the interaction of visual and tactile tokens for visual-tactile representation learning. We utilize our dataset to pre-train the fusion model and embed the pre-trained model into a reinforcement learning framework for downstream tasks. Experimental results demonstrate that our pre-trained model significantly aids in learning manipulation skills. Compared to methods without pre-training, our approach achieves a success rate increase of over 60%. Additionally, when compared to current visual pre-training methods, our success rate exceeds them by more than 50%.

关键词： Representation learning Visualization Natural languages Tactile sensors Reinforcement learning Turning Data models

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