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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation

arXiv

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arXiv 2023年

作者： Liu, Jiang Dai, Wei The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The Artificial Intelligence Research Institute China University of Mining Technology Xuzhou221116 China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

Given the prevalence of rolling bearing fault diagnosis as a practical issue across various working conditions, the limited availability of samples compounds the challenge. Additionally, the complexity of the external environment and the structure of rolling bearings often manifests faults characterized by randomness and fuzziness, hindering the effective extraction of fault characteristics and restricting the accuracy of fault diagnosis. To overcome these problems, this paper presents a novel approach termed constructive Incremental learning-based ensemble domain adaptation (CIL-EDA) approach. Specifically, it is implemented on stochastic configuration networks (SCN) to constructively improve its adaptive performance in multi-domains. Concretely, a cloud feature extraction method is employed in conjunction with wavelet packet decomposition (WPD) to capture the uncertainty of fault information from multiple resolution aspects. Subsequently, constructive Incremental learning-based domain adaptation (CIL-DA) is firstly developed to enhance the cross-domain learning capability of each hidden node through domain matching and construct a robust fault classifier by leveraging limited labeled data from both target and source domains. Finally, fault diagnosis results are obtained by a majority voting of CIL-EDA which integrates CIL-DA and parallel ensemble learning. Experimental results demonstrate that our CIL-DA outperforms several domain adaptation methods and CIL-EDA consistently outperforms state-of-art fault diagnosis methods in few-shot scenarios. © 2023, CC BY-NC-SA.

关键词： Roller bearings

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Existence and Completeness of Bounded Disturbance Observers: A Set-Membership Viewpoint

arXiv

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arXiv 2023年

作者： Li, Yudong Cong, Yirui Dong, Jiuxiang The State Key Laboratory of Synthetical Automation of Process Industries Northeastern University China The College of Information Science and Engineering Northeastern University China The Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education of China Northeastern University China The College of Intelligence Science and Technology National University of Defense Technology China

This paper investigates the boundedness of the Disturbance Observer (DO) for linear discrete-time systems. In contrast to previous studies that focus on analyzing and/or designing observer gains, our analysis and synthesis approach is based on a set-membership viewpoint. From this viewpoint, a necessary and sufficient existence condition of bounded DOs is first established, which can be easily verified. Furthermore, a set-membership filter-based DO is developed, and its completeness is proved;thus, our proposed DO is bounded if and only if bounded DOs exist. We also prove that the proposed DO has the capability to achieve the worst-case optimality, which can provide a benchmark for the design of DOs. Finally, numerical simulations are performed to corroborate the effectiveness of the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital control systems

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Incipient Sensor Fault Detection by Directly Monitoring Sliding Window Based Singular Values*

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IFAC-PapersOnLine 2022年第6期55卷 637-642页

作者： Wenqing Zhao Hao Luo Qiang Liu Hongquan Ji Nan Sheng College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao 266590 P. R. China Department of Control Science and Engineering Harbin Institute of Technology Harbin 150001 P. R. China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 P. R. China

Timely and accurate detection of incipient faults is critical to guarantee the normal operation of industrial processes. Nowadays, complex systems are usually equipped with a large number of sensors, which may be vulnerable to faults due to harsh environments. Statistical process monitoring is commonly used for fault detection purpose. Nevertheless, traditional fault detection methods are not sensitive enough to incipient faults, leading to the occurrence of many missed alarms. In this paper, the incipient fault detection task is achieved by monitoring the changes of sample singular values within a sliding window. Two incipient sensor fault types are considered, i.e. the sensor constant bias fault and sensor precision degradation fault. In addition, the rationale behind this strategy is also theoretically analyzed. Finally, a numerical example and the continuous stirred tank reactor process demonstrate the effectiveness of the proposed method.

关键词： Incipient sensor fault fault detection constant bias precision degradation singular value

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Asynchronous multi-rate systems of a transfer learning filtering algorithm using t-distribution

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Kongzhi Lilun Yu Yingyong/control Theory and Applications 2025年第5期42卷 947-954页

作者： Wang, Wei Zhao, Shun-Yi Zhang, Cheng-Xi Luan, Xiao-Li Liu, Fei Wu, Jin Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Institute of Automation Jiangnan University Jiangsu Wuxi214122 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology 999077 Hong Kong

This paper investigates a transfer learning filtering algorithm based on the t-distribution to address the problem of state estimation in asynchronous multi-rate sensor systems affected by outliers. By integrating full probability design and multi-scale system theory, we propose a novel transfer learning framework for asynchronous multi-rate systems. A multi-scale model is established to convert the asynchronous multi-rate system into a synchronous one. This design minimizes the Kullback-Leibler divergence between the predicted distribution in the source domain and the ideal distribution in the target domain, while allowing the sensor sampling rate ratio to be any positive integer. To account for outlier effects on state estimation, the source and target domains leverage the heavy-tailed properties of the t-distribution to model state and observation processes, with approximate estimation achieved through expectation-maximization and variational Bayesian methods. Simulation results on a planar position-velocity system demonstrate the superior performance of the proposed method. © 2025 South China University of Technology. All rights reserved.

关键词： Maximum likelihood estimation

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Nuclear Norm Subspace System Identification and Its Application on a Stochastic Model of Plague

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Journal of Systems Science & Complexity 2020年第1期33卷 43-60页

作者： YU Miao LIU Jianchang ZHAO Lichun College of Information Science and Engineering Northeastern UniversityShenyang 110819China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China School of Control Engineering Northeastern University at QinhuangdaoQinhuangdao 066004China School of Mathematics and Information Science Anshan Normal UniversityAnshan 114007China

The discrete-time model of plague is deduced by zero-order holder based on the continuoustime *** to the existence of stochastic disturbances,the stochastic model is given corresponding to the discrete-time *** state estimation and noise reduction of the stochastic model are achieved by designing Kalman *** norm minimization is to structure the low-rank matrix approximation instead of the singular value decomposition in the process of subspace system *** to the plague data from the World Health Organization,the system matrices and noise intensity of the model are *** are carried out to show the higher approximation capability of the proposed method.

关键词： Kalman filter nuclear norm plague stochastic model subspace identification

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

引用

IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Hexapod Robot Gait Switching Based on Different Wild Terrains

Hexapod Robot Gait Switching Based on Different Wild Terrain...

引用

Data Driven control and Learning Systems (DDCLS)

作者： Shijie Fei Yiyang Chen Hongfeng Tao Hongtian Chen School of Mechanical and Electrical Engineering Soochow University Suzhou P. R. China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi P. R. China Department of Automation Shanghai Jiao Tong University Shanghai China

Hexapod robots have become an indispensable part of legged robots due to their excellent stability and traversability, and have recently become a hot research topic. In complex outdoor environments, the ability of the hexapod robot to switch its motion strategy according to the terrain is crucial for improving its mobility. This paper presents a terrain recognition network that enables our hexapod robot to switch its forward gait based on the different types of outdoor terrain. This method provides better stability for the hexapod robot than traditional single-gait walking through different terrains.

关键词：

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Two Feature Fusion Network Based on Efficient Deep Residual Shrinkage Spatial-Temporal Graph Convolution for Emotion Recognition from Gaits

Two Feature Fusion Network Based on Efficient Deep Residual ...

引用

第34届中国控制与决策会议

作者： Xiaoqing Liu Sen Zhang Wendong Xiao School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Shunde Graduate School University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging

Emotion Recognition plays a key role in human social *** current methods choose facial information for emotion *** facial information is easily limited by distance in large *** information can break this limitation,but the amount of publicly available data is *** paper proposes two feature fusion network based on efficient deep residual shrinkage spatial-temporal graph convolution to achieve automatic emotion recognition from gaits in large *** chebyshev polynomial approximation graph convolution is combined with efficient deep residual shrinkage network to obtain efficient deep residual shrinkage spatial-temporal graph convolution network to extract deep *** sixteen temporal affective features extracted from gaits are used as input of efficient deep residual shrinkage network to extract new affective *** the deep features is spliced with new affective features to divided emotion into happy,angry,sad and *** performed a ten-flod cross-validation experiment on the E-gait real *** highest recognition accuracy is 0.87,with an average of *** results show that the proposed model is effective in dealing with emotion recognition promblem from gait in fewer sample situation.

关键词： emotion recognition from gaits chebyshev polynomial approximation deep residual shrinkage Network efficient channel attention mechanism

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