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42nd Chinese control Conference, CCC 2023

作者： Chen, Yining School of Artificial Intelligence Tiangong University Tianjin300387 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai639798 China

ISBN: (纸本)9789887581543

This paper mainly solves the containment control issue for a network of infinite-dimensional agents described by wave equations. To eliminate influences caused by external disturbances, observers based on partial differential equations are proposed. A novel interaction protocol is then designed to reach containment control under a fixed directed communication topology. Different from the existing works, disturbances with unbounded derivatives are allowed in this paper. A proper Lyapunov function is constructed and the sufficient condition assuring containment control is obtained. Finally, the theoretical results are verified via numerical examples. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Lyapunov functions

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Data-Driven Distributionally Robust Planning of Energy Hub Coupled with Carbon Capture Utilization and Storage Facilities 42

Data-Driven Distributionally Robust Planning of Energy Hub C...

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42nd Chinese control Conference, CCC 2023

作者： Ma, Xutao Ning, Chao Shanghai Jiao Tong University Department of Automation Shanghai200240 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9789887581543

Toward carbon peaking and carbon neutrality goals in China, a transition from traditional fossil power to renewable energy becomes an imperative need. This paper aims to address the planning problem of a highly renewable-energy penetrated energy hub system, coupled with carbon capture facilities and downstream production processes of hydrogen and methanol, under the uncertainty of renewable power output. To this end, we propose a two-stage distributionally robust optimal planning model, where investment decisions are the first-stage variables and operation variables are determined at the second stage after the realization of uncertainty. In the proposed model, we utilize historical data samples to construct a strengthened ambiguity set that leverages both Wasserstein distance and moment information for a better characterization of renewable generation uncertainty. Moreover, to efficiently solve the proposed planning problem, we develop a simplified affine decision rule method in a general setting with both moment and Wasserstein ambiguity, and then apply it to reformulate the distributionally robust planning problem. The derived reformulation possesses the favorable property that its scale is free from the sample size, thus greatly reducing the computational complexity. Computational results validate the effectiveness of the proposed planning model and the computational efficiency of the solution methodology. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Carbon capture

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Streaming-Data-Driven Multi-Objective Optimal Operation for Ammonia-based Integrated Energy Chemical systems: An Event-Triggered Scheme 42

Streaming-Data-Driven Multi-Objective Optimal Operation for ...

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42nd Chinese control Conference, CCC 2023

作者： Li, Longyan Ning, Chao Shanghai Jiao Tong University Department of Automation Shanghai200240 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9789887581543

As an important component in the industry sector, the integrated energy chemical system is inescapably responsible for carbon reduction in addition to economic and reliability performance. We propose a novel streaming-data-driven multi-objective optimal operation for ammonia-based integrated energy chemical systems (AIECS). In the day ahead scheduling, a detailed mathematical model for AIECS is presented, upon which a multi-objective optimization considering cost, CO2 emission and reliability is established. For the intra-day operation, an event-triggered mechanism is firstly developed in the real-time operation of AIECS, and it enables the AIECS to be risk-aware just in time. By monitoring the difference between the predicted state of charge and the capacity limitations of battery, the AIECS identifies whether it is in hazardous regime. Then, the intelligent switch between certainty equivalence model predictive control and online-learning based distributionally robust model predictive control is proposed, such that their respective advantages are leveraged. Case studies demonstrate that the proposed approach guarantees the system safety, and brings good revenue. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Model predictive control

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Adaptive Dynamic Programming and Decentralized Optimal Output Regulation of Two-Time-Scale Interconnected systems 12

Adaptive Dynamic Programming and Decentralized Optimal Outpu...

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12th IEEE International Conference on control, Automation and information Sciences, ICCAIS 2023

作者： Zhao, Jianguo Yang, Chunyu Zhou, Linna Gao, Weinan China University of Mining and Technology School of Information and Control Engineering Xuzhou China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang China

ISBN: (纸本)9798350328783

In this paper, we address the decentralized optimal output regulation problem for two-time-scale (TTS) interconnected systems with unknown slow dynamics through adaptive dynamic programming (ADP). Firstly, singular perturbation theory is leveraged to decompose the TTS interconnected system into a slow subsystem and several interconnected fast time-scale subsystems. Secondly, we develop a decentralized optimal control scheme for the fast time-scale subsystems with interconnections. Also, we devise an off-policy ADP algorithm to learn the optimal control strategy for the slow time-scale subsystem with unknown dynamics. Moreover, we combine the slow and fast control strategies to design the composite decentralized optimal output regulator. Both optimality and stability of the closed-loop system are rigorously analyzed. Finally, simulation results are provided to validate the effectiveness of the proposed methodology. © 2023 IEEE.

关键词： Dynamic programming

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Fault Diagnosis Method Based on CND-SMOTE and BA-SVM Algorithm 2

Fault Diagnosis Method Based on CND-SMOTE and BA-SVM Algorit...

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2022 2nd International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2022

作者： Wang, Sheng Ma, Liling Wang, Junzheng Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing100081 China

The problem of unbalanced data classification has gotten extensive attention in the past few years. Unbalanced sample data makes the fault diagnosis and classification accuracy rate low, and the capability to classify minority-class fault samples is restricted. To address the problem that the classification algorithm in machine learning has the insufficient capability to identify minority class samples for unbalanced sample data classification problems. Therefore, this paper proposes an improved support vector machine (SVM) classification method based on the synthetic minority over-sampling technique (SMOTE). For the sampler, an improved synthetic minority over-sampling technique based on the characteristics of neighborhood distribution (CND-SMOTE) algorithm is used to equilibrate the minority class samples and the majority class samples. For the classifier, the parameter optimization method of support vector machines based on the bat algorithm (BA-SVM) is used to solve the multi-classification problem of faulty samples. Finally, experimental results prove that the CND-SMOTE+BA-SVM algorithm can synthesize high-quality minority fault samples, increase the classification accuracy rate of fault samples, and decrease the time spent on the classification. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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A Novel FL-AoI-Based control and Event-Triggered Strategy Integrating Network Characteristics for ICPS 49

A Novel FL-AoI-Based Control and Event-Triggered Strategy In...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Lu, Xuanzhao Xu, Qimin Zhang, Jinglong Lin, Meihan Chen, Cailian Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China China

ISBN: (纸本)9798350331820

The joint design of control and transmission has been demonstrated to be a successful technique for enhancing the performance of industrial cyber-physical systems (ICPS). In the majority of existing works, the control cost and the transmission cost are defined independently, followed by a weighted total calculation. This approach suffers from a dimension consistency issue, leading the results to diverge from the system's actual optimal performance. Hence, it is necessary to consider the overall information of the loop and characterize the coupling relationship between control and transmission to construct the overall system performance function. This paper proposes a full loop age of information (FL-AoI) based control and transmission joint design architecture for multi-subsystem ICPS integrating multi-hop network. The state delay, input delay, and event trigger are all taken into consideration by FL-AoI to more fully portray the freshness of the information. We provide a novel control performance based on FL-AoI where the network characteristics are incorporated into the control cost, which could tackle the dimensionality mismatch brought by the form of weighted summation of the control and transmission cost. We also provide the FL-AoI-based strategy for the controller and event-triggering mechanism and derive the cost's boundary. The evaluation results demonstrate that, in comparison to the conventional joint design strategy, our solution increases the stability of the control system while decreasing the network burden. © 2023 IEEE.

关键词： Cyber Physical system

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Novel £2,1-Norm Based Least Square Regression Method for Classification 9

Novel £2,1-Norm Based Least Square Regression Method for Cl...

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9th International Conference on information, Cybernetics, and Computational Social systems, ICCSS 2023

作者： Jin, Junwei Yan, Qiuling Zhu, Chunhua Qin, Zhenghao Li, Lei Henan University of Technology Ministry of Education Key Laboratory of Grain Information Processing and Control China Henan Key Laboratory of Grain Photoelectric Detection and Control China Henan University of Technology College of Information Science and Engineering China Unmanned System Research Institute Northwestern Polytechnical University China

ISBN: (纸本)9798350342239

In the realm of linear regression, the concept of utilizing relaxed regression targets for classification has shown considerable success. However, it suffers from a lack of strong discriminative ability. In this study, we build upon the theory of relaxed regression targets and propose a more concise and discriminative model for multi-class classification. Our approach introduces the £2,1-norm regularized term to enhance the efficacy and compactness of the learned projection matrix. This regularization term not only generates a sparse row structure but also facilitates feature selection during the training phase. Consequently, both the accuracy of classification and the convergence speed of the model are improved. Furthermore, when compared to various variants of the linear regression model, our designed method exhibits superior performance in image classification. Through extensive experiments conducted on image databases, we provide evidence that our proposed approach excels in recognition capability, surpassing the performance of state-of-The-Art methods in terms of classification accuracy. © 2023 IEEE.

关键词： Classification (of information)

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A Ground-Based Cloud Motion Prediction Method 42

A Ground-Based Cloud Motion Prediction Method

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42nd Chinese control Conference, CCC 2023

作者： Lu, Zhiying Dong, Yixian Nie, Bing Cao, Xinlei Zhang, Jianfeng Key Laboratory of Smart Grid of Tianjin University Tianjin300072 China Tianjin Key Laboratory of Process Testing and Control Tianjin300072 China School of Information Science and Engineering Shandong Normal University Jinan250000 China

ISBN: (纸本)9789887581543

Cloud conditions play a significant role in affecting the output power of solar photovoltaic (PV) panels, and ground-based cloud images can provide valuable data for cloud monitoring and ultra-short-term PV power forecasting. From the perspective of image sequence prediction, a cloud motion prediction method based on Cascade Causal LSTM is proposed by using the ground-based cloud image data taken by Total Sky Imager, realizing the prediction of cloud contour change. On this basis, digital image processing technology is used for features extraction from the ground-based cloud images, including clear sky irradiance, clusters, color statistics, and texture that affect PV power generation. Finally, the ultra-short-term PV power forecasting model with multi-information fusion is established. Experimental results demonstrate that the proposed PV power prediction method can effectively improve the accuracy of ultra-short-term PV power forecasting. It is further shown that the effectiveness of the proposed cloud motion prediction method. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Extraction

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Network system capacity:Towards integrating sensing,communication and control

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National Science Open 2024年第1期3卷 147-149页

作者： Xinping Guan Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China

Network systems refer to a new generation of systems with integrated information perception,transmission and utilization capabilities through communication networks,which are adopted to achieve desirable objectives under physical and information related uncertainties and/or adversary *** the information-rich era[1,2],one of the fundamental issues is to exploit the limit of feedback control on dissipating such uncertainties in the scenarios of networked sensing and communication[3].

关键词： communication integrating system

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Fault diagnosis for small samples based on VMR

Fault diagnosis for small samples based on VMR

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2024 China Automation Congress, CAC 2024

作者： Wu, Xuejin Wu, Yali Hu, Yanghu Department of Information and Control Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China State Key Laboratoiy of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an710016 China

ISBN: (纸本)9798350368604

The scarcity of fault samples presents a significant challenge for fault diagnosis in industrial processes. Conventional methods often fail to deliver satisfactory results when dealing with small sample sizes. In addressing the issue of small sample fault diagnosis in industrial processes, this paper utilizes the Variational Mode Reconstruction(VMR) algorithm to screen the best components decomposed by the Variational Mode Decomposition(VMD) through the envelope entropy then random weighting them and signal reconstruction to achieve data enhancement, to enrich the sample size and to prevent the model overfitting phenomenon due to the scarcity of samples. Meanwhile, this paper integrates Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) to propose a network model of spatio-temporal feature fusion for feature extraction and classification. The experiments demonstrate that this model not only enhances data diversity but also ensures consistency before and after enhancement, effectively mitigating overfitting. Furthermore, it guarantees high recognition and generalization rates in fault diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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