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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 893-906页

作者： Zhipeng Chen Xinyi Wang Weihua Gui Jilin Zhu Chunhua Yang Zhaohui Jiang the authors are with the school of automation Central South UniversityChangsha 410083China

Blast furnace(BF)burden surface contains the most abundant,intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control,optimize gas flow distribution and improve ironmaking *** has been challengeable to obtain high-quality optical burden surface images under high-temperature,high-dust,and extremelydim(less than 0.001 Lux)*** on a novel endoscopic sensing detection idea,a reverse telephoto structure starlight imaging system with large field of view and large aperture is *** with a water-air dual cooling intelligent self-maintenance protection device and the imaging system,a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh *** on an endoscope imaging area model,a material flow trajectory model and a gas-dust coupling distribution model,an optimal installation position and posture configuration method for the endoscope is proposed,which maximizes the effective imaging area and ensures large-area,safe and stable imaging of the device in a confined *** experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions,providing key data support for green iron smelting.

关键词： Blast furnace(BF) burden surface extremely dim light industrial endoscope sensing imaging starlight

Adaptive Consensus of Uncertain Multi-Agent Systems With Unified Prescribed Performance

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1310-1312页

作者： Kun Li Kai Zhao Yongduan Song the authors are with the school of automation Chongqing UniversityChongqing 400044China

Dear Editor,An adaptive consensus control algorithm for uncertain multi-agent systems(MAS),capable of guaranteeing unified prescribed performance,is presented in this *** many existing prescribed performance related works,the developed control exhibits some ***,a distributed prescribed time observer is introduced so that not only each follower is able to estimate the leader’s signal within a predetermined time,but also the control design for each agent is independent with its neighbors.

关键词： agent prescribed Uncertain

Output feedback stabilization of stochastic high-order planar nonlinear systems with stochastic inverse dynamics and output-constraint

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Science China(Information Sciences) 2024年第3期67卷 182-196页

作者： Ruiming XIE Shengyuan XU Nanjing University of Science and Technology school of automation

In this paper, we solve the output feedback control problem of stochastic high-order planar nonlinear systems with output constraint and stochastic integral input-to-state stability(SiISS) inverse dynamics. By employing a key coordinate transformation, a stochastic nonlinear system with output constraint and SiISS inverse dynamics is converted into an unconstrained system. By skillfully constructing an observer and adopting SiISS small-gain conditions, we develop a new output feedback control design and analysis method, and prove that all the closed-system signals are bounded almost surely, the output constraint is not violated almost surely, and the equilibrium point of the closed-loop system is stochastically asymptotically stable.

关键词： stochastic high-order planar nonlinear system outputconstraint stochastic inverse dynamics output feedbackstabilization stochastic integral input-to-state stability(SiISS)

Fixed-time stabilization of output-constrained stochastic high-order nonlinear systems

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Science China(Information Sciences) 2024年第4期67卷 5-20页

作者： Ruiming XIE Shengyuan XU Nanjing University of Science and Technology school of automation

In this study, the fixed-time stabilization problem of stochastic high-order nonlinear systems with output constraint and high-order and low-order nonlinearities is addressed. A new coordinate transformation is employed to directly convert output-constrained stochastic systems into an equivalent unconstrained *** fully extracting the characteristics of system nonlinearities and using the stochastic fixed-time stability theorem, a new design and analysis method is constructed to guarantee that the trivial solution of the closed-loop system is stochastically fixed-time stable while fulfilling the output constraint.

关键词： stochastic high-order nonlinear system outputconstraint high-order and low-order nonlinearity fixed-timestabilization finite-time stabilization

Logit prototype learning with active multimodal representation for robust open-set recognition

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Science China(Information Sciences) 2024年第6期67卷 297-312页

作者： Yimin FU Zhunga LIU Zicheng WANG school of automation Northwestern Polytechnical University

Robust open-set recognition(OSR) performance has become a prerequisite for pattern recognition systems in real-world applications. However, the existing OSR methods are primarily implemented on the basis of single-modal perception, and their performance is limited when single-modal data fail to provide sufficient descriptions of the objects. Although multimodal data can provide more comprehensive information than single-modal data, the learning of decision boundaries can be affected by the feature representation gap between different modalities. To effectively integrate multimodal data for robust OSR performance, we propose logit prototype learning(LPL) with active multimodal representation. In LPL, the input multimodal data are transformed into the logit space, enabling a direct exploration of intermodal correlations without the impact of scale inconsistency. Then, the fusion weights of each modality are determined using an entropybased uncertainty estimation method. This approach realizes adaptive adjustment of the fusion strategy to provide comprehensive descriptions in the presence of external disturbances. Moreover, the single-modal and multimodal representations are jointly optimized interactively to learn discriminative decision ***, a stepwise recognition rule is employed to reduce the misclassification risk and facilitate the distinction between known and unknown classes. Extensive experiments on three multimodal datasets have been done to demonstrate the effectiveness of the proposed method.

关键词： logit prototype learning multimodal perception open-set recognition uncertainty estimation

ControlService: a containerized solution for control-algorithm-as-a-service in cloud control systems

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Science China(Information Sciences) 2024年第08期67卷 172-189页

作者： Chenggang SHAN Runze GAO Zhen YANG Wei ZHANG Yuanqing XIA school of Artificial Intelligence Zaozhuang University school of automation Beijing Institute of Technology

As an extension of networked control systems, cloud control systems(CCSs) have emerged as a new control paradigm to improve the service quality of emerging control missions, such as data-driven modeling and automated vehicles. Existing studies have used the workflow-based restructured method to optimize the computation-intensive algorithms in the CCSs. However, the challenges here are how to define and submit these algorithms' workflows as cloud services and execute these algorithms' workflows in a containerized manner. Based on these challenges, we propose a containerized solution for the control-algorithm-as-a-service(C3aS) in the CCSs, namely Control Service. It offers the control algorithm as a cloud workflow service and uses a customized workflow engine to realize the containerized execution. First, we employ a cloud workflow representation method to define a control algorithm into an abstract cloud workflow form. Afterward, we provide a cloud service representation of the abstract cloud workflow. Next, we design a workflow engine and submit the cloud service to this workflow engine to implement containerized execution of this cloud service in the CCSs. In the experiment, we discuss the cloud service form and containerized implementation of the subspace identification method. Experimental results show that the proposed Control Service has significant performance advantages in computational time, reduction percentage, and speedup ratio compared with the baseline method.

关键词： cloud control systems control-algorithm-as-a-service containerization workflow engine cloud workflow service subspace identification

Spiking neural networks in intelligent control systems:a perspective

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Science China(Information Sciences) 2024年第7期67卷 324-326页

作者： Anguo ZHANG Yongduan SONG school of Artificial Intelligence Anhui University school of automation Chongqing University

Artificial intelligence (AI) has undergone substantial advancements in recent years, prompting an era of innovation in various domains. Among the groundbreaking contributions, neural networks stand at the forefront, powering applications ranging from computer vision and natural language processing to intelligent control systems [1, 2].

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Deterministic Learning-Based Neural PID Control for Nonlinear Robotic Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1227-1238页

作者： Qinchen Yang Fukai Zhang Cong Wang The authors are with the school of Control Science and Engineering Shandong UniversityJinan 250061China

Traditional proportional-integral-derivative(PID)controllers have achieved widespread success in industrial ***,the nonlinearity and uncertainty of practical systems cannot be ignored,even though most of the existing research on PID controllers is focused on linear ***,developing a PID controller with learning ability is of great significance for complex nonlinear *** article proposes a deterministic learning-based advanced PID controller for robot manipulator systems with *** introduction of neural networks(NNs)overcomes the upper limit of the traditional PID feedback mechanism’s *** proposed control scheme not only guarantees system stability and tracking error convergence but also provides a simple way to choose the three parameters of PID by setting the proportional *** the partial persistent excitation(PE)condition,the closed-loop system unknown dynamics of robot manipulator systems are accurately approximated by *** on the acquired knowledge from the stable control process,a learning PID controller is developed to further improve overall control performance,while overcoming the problem of repeated online weight *** studies and physical experiments demonstrate the validity and practicality of the proposed strategy discussed in this article.

关键词： PID robot system

A Novel Scalable Fault-Tolerant Control Design for DC Microgrids With Nonuniform Faults

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1886-1888页

作者： Aimin Wang Minrui Fei Dajun Du Yang Song the Shanghai Key Laboratory of Power Station automation Technology the School of Mechatronic Engineering and Automation Shanghai University

Dear Editor,The existing control schemes for microgrids (MGs) face challenges in effectively addressing plugging in/out operations under uncertain power lines and faults. To tackle this issue, this letter proposes a novel scalable fault-tolerant control (FTC) strategy for DC MGs. By developing a structured Lyapunov matrix (SLM), a decoupled FTC method is introduced to mitigate the adverse effects of uncertain lines and nonuniform faults.

关键词： Linear matrix inequalities

Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 719-729页

作者： Qing Ma Changhong Deng the school of Electrical Engineering and automation Wuhan UniversityWuhanChina

Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity *** existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables,which in turn increases the optimization difficulty or even leads to optimization *** paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF).This method uses the input convex neural network(ICNN)to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment,which can ensure the global optimum with extremely fast computation *** cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC,this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF),which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage *** DLIPF with DLPF,this method decreases the modeling and optimization difficulty of robust VVC *** results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.

关键词： Volt-var control convexity conversion convex deep learning power flow