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第40届中国控制会议

作者： Qian Liu Lijun Long Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Institute of Artificial Intelligence Faculty of Information TechnologyBeijing University of Technology College of Information Science and Engineering Northeastern University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

This paper provides some sufficient conditions to achieve asymptotic stability of switched systems under spatiotemporal specifications via a state-dependent switching law. A common finite-time barrier function, multiple barrier functions and multiple Lyapunov functions are merged as a tool for analyzing the asymptotic stability of a switched nonlinear system under spatio-temporal specifications. Finally, a numerical example is presented to demonstrate the effectiveness of our proposed method.

关键词： Switched systems Common finite-time barrier function Multiple barrier functions Multiple Lyapunov functions State-dependent switching law

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Global Asymptotic Stability Analysis for Autonomous Optimization

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IEEE Transactions on Automatic control 2025年

作者： Jin, Zhenghong Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore

This paper studies the feedback optimization problem for nonlinear systems. In particular, we assume that the controlled plant admits a global asymptotic stable equilibrium corresponding to each fixed reference input and consider a modified gradient-flow optimizer augmented with a nonlinear perturbation function. With the output map of the controlled plant satisfying a linear growth condition, this paper proves the existence of a perturbation function such that the resulting feedback optimization system is globally asymptotically stable at the desired equilibrium. The proof is based on the seamless integration of tools from the singular perturbation theory, input-to-state stability (ISS), and the nonlinear small-gain theorem. The possibility of extending the proposed approach to other optimizers is also discussed. Three numerical examples are employed to verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Perturbation techniques

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Manifold-aware and sparsity discriminant collaborative representation model and its application to multimode process monitoring

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Knowledge-Based Systems 2025年 324卷

作者： Fu, Yuanjian Xia, Fubing Ding, Jinliang Xu, Xue School of Control Science and Engineering Tiangong University Tianjin 300387 China Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tiangong University Tianjin 300387 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China School of Artificial Intelligence Tiangong University Tianjin 300387 China

Industrial processes are generally characterized by multiple operating modes from several factors, such as changes in market demand or raw material fluctuations, which pose challenges to traditional process monitoring. Existing multimode process monitoring primarily learns the distinct characteristics in each mode without collaboratively capturing the connections encapsulated across different modes. In this study, a novel multimode process monitoring approach, called the manifold-aware and sparsity discriminant collaborative representation model is proposed, which effectively captures the collaborative representation between and among modes, providing deeper insights into multimodal processes. The between-mode and within-mode affinity matrices were designed by considering the local information of the data to characterize the correlation between modes and the uniqueness of each mode, respectively. To faithfully reveal the characteristics of multimodal processes, a shared representation matrix was constructed to learn the common information among the different modes. Furthermore, the L1/2 norm was imposed on the projection matrix, enabling the proposed approach effective in selecting the key variables for monitoring tasks. On the basis of these distinct model characterizations, the proposed approach is powerful in learning informative representations of data, further enhances the process monitoring capability. In addition, contrastive clustering was introduced to divide the historical modes and identify the running-on modes of the new samples, thereby reducing the online identification time. The monitoring results for both the Tennessee Eastman process and three-phase flow facility demonstrate the effectiveness of the proposed approach, in which fault detection rate was improved by 6 %-26 % in most cases.

关键词： Discriminant analysis Multimode process monitoring Representation learning Sparse regularization

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Preface

Lecture Notes in Control and Information Sciences

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Lecture Notes in control and Information Sciences 2023年 492卷 v-vi页

作者： Luan, Xiaoli He, Shuping Liu, Fei Key Laboratory of Advanced Process Control for Light Industry [Ministry of Education Institute of Automation Jiangnan University Jiangsu Wuxi China Key Laboratory of Intelligent Computing and Signal Processing [Ministry of Education School of Electrical Engineering and Automation Anhui University Anhui Hefei China

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A Deep Reinforcement Learning-Based Multi-Objective Charging Scheduling Method for Electric Vehicle Charging Station

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IEEE Internet of Things Journal 2025年

作者： Zhao, Qiang Wu, Wenwei Cheng, Ningyu Guo, Ge Han, Yinghua Northeastern University at Qinhuangdao School of Control Engineering Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinghuangdao066004 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China Northeastern University at Qinhuangdao School of Computer and Communication Engineering Hebei Qinhuangdao066004 China

This paper investigates the charging scheduling problem within an Electric Vehicle Charging Station (EVCS), aiming to coordinate multiple charging piles to optimize the charging process. The objectives are to increase the profit for the Charging Station Operator (CSO) and enhance user satisfaction while mitigating significant fluctuations in grid load. However, since these objectives originate from different stakeholders, they are inherently conflicting;a strategy trained using fixed objective weights cannot satisfy all of them simultaneously. Additionally, uncertainties such as vehicle arrival and departure times, power requirements, and grid tariffs complicate the modeling of this process. To address these challenges, this paper reformulates the scheduling process as a Markov Decision process (MDP) and proposes a novel model-free reinforcement learning method. By integrating randomly varying objective preferences into the inputs of agents, a set of policies corresponding to different objective preferences can be learned through a single training session. As a result, agents can execute various policies to satisfy the stakeholders’ needs based on these preferences. Moreover, a sampling adjustment strategy is introduced to improve the uniformity of the final non-dominated solution set and enhance the ability of the agent to explore the objective space. Experiments show that the proposed method can effectively solve the multi-objective problem of cooperative charging within an EVCS. Compared to the baseline method, the proposed method can achieve superior results while simultaneously reducing the computational cost to a significant extent. © 2014 IEEE.

关键词： Deep reinforcement learning

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A self-triggered control scheme for Markov jump systems under multiple range performance restrictions

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IFAC-PapersOnLine 2020年第2期53卷 2783-2788页

作者： Haiying Wan Hamid Reza Karimi Xiaoli Luan Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi214122 China Department of Mechanical Engineering Politecnico di Milano Milan Italy

This paper proposes a multi-frequency controller design scheme for Markov jump systems (MJSs) based on the self-triggered strategy in a resource-aware way. Firstly, a derandomization technique is introduced to make sure the transition probability information is included in the finite frequency specification analysis. Then, a self-triggered policy is developed to update the control input of the system via the history measurement. Finally, sufficient conditions are deduced that guarantee the multiple range frequency performances and the reduction of computation and communication occupation for the controlled MJSs, simultanously. The cart- spring system is employed to illustrate the effectiveness of the proposed approach.

关键词： Self-triggered control finite frequency performance Markov jump systems derandomization disturbance rejection

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Data driven particle size estimation of hematite grinding process using stochastic configuration network with robust technique

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Journal of Central South University 2019年第1期26卷 43-62页

作者： DAI Wei LI De-peng CHEN Qi-xin CHAI Tian-you School of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real *** achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares ***,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty *** experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and *** results show that the RSCN has an excellent performance for the PS estimation.

关键词： hematite grinding process particle size stochastic configuration network robust technique M-estimation nonparametric kernel density estimation

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Observer-Based Adaptive Finite-Time Containment control for Nonlinear Multiagent Systems Subject to FDI Attacks

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IEEE Transactions on automation Science and engineering 2025年

作者： Kang, Haobo Ma, Hongjun Niu, Ben Guangzhou University School of Mechanical and Electrical Engineering Guangdong Guangzhou510006 China Guangdong-Hong Kong-Macao Key Laboratory of Multi-scale Information Fusion and Collaborative Optimization Control of Complex Manufacturing Process Guangdong Guangzhou510006 China South China University of Technology School of Automation Science and Engineering Guangzhou510641 China Dalian University of Technology School of Control Science and Engineering Liaoning Dalian116024 China

This paper investigates the adaptive finite-time containment control problem for a class of nonlinear multiagent systems (MASs) under false data injection (FDI) attacks. On account of the fact that instability is inevitable when the false datas are injected into the researched system, the backstepping technique based on the modified coordinate transformation is applied to eliminate the impact of the FDI attacks. Furthermore, the "dynamic surface control"(DSC) approach is utilized to overcome the issue of "explosion of complexity" caused by repeatedly taking derivatives for the virtual control laws. Then, by designing an observer to refactor the immeasurable states of the MASs, a finite-time adaptive output-feedback tracking containment controller is constructed. It is shown that all the signals in the closed-loop systems are semi-globally practical finite-time stability(SGPFS), and the outputs of all the followers converge to the convex hull spanned by the multiple leaders’s outputs. Besides, the observer errors and the containment errors can converge to a small neighborhood of the origin in finite time. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed containment control protocol. © 2004-2012 IEEE.

关键词： Closed loop control systems

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Model Transformation for Asset Administration Shells

Model Transformation for Asset Administration Shells

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Annual Conference of Industrial Electronics Society

作者： Torben Miny Michael Thies Ulrich Epple Christian Diedrich Chair of Process Control RWTH Aachen University Aachen Germany Institute for Automation Engineering (IFAT) Otto-von-Guericke Universitat Magdeburg Magdeburg Germany

ISBN: (数字)9781728154145

ISBN: (纸本)9781728154152

In the scope of Industry 4.0 (I40), one goal is the standardized access to asset information and asset services using standardized submodels (submodel templates) in the Asset Administration Shell. Since submodel templates are modeled by different groups of people, the same asset information will be contained in several submodel templates. For automatic generation of new submodels based on existing information from other submodels, model transformation can be a solution. Therefore, in this contribution, we present a guideline on how to develop a new model transformation language for a given use case and apply this guideline to the concrete use case (model transformation for Asset Administration Shells). As a result, we define of the abstract syntax of a customized model transformation language called AASMTL.

关键词： Unified modeling language Object oriented modeling Syntactics Guidelines Semantics Tools Industries

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Recursive Weighted Null-Space Fitting Method for Identification of Multivariate Systems ⁎

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IFAC-PapersOnLine 2021年第7期54卷 345-350页

作者： Mengyuan Fang Miguel Galrinho Håkan Hjalmarsson Zhejiang Engineering Laboratory of Industrial Internet Communication Technology Zhejiang Sci-Tech University Hangzhou China. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China OHB Sweden AB Kista Sweden Centre for Advanced Bio Production and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

关键词： Recursive system identification multivariate systems weighted null-space fitting

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