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2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Zhao, ZiYan Liu, ShiXin Zhou, MengChu Guo, XiWang State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Northeastern University Shenyang110819 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Computer and Communication Engineering College Liaoning Shihua University Fushun113001 China

ISBN: (纸本)9781728168531

A wire rod and bar rolling process is important in steel production systems. Its scheduling problem involves the constraints on sequence-dependent family setup time and release time. This work intends to schedule the batches with multiple jobs to minimize the number of late jobs. An important characteristic of this problem is that the number of late jobs within a batch varies with its start time. Given a start time of a batch, the number of late jobs within it can be derived. Two problem-specific scatter search algorithms are developed to solve this problem. They are tested on a benchmark with 120 instances whose optimal solutions are known and compared with an exact method. It shows that they can effectively solve the concerned problem and are much faster than the exact method for larger-scale cases. Their usage can well meet the industrial scheduling needs arising from a wire rod and bar rolling process while an exact method fails. © 2020 IEEE.

关键词： Scheduling

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Sliding mode control of Markovian jump systems under multi-node round-robin protocol

Sliding mode control of Markovian jump systems under multi-n...

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Chinese Control and Decision Conference, CCDC

作者： Zhiru Cao Jun Song Zhina Zhang Yugang Niu School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Artificial Intelligence Anhui University Hefei China Anhui Provincial Department of Education School of Mathematics-Physics and Finance Energy Internet Engineering Research Center Anhui Polytechnic University Wuhu China Key Laboratory of Smart Manufacturing in Energy Chemical Process (East China University of Science and Technology) Ministryof Education Shanghai China

ISBN: (纸本)9781665478977

In this paper, the sliding mode control problem is addressed for the networked Markovian jump system. In order to avoid data collision and congestion in transmission via the shared network, the scheduling of sensor signals towards the controller and controller signals towards the actuators is regulated by a novel round-robin protocol, the so-called multi-node round-robin protocol. This means that at each transmission instant, some consecutive actuator/sensor nodes (but not all) can access the transmission network, and the control/measurement information corresponding to other actuators/sensors is not updated. In order to deal with the incomplete transmission problem accounting for the considered protocol, a corresponding signal hold strategy is proposed to compensate for the unavailable signals. And then, a mode-token-dependent sliding mode controller is designed to ensure the reachability of the sliding surface, and sufficient conditions are presented to guarantee the stochastically ultimate bound of the resultant closed-loop system. Finally, a numerical example is adopted to illustrate the proposed method.

关键词： Sufficient conditions Actuators Protocols Current measurement Closed loop systems Sliding mode control

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Reduced-order observer design for a class of switched singular systems

Reduced-order observer design for a class of switched singul...

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第三十九届中国控制会议

作者： Jiasheng Shi Jun Zhao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University College of Information Science and Engineering Northeastern University

This paper studies reduced-order observer design problems for a class of switched singular systems with unknown inputs by average dwell time switching signals. The switched singular systems is first to be decomposed into a new equivalent form, and then a switched reduced-order observer is developed to asymptotically estimate the system states. In the end, the technique is applied to an electronic circuit model to demonstrate the merit and effectiveness of the proposed methods.

关键词： Average dwell time Switched singular systems Reduced-order observer

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Graph neural architecture for dynamic hybrid flowshop: Addressing stochastic events and uncertain processing sequences

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Neurocomputing 2025年 648卷

作者： Weixiang Xu Xiaochuan Luo Yejian Zhao Yulin Zhang College of Information Science and Engineering Northeastern University Shenyang 110819 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China University of Picardie Jules Verne Saint Quentin 02100 France

Dynamic hybrid flow shop scheduling (DHFSP) presents substantial difficulties in attaining optimal production efficiency. These challenges arise particularly when addressing indeterminate job sequences and stochastic events. Current dynamic scheduling methodologies employing deep reinforcement learning (DRL) predominantly target static-scale problems while exhibiting limited scalability to evolving complexities in industrial production systems. To enable responsive shop-floor decisions, this work introduces a hierarchical interaction attention-driven multi-graph network (HIAMG) framework engineered for DHFSP with uncertain job sequences and stochastic events. The proposed architecture implements graph-structured scenario encoding to address neural network dimension constraints. It is coupled with a novel graph embedding paradigm that hierarchically models job-machine interdependencies. Through explicit incorporation of stochastic events in scheduling simulations, our approach achieves enhanced operational fidelity reflecting real manufacturing settings. Integrating attention-based mechanisms further empowers the model to dynamically prioritize critical scheduling parameters and self-tune to environmental fluctuations. Comprehensive simulations across diverse operational regimes demonstrate that HIAMG surpasses conventional scheduling benchmarks in both performance metrics and configuration adaptability.

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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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Obstacle-Avoidance Distributed Optimal Coordination of Multiple Euler-Lagrangian Systems

Obstacle-Avoidance Distributed Optimal Coordination of Multi...

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第三十九届中国控制会议

作者： Liwei An Guang-Hong Yang College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

This paper investigates the problem of obstacle avoidance in the distributed optimal coordination for multiple uncertain Euler-Lagrangian(EL) systems. The main challenge focuses on the co-design of obstacle avoidance mechanism and distributed optimization strategy. To address it, a novel safety barrier function in the closed form of path integrals is constructed. By combining distributed optimization algorithm and adaptive tracking control, a distributed coordination algorithm is *** on Lyapunov method and boundedness analysis for the barrier function, it is proven that the global convergence and collision avoidance of the EL systems can be guaranteed in the presence of parametric uncertainties.

关键词： Distributed optimization obstacle avoidance Euler-Lagrangian systems multi-agent systems

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A Combinatorial Optimization Model for Customer Routing Problem Considering Cost Discount

A Combinatorial Optimization Model for Customer Routing Prob...

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Chinese Control and Decision Conference, CCDC

作者： Shenghao Zhou Qing Wang Yibo Sun Mutian Yang Dongxuan Li Min Huang College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Cost discount is a widely adopted strategy of logistics suppliers to increase order quantity. To improve the process of logistics routing decision in E-commerce, this paper studies a customer routing problem considering cost discount. Firstly, the 0-1 programming model is used to find the route to minimize the operating cost, considering the constraints of the due date by customers, transportation capacity and cost discount. Secondly, this optimization model is linearized and solved by CPLEX. Finally, numerical experiment is conducted to verify the validity of the model, which indicates that appropriate due date promised to customers, scale of order and proper cost discount will significantly save the transportation costs.

关键词： Logistics Routing Task analysis Airports Sorting Optimization

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A Study of Complex Industrial Systems Based on Revised Kernel Principal Component Regression Method

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IFAC-PapersOnLine 2020年第2期53卷 126-131页

作者： Chengyuan Sun Hongjun Ma College of information science and engineering Northeastern University Shenyang 110819 China Key Laboratory of Knowledge Automation for Process Industries Ministry of Education Beijing 100083 China

As a data-driven process monitoring method, multivariable statistics techniques have special potentials and advantages to handle the increasingly prominent “Big data challenge” in the complex industrial systems. However, the standard partial least square (PLS) method and the principal component regression (PCR) method cannot maintain stable function in the nonlinear operating environment. In order to capture the precise relation of process variables and product variables, an approach called the revised kernel PCR (RKPCR) method is proposed in this thesis to resolve the problems encountered in the traditional PCR method. In addition, a brief and effective diagnosis logic is designed to decrease the difficulty of fault diagnosis. Finally, the effectiveness of the RKPCR algorithm is illustrated utilizing the Tennessee Eastman case (TEC) platform.

关键词： Nonlinear process the RKPCR method quality-related process monitoring

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Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater’s Condition

arXiv

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arXiv 2023年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore 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 distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize network regret and cumulative constraint violation. A novel distributed online algorithm is proposed and it achieves an O(T max{c,1−c}) network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the number of rounds and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T 1−c). Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to O(log(T)), and the network cumulative constraint violation bound is reduced to O(√log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is the first to achieve reduced (network) cumulative constraint violation bounds for (distributed) online convex optimization with adversarial constraints under Slater’s condition. Finally, the theoretical results are verified through numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

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Operating Performance Assessment Based on Performance-Relevant Independent Component Analysis for Non-Gaussian Batch processes

Operating Performance Assessment Based on Performance-Releva...

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第三十九届中国控制会议

作者： Yan Liu Hang Nie Fuli Wang College of Information Science & Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

As an effective way to ensure the economic benefits of enterprises, the process operating performance assessment has attracted more and more attentions from industry and academia in recent years. In this paper, a operating performance assessment method based on performance-relevant independent component analysis（PR-ICA） is proposed for multiphase batch processes with non-Gaussian distribution. To identify the phases with different characteristics, a new PR-ICA based phase division method is developed first, which can not only ensure that each phase has consecutive samplings but also retains the specific performance-relevant variation information of different phases. Then the assessment models of each phase are established based on PR-ICA with the separated process data after offline phase division. Furthermore, the similarity between the performance-relevant variation information of the online data and that of the corresponding phase is calculated and used to define the assessment index for online assessment. For the nonoptimal operating performance, it is to identify the responsible cause variables through the variable contribution rate based nonoptimal cause identification method. Finally, the effectiveness of the proposed assessment method is demonstrated through the fed-batch penicillin fermentation process.

关键词： Non-Gaussian batch processes operating performance assessment PR-ICA nonoptimal cause identification

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