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SSRN 2024年

作者： Ma, Shiquan Ding, Jinliang College of Information science and Engineering Northeast University Shenyang110819 China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

Accommodating mass renewable energy into the power grid requires coal-fired power plants to trace reference trajectory, which varies rapidly and is prescribed by the power dispatching system. The coordinating control system (CCS) is impotent because decoupling nets work well only on one linearized point of this nonlinear system. This paper presents a multi-observer prediction framework based on model predictive control (MPC) to address the tricky problem. In this framework, multiple state observers predict the output states within the predictive horizon. The arbitration mechanism chooses the right observer on the actual outputs of the system. The picked observer will calculate control inputs subject to energy constraints. Then, the issue is transformed into a quadratic programming formulation. Numerical computation based on gradient descent is used to obtain the optimal control inputs. The experimental results demonstrate that the outputs of coal-fired plants perform more flexibly under the proposed framework than CCS. This approach can improve the capability of equipment. © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Multimodal process Monitoring Based on Dynamic Extended K-means-VAE-RF

Multimodal Process Monitoring Based on Dynamic Extended K-me...

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第35届中国控制与决策会议

作者： Shiwei Su Kaixiang Peng Xueyi Zhang Key Laboratory for Advanced Control of Iron and Steel Process of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing

With the development of customization and flexibility of manufacturing industry,intelligent manufacturing process gradually presents multimodal characteristics,which are reflected in the strong coupling,nonlinear,multi working conditions and other characteristics of the *** this paper,a multi-modal process monitoring method based on dynamic expansion is *** view of the dynamic characteristics of process data,the process data is dynamically expanded through the amplification matrix,and then the multi-modal process data is modal divided through the improved K-means *** divided multi-modal process data is monitored using Variational Autoencoder,and relevant statistics and control limits are ***,the multi-modal process monitoring is constructed by introducing the method of random forest for modal *** validity of the algorithm is verified by real data of steel rolling process.

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A Self-Attention and GRU based Method for Operational State Assessment of Hydrocyclone

A Self-Attention and GRU based Method for Operational State ...

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2022 Chinese automation Congress, CAC 2022

作者： Liang, Tao Wang, Fuli Wang, Shu Wang, Yun Lu, Di Xiao, Yuan Northeastern University College of Information Science and Engineering Shenyang China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang China Zhangjiagang Secondary Vocational School in Jiangsu Suzhou China

ISBN: (纸本)9781665465335

Hydrocyclone is extensively known as an important equipment for separating mineral mixtures with different particle sizes in the mineral processing. However, the undesirable blockage faults seriously threaten the safety and stability of the hydrocyclones. Therefore, it is valuable to evaluate the operational state of the hydrocyclone and detect the blockage fault timely. To solve the above problems, a Self-Attention and GRU based operational state assessment algorithm of hydrocyclone is proposed in this paper. Firstly, the acquired multivariate temporal sequences are split into the many discrete window data by sliding window technique. Then, K-mean method is used to cluster the obtained window data, and the state labels can be assigned to each window data in chronological order. Lastly, the self-attention mechanism is used to obtain the dependencies between the window data variables, while the corresponding temporal features are captured by Gated Recurrent Unit (GRU) network. Based on the obtained variable dependencies and temporal features, an operational state assessment model of the hydrocyclone is built by using a feedforward neural network. Verification experiments using the operational data of the hydrocyclone show that the proposed algorithm is effective and feasible. © 2022 IEEE.

关键词： Feedforward neural networks

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Set-valued state estimation of nonlinear discrete-time systems and its application to attack detection

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IEEE/CAA Journal of Automatica Sinica 2024年 1-11页

作者： Liu, Hao Han, Qing-Long Li, Yuzhe School of Automation Hubei University of Science and Technology Xianning 437100 China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC 3122 Australia State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110004 China

This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded (UBB) noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets. First, properties of constrained polynomial zonotopes are provided and the order reduction method is given to reduce the computational complexity. Then, the corresponding improved prediction-update algorithm is proposed so that it can be adapted to non-convex sets. Based on generalized intersection, the utilization of set-based estimation for attack detection is analyzed. Finally, an example is given to show the efficiency of our results. IEEE

关键词： Polynomials

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Resonance-interference concurrent fiber sensor: Enabling simultaneous detection of temperature and humidity

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Sensors and Actuators B: Chemical 2025年 440卷

作者： Wang, Mingyue Sun, Yutong Zheng, Hongkun Zhang, Ya-nan College of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

This paper presents an innovative fiber sensor that enables concurrent measurement of relative humidity (RH) and temperature. The sensor predominantly comprises a polyvinyl alcohol (PVA) microbottle resonator (MBR) operating on resonance and a silica coupler functioning via interference. The two components are fabricated by simple self-assembly and arc fusion respectively, and then coupled into an integrated sensor. The coupler serves a dual role as both a temperature sensor that capitalizes on the thermal response of silica and a coupling component that excites the resonance circulated in the MBR. Meanwhile, PVA exhibits variable shrinkage and expansion behaviors subjected to the stimulation of diverse temperatures and RH, which triggers the refractive index and radius evolution of MBR, thereby modulating the resonance dip shift. For RH measurement (32–84 % RH), the resonance dip exhibits an average sensitivity of 103.34 pm/%RH. For temperature measurement (24–60℃), the average sensitivity of the resonance dip is −22.41 pm/℃ and that of the interference dip is −29.35 pm/℃. In addition, comprehensive characterizations were carried out on its repeatability, dynamic response, reversibility, and stability. The superior comprehensive behavior positions the sensor favorably for simultaneous RH and temperature measurement in intricate and challenging environments. © 2025 Elsevier B.V.

关键词： Temperature measurement

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Robust Event-triggered Model Predictive Control for Multi-Agent Systems with Switching Topologies

Robust Event-triggered Model Predictive Control for Multi-Ag...

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2022 Chinese automation Congress, CAC 2022

作者： Liu, Jiajun Zhao, Hui Li, Ya Zhao, Yuan Dai, Xuewu Shenyang University of Technology School of Artificial Intelligence Shenyang China Dalian University College of Information Engineering Dalian China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang China

ISBN: (纸本)9781665465335

A robust event-triggered model predictive control strategy for multi-agent systems with random switching topologies is proposed in this paper. An event-triggered scheme is proposed to determine when state information of an agent is broadcast to its local controller and neighboring agents. A distributed control protocol is presented. The consensus problem is converted to the stabilization of a Markov jump system. Then a sufficient condition for leader-following consensus is derived. With a synthesis method to propose the robust controller and event-triggered weighting matrix, the objective function is minimized. The effectiveness of the proposed method is illustrated by a numerical example. © 2022 IEEE.

关键词： Multi agent systems

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Long‐time target tracking algorithm based on re‐detection multi‐feature fusion

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IET Cyber-Systems and Robotics 2022年第1期4卷 38-50页

作者： Junsuo Qu Chenxue Tang Yuan Zhang Kai Zhou Abolfazl Razi School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent ProcessXi'an University of Posts and TelecommunicationsXi'anShaanxiChina School of Communication and Information Engineering Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina School of Informatics Computing and Cyber SystemsNorthern Arizona UniversityFlagstaffAZUSA

This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change,target deformation,scale change,motion blur,and other *** specifically,a target tracking algorithm,called re-detection multi-feature fusion,is proposed based on the fusion of scale-adaptive kernel correlation filtering and *** target tracking algorithm trains three kernel correlation filters based on the histogram of oriented gradients,colour name,and local binary pattern features and then obtains the fusion weight of response graphs corresponding to different features based on average peak correlation energy criterion and uses weighted average to complete the position estimation of the tracked *** order to deal with the problem that the target is occluded and disappears in the tracking process,a random fern classifier is trained to perform re-detection when the target is *** comparing the OTB-50 target tracking dataset,the experimental results show that the proposed tracker can track the target well in the occlusion attribute video sequence in the OTB-100 test dataset and has a certain improvement in tracking accuracy and success rate compared with the traditional correlation filter tracker.

关键词： machine learning pedestrian identification robustness visual surveillance visual tracking

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Non-Line-Of-Sight Target Localization in T-Shaped Roadways Using Simo Fmcw Millimeter-Wave Radar

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SSRN 2024年

作者： Hu, Qingsong Luo, Yujia Wang, Liudi Cheng, Yuanxun Zhang, Yuansheng Yuan, Shuya Kou, Zhihao School of Information and Control Engineering China University of Mining & Technology Jiangsu Xuzhou221116 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing100032 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing100032 China

The hazardous and complex nature of underground mining imposes stringent safety requirements on autonomous driving. T-shaped roadways, common in such environments, present significant challenges for non-line-of-sight (NLOS) target localization. Traditional underground positioning technologies often fail in these settings due to severe signal attenuation and multipath effects, leading to potential accidents. This manuscript proposes a novel localization algorithm using single-input-multi-output (SIMO) Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar to address these challenges. We analyze millimeter wave multipath propagation in T-shaped tunnels and construct an FMCW multipath echo model. Using CA-CAFAR and MVDR algorithms, we accurately estimate target distance and angle information. Additionally, a theoretical false point matching elimination method is designed to improve positioning accuracy by mitigating false target interference. Field tests in typical T-shaped NLOS scenarios demonstrate the method's high-precision positioning, enhancing the safety and efficiency of autonomous vehicles in coal mines. © 2024, The Authors. All rights reserved.

关键词： Coal mines

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Adaptive Optimal Output-Feedback Control of Discrete-Time Systems based on Hybrid Iteration

Adaptive Optimal Output-Feedback Control of Discrete-Time Sy...

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IEEE Conference on Decision and Control

作者： Sitong Liu Weinan Gao Zhong-Ping Jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Lianning China Department of Electrical and Computer Engineering Tandon School of Engineering New York University Brooklyn NY USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, a novel adaptive dynamic programming (ADP) algorithm, named output-feedback hybrid iteration (HI),is proposed to address the adaptive optimal control problem of discrete-time linear systems. The proposed output-feedback HI strategy learns the optimal control policy through two phases. First, a novel data-driven value-iteration (VI) scheme is employed to learn an admissible output-feedback control policy using the input/output data without relying on the knowledge of system matrices. Then, with the obtained admissible control policy, the optimal output feedback-control policy is approximated with an accelerated convergence rate through output-feedback policy iteration (PI). Online input/output data are utilized to reconstruct the full state of the system and integrated into the new output-feedback HI algorithm. Simulation results are presented and demonstrate the efficacy and practicality of the proposed output-feedback HI approach in comparison with traditional PI and VI techniques.

关键词： Linear systems Discrete-time systems Adaptive systems Simulation Heuristic algorithms Optimal control Approximation algorithms Dynamic programming Convergence

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Dynamic Graph Learning Soft Sensor in process Industry

Dynamic Graph Learning Soft Sensor in Process Industry

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Data Driven Control and Learning Systems (DDCLS)

作者： Mingwei Jia Danya Xu Tao Yang Yuan Yao Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Department of Chemical Engineering National Tsing Hua University Taiwan

The development of an accurate soft sensor modeling method in the process industry remains a great challenge because the coupling relationship between variables is always intricate and difficult to model. In this work, a dynamic graph learning (DGL) soft sensor is proposed to alleviate this problem. The proposed model realizes the ability of the soft sensor to perceive the coupling relationship in real time by automatically learning the dynamic graph. Then, a causal convolutional mechanism and a multi-hop graph attention mechanism are used to systematically construct the dependencies of variables in the spatial-temporal dimension and model their variation patterns effectively. Finally, the proposed method is tested on the penicillin fermentation process and shown to be feasible and effective. The results showed that the change of the dynamic graph in the spatial-temporal dimension was in line with the process mechanism.

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