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2024 China automation Congress, CAC 2024

作者： Xi, Chao Wu, Gaochang Fan, Zizhu Xu, Feng Wang, Hui Tang, Xiaokun School of Electrical and Automation Engineering East China Jiaotong University Nanchang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang China

ISBN: (纸本)9798350368604

The smelting process of the fused magnesium furnace(FMF) is prone to semi-molten abnormal conditions, which may lead to low product quality and production efficiency, and is also prone to safety accidents. At present, the operating conditions of FMF mainly rely on manual observation of the furnace shell features for diagnosis, and it is hard to timely and effectively identify abnormalities in the smelting process. Given the difficulty in identifying abnormal operating conditions of FMF, this paper proposes a deep multiview fuzzy classification method(DMFC) that can automatically identify abnormal operating conditions. First, based on the three-phase current data, CNN and Transformer are used to extract features as multiple views. Secondly, the Takagi-Sugeno-Kang(TSK) fuzzy model is used as a single-view model, and the evidence theory is used for dynamic fusion of multiple views. This method can effectively fuse multiple deep features, and has strong scalability and a certain degree of interpretability. Through verification of the real historical data of multiple FMFs, our model has relatively high accuracy, false detection rate(FDR), and missed detection rate(MDR). © 2024 IEEE.

关键词： Smelting

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Iterative Learning Hybrid Robust Predictive Control for Multi-Phase Batch processes with Asynchronous Switching via a Lyapunov-Razumikhin Approach

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 345-358页

作者： Li, Hui Wang, Shiqi Shi, Huiyuan Li, Ping Su, Chengli School of Electronics and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Information Engineering Liaodong University Dandong118001 China

For a class of the industrial cyber-physical system, i.e., multi-phase batch processes with small time-delays and asynchronous switching, this study develops a two-dimensional iterative learning hybrid robust predictive control method. Compared with the previous Lyapunov-Krasovsky function-based method, stability conditions given by the Lyapunov-Razumikhin function-based method is less conservative intensive in dealing with small time-delays. Not only that, compared to the traditional iterative learning method, the rolling-optimized gains of the control law make it possible for the designed controller to have fewer learning cycles, and the control performance is even better. Meanwhile, the minimum and the maximum dwelling times are obtained in conjunction with the exponential stability analysis of the system. Since the obtained dwelling times, an advanced switching signal is provided to avoid asynchronous switching situations. The simulation results for the injection molding process are finally utilized to illustrate the effectiveness and feasibility of the developed approach. © 2023 The Authors.

关键词： Time delay

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Method and application for estimating the density of broiler flocks based on deep learning 4

Method and application for estimating the density of broiler...

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4th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Zhang, Xiaolin Peng, Xin He, Kaixun Zhong, Ningfan Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai China

ISBN: (纸本)9798350373233

In the intensive poultry breeding process, precise regulation of environmental factors such as temperature and humidity is directly related to the breeding efficiency. Intelligent regulation methods are extremely important for cost reduction and efficiency improvement. The setting values of existing poultry house temperature and humidity controllers are mostly manually determined, which requires enormous labor costs. With the development of information technology, it is becoming easier to obtain breeding data. Starting from large-scale breeding data, using machine learning and other methods to intelligently estimate the optimal temperature and humidity of poultry houses is an effective way to solve the above problems. Considering that there is a strong correlation between temperature and humidity and the spatial distribution of poultry, this article starts with the density estimation of poultry groups, proposes a Transformer-based poultry density estimation method, and selects broilers as the research object. The results obtained from experiments on the broiler density estimation dataset (M2-fowl) demonstrate an average absolute error (MAE) of 20.97 and a mean squared error (MSE) of 33.15 for this method. These findings indicate the algorithm's capability to provide accurate estimates of poultry density, establishing a groundwork for assessing the comfort levels in poultry houses and implementing intelligent adjustments to temperature and humidity. © 2023 IEEE.

关键词： Deep learning

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Preference Prediction-Based Evolutionary Multiobjective Optimization for Gasoline Blending Scheduling

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 79-92页

作者： Fang, Wenxuan Du, Wei Yu, Guo He, Renchu Tang, Yang Jin, Yaochu East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Nanjing Tech University Institute of Intelligent Manufacturing Nanjing211816 China China University of Petroleum Department of Automation College of Artificial Intelligence Beijing102249 China Westlake University School of Engineering Hangzhou310030 China

Gasoline blending scheduling is challenging, involving multiple conflicting objectives and a large decision space with many mixed integers. Due to these difficulties, one promising solution is to use preference-based multiobjective evolutionary algorithms (PBMOEAs). However, in practical applications, suitable preferences of decision makers are often difficult to generalize and summarize from their operational experience. This article proposes a novel framework called preference prediction-based evolutionary multiobjective optimization (PP-EMO). In PP-EMO, suitable preferences for a new environment can be automatically obtained from historical operational experience by a machine learning-based preference prediction model when we feed the model with the input of the optimization environment. We have found that the predicted preference is able to guide the optimization to efficiently obtain a set of promising scheduling scenarios. Finally, we conducted comparative tests across various environments, and the experimental results demonstrate that the proposed PP-EMO framework outperforms existing methods. Compared with no preference, PP-EMO reduces operating costs by about 25% and decreases blending errors by 50% under demanding operational conditions. © 2024 IEEE.

关键词： Job shop scheduling

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Event-Triggered Cooperative Adaptive Cruise Control of Vehicular Platoons 63

Event-Triggered Cooperative Adaptive Cruise Control of Vehic...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhao, Fuyu Gao, Weinan Liu, Tengfei Jiang, Zhong-Ping Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110819 China New York University Tandon School of Engineering Department of Electrical and Computer Engineering BrooklynNY11201 United States

ISBN: (纸本)9798350316339

This paper presents a novel policy iteration (PI) based event-triggered cooperative adaptive cruise control (CACC) approach for vehicular platoons, which are composed of autonomous vehicles. An observer is established to observe the signal of leading vehicle. To reduce computation and communication resources, event-triggered based adaptive optimal controllers are designed by using the PI algorithm without relying on the knowledge of system dynamics. Sufficient conditions are introduced to achieve the leader-to-formation stability (LFS) of the vehicular platoons. Simulation results are presented to verify the effectiveness of the proposed design. © 2024 IEEE.

关键词： Adaptive cruise control

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An End-to-End Framework for Optimizing Foot Trajectory and Force in Dry Adhesion Legged Wall-Climbing Robots

arXiv

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arXiv 2025年

作者： Xiao, Jichun Nie, Jiawei Hao, Lina Li, Zhi The School of Mechanical Engineering and Automation Northeastern University United States The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University United States

Foot trajectory planning for dry adhesion legged climbing robots presents challenges, as the phases of foot detachment, swing, and adhesion significantly influence the adhesion and detachment forces essential for stable climbing. To tackle this, an end-to-end foot trajectory and force optimization framework (FTFOF) is proposed, which optimizes foot adhesion and detachment forces through trajectory adjustments. This framework accepts general foot trajectory constraints and user-defined parameters as input, ultimately producing an optimal single foot trajectory. It integrates three-segment C2 continuous Bezier curves, tailored to various foot structures, enabling the generation of effective climbing trajectories. A dilate-based GRU predictive model establishes the relationship between foot trajectories and the corresponding foot forces. Multi-objective optimization algorithms, combined with a redundancy hierarchical strategy, identify the most suitable foot trajectory for specific tasks, thereby ensuring optimal performance across detachment force, adhesion force and vibration amplitude. Experimental validation on the quadruped climbing robot MSTM3F showed that, compared to commonly used trajectories in existing legged climbing robots, the proposed framework achieved reductions in maximum detachment force by 28 %, vibration amplitude by 82 %, which ensures the stable climbing of dry adhesion legged climbing robots. © 2025, CC BY.

关键词： Multiobjective optimization

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A Motion Intent Recognition Method Based on Dynamic Feature Fusion and Multi-Stream TCN 6

A Motion Intent Recognition Method Based on Dynamic Feature ...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Chen, Jianfeng Li, Xing Wang, Fujie Zhu, Yuxuan School of Electrical Engineering & Intelligentization Dongguan University of Technology Guangdong Dongguan523808 China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning Shenyang110819 China School of Information Science and Engineering Northeastern University Liaoning Shenyang110819 China

ISBN: (纸本)9798350356618

Clinical motor relearning theory emphasizes the active participation of patients, so the recognition of patient's motor imagery plays a key role in robot-assisted rehabilitation exercise training. We constructed an upper limb rehabilitation sEMG (Surface Electromyography) dataset by capturing 18 upper limb rehabilitation movements using a wireless sEMG acquisition system and proposed a deep learning model based on dynamic feature fusion with a multi-stream Temporal Convolutional Network (MS-TCN), which identified the patient's motor intention from the captured sEMG by end-to-end learning. On the one hand, the method can extract the temporal features of single-channel signals through the multi-stream TCN structure, and on the other hand, dynamic feature fusion can effectively utilize the primary-secondary relationship of multiple muscles in completing different motions, achieving high recognition accuracy and low response time. Through simulation analysis and experimental testing, the results show that this method can achieve higher classification accuracy than mainstream motor intention recognition algorithms on different subjects, and the effective use of this method will promote the recovery of patient's limb function and significantly improve the efficiency of rehabilitation training. © 2024 IEEE.

关键词： Deep learning

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Recent Developments in Event-Triggered Control of Nonlinear Systems:An Overview

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无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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Temporal Graph Convolutional Network-Based Fault Detection Method for the Two-Wheeled Self-Balancing Robot 6

Temporal Graph Convolutional Network-Based Fault Detection M...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Hu, Yuxiang Yue, Peng Liu, Hui Kang, Jiajun Cui, Dongliang Dai, Xuewu Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northumbria University Department of Mathematics Physics and Electrical Engineering Newcastle-upon-TyneNE1 8ST United Kingdom

ISBN: (纸本)9798350356618

The fault detection method based on graph neural networks has been widely applied. However, few of these methods consider the dynamic characteristics and prior knowledge of control systems, resulting in limited usage for fault detection in control systems. To address this, this article proposes a fault detection method based on temporal graph convolutional neural network (T-GCN) and multi-layer perceptron (MLP), which is applicable to general linear dynamic control systems. The proposed method first constructs a relational graph based on prior knowledge of the control system and utilizes T-GCN to extract spatio-temporal relationships between various state variables and control input signals within the system. Then, a MLP integrates the extracted information to detect whether the control system has experienced faults. Finally, a two-wheeled self-balancing vehicle is utilized to test the effectiveness of the proposed method. The results demonstrate that the proposed approach could achieve high-precision detection of sensor faults within the system. © 2024 IEEE.

关键词： Graph neural networks

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Distributed optimal consensus of multiagent systems with Markovian switching topologies: synchronous and asynchronous communications

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Science China(Information Sciences) 2023年第12期66卷 154-163页

作者： Juan ZHANG Huaguang ZHANG Bowen ZHOU Xiangpeng XIE College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries School of Internet of Things Nanjing University of Posts and Telecommunications

Considering limited system configurations and communication network bandwidths, this paper proposes two fully distributed event-triggered control schemes, namely synchronous and asynchronous communication, to solve the optimal consensus problem of linear multiagent systems(MASs) with Markovian switching topologies. The concept here is to design a novel composite dynamic event-triggered mechanism for each agent(using the information of all neighbors and one of them), based on which two fully distributed event-triggered protocols are designed to reach consensus and minimize a global convex team performance function. The effectiveness of the proposed control schemes is analyzed theoretically, that is, the optimal consensus problem can be realized, and the MAS does not exhibit Zeno behavior. Compared with existing optimal consensus results, the outstanding advantage of the proposed schemes is that a fully distributed asynchronous event-triggered communication scheme is designed, which fills the gap of optimal consensus results in this respect.

关键词： optimal consensus dynamic event-triggered control synchronous and asynchronous communications

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