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第32届中国过程控制会议(CPCC2021)

作者： Zhiqiang Wang Qiang Li Dakuo He Xiang Ma School of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University SINTEF Industry

Copper flotation process is a complex industrial *** copper grade(CCG) is one of the important production indexes of flotation process,and it is also one of the important factors to determine the economic ***,the accurate prediction of CCG can not only provide operational advice for copper flotation process,but has important economic value and practical *** to the long process of copper flotation,the existence of time delay and the serious coupling of variables,the accurate prediction of CCG is *** on the analysis and study of copper flotation process,this paper adopts partial least squares(PLS),kernel partial least squares(KPLS),least squares support vector machine(LSSVM) and long short term memory(LSTM) methods to predict the CCG for a copper flotation *** of the proposed prediction models are validated by the field data in this paper and it can be learnt from the prediction evaluation indexes for root mean square error(RMSE),mean absolute error(MAE) and mean absolute percentage error(MAPE) that the prediction accuracy based on LSTM model is higher and suitable for the prediction of CCG for the copper flotation process.

关键词： copper flotation process prediction models linear and nonlinear methods copper concentrate grade

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Research on Joint Optimization of Single Train's Delay Reduction and Energy-Saving Operation Trajectory

Research on Joint Optimization of Single Train's Delay Reduc...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Yunjia Zhang Dongliang Cui Jianming Li Feng Gao Haijun Zang Xuewu Dai State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China China Railway Shenyang Group Co. Ltd Tongliao logistics center Shenyang China China Railway Shenyang Group Co. Ltd Dispatching office Shenyang China Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon Tyne UK

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

This study focuses on optimizing urban rail transit operations during emergencies, aiming to reduce train traction energy consumption and expedite delay recovery by jointly optimizing arrival/departure times at each station and the operational trajectory between stations. A 3D STS (Space-Time-Speed) network is created to merge macroscopic train timetable scheduling with microscopic train operation trajectory optimization. By appropriately setting dimension resolutions, the system approximates the train's position, speed, and acceleration, transforming the complex train operation problem into a more manageable path planning challenge. Propose dual objectives: minimize net traction energy and delay time, aiming to reduce overall train operating costs and delays. Weighted summation combines objectives into a single nonlinear optimization. Dynamic programming then tackles the transformed path planning problem. The simulation results of Beijing Yizhuang Line show that while delay increases energy consumption, judicious selection of train operation trajectory can significantly reduce the energy consumption of the train. Factoring in energy consumption yields a 15.43% reduction compared to disregarding it, ensuring more efficient energy use without disrupting normal train operations.

关键词： Energy consumption Costs Three-dimensional displays Simulation Velocity control Delays Dynamic programming Time factors Resource management Trajectory optimization

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Hexapod Robot Gait Switching Based on Different Wild Terrains

Hexapod Robot Gait Switching Based on Different Wild Terrain...

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Data Driven Control and Learning Systems (DDCLS)

作者： Shijie Fei Yiyang Chen Hongfeng Tao Hongtian Chen School of Mechanical and Electrical Engineering Soochow University Suzhou P. R. China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi P. R. China Department of Automation Shanghai Jiao Tong University Shanghai China

Hexapod robots have become an indispensable part of legged robots due to their excellent stability and traversability, and have recently become a hot research topic. In complex outdoor environments, the ability of the hexapod robot to switch its motion strategy according to the terrain is crucial for improving its mobility. This paper presents a terrain recognition network that enables our hexapod robot to switch its forward gait based on the different types of outdoor terrain. This method provides better stability for the hexapod robot than traditional single-gait walking through different terrains.

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Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Control for Unmanned Surface Vehicles with Uncertainties

Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Con...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huarong Zhao Jinjun Shan Xing Li Hongnian Yu School of Internet of Things Engineering Jiangnan University Wuxi P. R. China Department of Earth and Space Science and Engineering York University Toronto ON Canada School of Electrical Engineering and Intelligentization Dong-guan University of Technology Dongguan P. R. China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Liaoning P. R. China School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches.

关键词： Uncertainty Event detection Soft sensors Delay effects Mathematical models Data models Steady-state Vehicle dynamics Autonomous vehicles Cybernetics

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Towards Greener Rail Transit: Multi-Train Timetable and Speed Profile Optimization for Energy Conservation*

Towards Greener Rail Transit: Multi-Train Timetable and Spee...

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International Conference on automation and Computing (ICAC)

作者： Yunjia Zhang Dongliang Cui Weiqiang Wang Xubin Sun Zhiming Yuan Zhongbei Tian Xuewu Dai State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Shandong High-Speed Rail Transit Group Co. Ltd China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing China School of Engineering The University of Birmingham Birmingham UK Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle Upon Tyne UK

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

Aiming at dynamic scheduling in multi-train timetable, a optimization model of multi-train timetable and speed profile optimization for energy conservation is proposed. The proposed approach integrates train operation trajectory optimization with the scheduling of arrival and departure times at stations. At each instance, the highest-priority train operation process is selected, and optimization is performed for the operation of only one train between stations. Various constraints are considered to ensure safe train operation and prevent conflicts with preceding trains. By collaboratively regulating and optimizing train arrival/ departure time, speed profile, traction/braking force, minimizing train traction energy consumption and minimizing power peak can be achieved. The objectives are combined into a single-objective nonlinear optimization problem through weighted summation, which is then solved using the differential evolution algorithm. The simulation verification conducted on the Yizhuang Line demonstrates that this method enables more efficient utilization of energy while also recovery from the delay. The train traction energy consumption was reduced by 11.35%. This work provides an important technical support and guidance for realizing the green, low-carbon and sustainable development of urban rail transit in the future.

关键词： Rails Energy consumption Optimization models Energy conservation Green products Dynamic scheduling Traction power supplies Delays Optimization Trajectory optimization

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Spectral Bisection Community Detection Method for Urban Road Networks

Spectral Bisection Community Detection Method for Urban Road...

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第40届中国控制会议

作者： Lu Guo Ying Cui Haili Liang Zhao Zhou Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and AutomationShanghai University Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology

Coordination control of large-scale urban traffic networks provides a more easy and feasible way to improve mobility in heterogeneous city centers. Urban traffic network partitioning is the foundation and prerequisite for achieving regional *** order to describe the correlation strength between two adjacent intersections, a quantitative indicator considering the dynamic traffic flows is proposed to depict the congested degree in each link. Based on the simulation data collected from microscopic traffic model, the topology of urban traffic network could be converted into an adjacent matrix. Inspired by the concept of community detection in complex networks theory, spectral bisection method is utilized to find the spatial compactness areas in traffic networks. In addition, the modularity is adopted to evaluate the partitioning results and to point out the next partitioning direction at each step. The simulation is carried out for a peak hour in SUMO. Compared with other classical community detection methods, the proposed approach can obtain the adequate number of subnetworks with less computational complexity.

关键词： Complex Network Urban Road Networks Community Detection Spectral Bisection Method

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A Deep Neural Network-Assisted Diagnosis Method for Cardiovascular Disease Based on the Optimization of the TAdam Algorithm

A Deep Neural Network-Assisted Diagnosis Method for Cardiova...

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Chinese automation Congress (CAC)

作者： Yumei Hu Xing Li Yang Zhang Yuxuan Zhu Yuechao Sun College of Computer Science and Technology Dongguan University of Technology Dongguan China School of Electrical Engineering & Intelligentization Dongguan University of Technology Dongguan China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Mechanical Engineering of DGUT Dongguan University of Technology Dongguan China Northeastern University College of Information Science and Engineering Northeastern University Shenyang China

Cardiovascular disease is a sudden and extremely disabling and deadly disease that hinders the development of public health care in China and poses a serious threat to the health of users. Therefore, this paper proposes a deep neural network-assisted diagnosis of cardiovascular disease based on TAdam algorithm optimization under a distributed medical monitoring network to enable rapid identification of cardiovascular patients, timely detection and reporting of the condition. This paper addresses the problems that deep neural network models are susceptible to the influence of their structure and hyperparameters and poor generalization convergence, firstly, the mainstream optimization algorithms based on deep learning are sorted out, and finally the T Adam algorithm is used to optimize the model. By comparing the common gradient optimization algorithms SGD (Stochastic Gradient Descent), Adagrad (Adaptive Gradient Algorithm), RMSprop (Root Mean Square propagation), Adam (Adaptive Moment Estimation) and the improved TAd am optimization algorithm trained and tested on the same dataset, the experimental results show that the TAdam algorithm exhibits the fastest convergence speed and the best generalization performance on the cardiovascular disease dataset. The method proposed in this paper enables rapid diagnosis of the user's disease risk and feedback to the relevant personnel to maximise user safety.

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Mean Square Consensus under Coopetitive Social Networks with Communication Noise

Mean Square Consensus under Coopetitive Social Networks with...

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第40届中国控制会议

作者： Hanzhe Cai Fanli Yuan Haili Liang Zhao Zhou Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and AutomationShanghai University Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology

This paper aims to investigate a stochastic Altafini model, in which individuals are disturbed by the communication noise. We use coopetitive networks to describe their affiliation of classes. Different from the general situations with noises, when the opinion values in the classes converge, the noise will decrease. We consider the communication noise, when the differences of opinion values are larger, the influences of noise are greater. When the opinion values are reaching to biparite consensus,the influences of noise are decreasing, such a design is more in line with the reality. By constructing the appropriate Lyapunov function, the conditions for the stochastic stability of the system are obtained. We get the conclusion of mean square biparite consensus. In addition, numerical simulation results can verify the theoretical results.

关键词： Opinion dynamic Coopetitive networks Undirected graph Altafini model Communication noise

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Input-output finite-time sliding mode fault-tolerant control for Markovian jump systems with incomplete transition rates

Input-output finite-time sliding mode fault-tolerant control...

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Chinese Control and Decision Conference, CCDC

作者： Meng Zhao Haijuan Zhao Zhiru Cao Yugang Niu Ministry of Education The Key Laboratory of Smart Manufacturing in Energy Chemical Process (East China University of Science and Technology) Shanghai China School of Information and Control Engineering Qingdao University of Technology Qingdao China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9781665478977

This paper deals with the input-output finite-time sliding mode control (SMC) problem of a class of uncertain Markovian jump systems with incomplete transition rates. It is assumed that the actuator failure may occur. A mode-dependent sliding surface is constructed. By synthesizing a fault-tolerant SMC scheme, the specified sliding surface can be reached in finite time. Furthermore, the input-output finite-time stability over both the reaching and sliding motion stages is ensured simultaneously by establishing some sufficient conditions. Finally, the developed fault-tolerant SMC approach is verified by a numerical example.

关键词： Fault tolerance Actuators Sufficient conditions Fault tolerant systems Resists Sliding mode control Numerical stability

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