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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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Prediction model for mechanical properties of hot-rolled strips by deep learning

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Journal of Iron and Steel Research International 2020年第9期27卷 1045-1053页

作者： Wei-gang Li Lu Xie Yun-tao Zhao Zi-xiang Li Wen-bo Wang Engineering Research Center of Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and TechnologyWuhan 430081HubeiChina Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and TechnologyWuhan 430081HubeiChina Hubei Province Key Laboratory of Systems Science in Metallurgical Process Wuhan University of Science and TechnologyWuhan 430081HubeiChina

The prediction of the mechanical properties of hot-rolled strips is a very complex,highly dimensional and nonlinear problem,and the published models might lack reliability,practicability and ***,a new model was proposed for predicting the mechanical properties of hot-rolled strips by deep ***,the one-dimensional numerical data were transformed into two-dimensional data for expressing the complex interaction between the influencing ***,a new convolutional network was proposed to establish the prediction model of tensile strength of hot-rolled strips,and an improved inception module was introduced into this network to abstract features from different *** comparative experiments were carried out to find the optimal network structure and its ***,the prediction experiments were carried out on different models to evaluate the performance of the new convolutional network,which includes the stepwise regression,ridge regression,support vector machine,random forest,shallow neural network,Bayesian neural network,deep feed-forward network and improved LeNet-5 convolutional neural *** results show that the proposed convolutional network has better prediction accuracy of the mechanical properties of hot-rolled strips compared with other models.

关键词： Deep learning Convolutional neural network Mechanical property Hot-rolled strip

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Operating Performance Assessment of Complex Industrial process Based on Kernel Locally Linear Embedding PLS

Operating Performance Assessment of Complex Industrial Proce...

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第32届中国过程控制会议(CPCC2021)

作者： Chu Fei Mo Shuangshuang Wang Fuli Lu Ningyun Ma Xiaoping Research Center of Underground Space Intelligent Control Engineering of the Ministry of Education China University of Mining and Technology School of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Automatic Control Technology for Mining and Metallurgy Process Beijing General Research Institute of Mining & Metallurgy State Key Laboratory of Integrated Automation for Process Industries Northeastern University College of Automation Engineering Nanjing University of Aeronautics and Astronautics

Due to the process disturbances and uncertainties,the operating performance of industrial process may deviate from the optimal operating point along with ***,non-optimal operation may lead to decline production quality and economic *** it is significant to implement online assessment strategy and further analyze the actual process operating performance,so as to make the production process in the best operating performance as far as *** process data is strongly nonlinear due to the change of system state caused by the change of machine performance or operation *** this time,the relationship between the local nearest neighbor samples is ***,the existing methods of operating performance assessment for solving nonlinear problems only focus on the global structure of data,i.e.,the variance,while ignoring the local neighborhood structure in each performance ***,a new method called kernel locally linear embedding PLS(KLLEPLS) is developed and applied for operating performance *** includes offline modeling,online evaluation and non-optimal cause *** algorithm not only has concise expression and similar analytical solutions for PLS,but also retains the original geometric structure of strongly nonlinear data when extracting the maximum correlation information between process ***,the practice and effectiveness of the proposed method are demonstrated by the dense medium coal preparation process and Tennessee Eastman(TE) process.

关键词： Complex industrial process operating performance assessment kernel locally linear embedding PLS non-optimal cause identification

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Lexicographic Multi-objective Order Picking Optimization for Robotic Mobile Fulfillment Systems

Lexicographic Multi-objective Order Picking Optimization for...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hanying Wang Ziyan Zhao Jiaqi Liang Xingyang Li Shixin Liu College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University Foshan China Département de mathématiques et génie industriel Polytechnique Montréal Montréal Qc Canada Faculty of Robot Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao Hebei China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In light of advancements in artificial intelligence, the Internet of Things, and mechatronics, robots are increasingly integrated into e-commerce warehouses to enable smart order picking solutions and foster intelligent automation. A robot-assisted order picking process revolutionizes the traditional labor-intensive person-to-goods order picking technology, leading to a goods-to-person (G2P) smart warehouse. Within it, robots transport pods to predefined picking stations, where human pickers retrieve the requested goods from these pods to fulfill customer orders. The allocation of pods to robots and the scheduling of picking operations are key optimization issues in G2P order picking systems. Although they play a key role in improving operational efficiency, existing research has paid limited attention to their joint optimization. This study considers a lexicographic multi-objective optimization problem to shorten the order picking cycles under the premise of optimizing the picking efficiency evaluated by makespan. We build a mixed integer program for the newly proposed problem and develop a matheuristic algorithm by integrating a commodity-order model into a metaheuristic algorithm to solve it. Experimental results show that the proposed method can significantly shorten the total order picking cycles while keeping the minimum makespan. It outperforms a recent state-of-the-art algorithm. This work emphasizes the importance of joint optimization within G2P smart warehouses and reveals the high potential of the proposed method to be used in practice.

关键词： Intelligent automation Adaptation models Mechatronics Metaheuristics Search problems Scheduling Resource management Internet of Things Electronic commerce Robots

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying 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 static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-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(T1-c). Moreover, if the loss functions are strongly convex, then static 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 tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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Linear parameter varying control of wind energy conversion systems in partial load 19

Linear parameter varying control of wind energy conversion s...

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19th International Conference on Machine Learning and Cybernetics, ICMLC 2020

作者： Xu, Jing Sun, Qing East China University of Science and Technology Ministry of Education The Key Laboratory of Advanced Control and Optimization for Chemical Process Shanghai200237 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China

ISBN: (纸本)9780738124261

This paper is dealt with the rotor speed tracking problem of variable-speed wind turbine systems operating under the partial load condition. Singular perturbation techniques are used to characterize the two-time-scale property of the wind turbine system, and a linear parameter varying (LPV) model is formed to approximate nonlinear behaviours of a wind turbine system. Based on the slow-fast decomposition method, slow and fast subsystems are constructed: one for the mechanical dynamics and the other for the electrical dynamics. Slow and fast controls are derived, respectively, and then a local state feedback controller, sum of the slow and fast control, is formulated. A design procedure, using the linear parameter varying control to combine local controllers, is proposed to guarantee the robustness of the closed-loop nonlinear wind turbine system. Numerical examples are given to show the validity of the proposed control scheme. © 2020 IEEE.

关键词： Wind turbines

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Deep Graph Network for process Soft Sensor Development

Deep Graph Network for Process Soft Sensor Development

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

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

ISBN: (纸本)9781665402460

In the (bio)chemical processes, traditional hardware sensors are difficult to directly measure the quality of critical products due to their time-varying, non-linear, and dynamic characteristics. This makes process soft sensor modeling methods important. Since the process variables can be regarded as natural graph data, this work introduces graphs in the soft sensor modeling area. A soft sensor model based on the graph neural network (GNN) is proposed. The model can learn the topological structure of graph data between each unit variable. Moreover, it characterizes variable relationships from the spatial and temporal dimensions to the output prediction by introducing the spatial-temporal convolutional layer. The effectiveness and advantages of the GNN-based soft sensor model are verified using a simulated fermentation process.

关键词： Soft sensors Computational modeling Predictive models Sensor phenomena and characterization Data models Product design Hardware

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Enhanced Soft Sensor with Qualified Augmented Data Using Centroid Measurement Criterion

Enhanced Soft Sensor with Qualified Augmented Data Using Cen...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Yun Dai Qing Yu Tao Yang Yuan Yao Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9781665402460

Development of reliable soft sensors using limited labeled samples is not an easy task in industrial processes. A selective generative adversarial network (SGAN)-based support vector regression (SGAN-SVR) soft sensor is proposed for quality prediction using limited labeled training data. Specifically, SVR is considered as a base prediction model. The Wasserstein GAN (WGAN) is adopted to capture the distribution of available labeled data and generate virtual candidates. Subsequently, using a proposed similarity measurement strategy, those synthetic data with more information are selected and introduced into the training set. Using the designed data augmentation approach, the SGAN-SVR model can achieve better prediction performance compared with the SVR soft sensor. The quality prediction results on an industrial polyethylene process demonstrate the effectiveness and advantages of the proposed method.

关键词： Training Support vector machines Polyethylene Filtering Soft sensors Training data Predictive models

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Operating Performance Assessment of Plant-Wide processes Based on Hierarchical Multiblock Performance-Relevant Kernel Independent Component Analysis

Operating Performance Assessment of Plant-Wide Processes Bas...

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第40届中国控制会议

作者： Yan Liu Fei Chu Fuli Wang Dakuo He College of Information Science&Engineering Northeastern University College of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

The operating performance assessment of plant-wide processes is very important for improving product quality and comprehensive economic *** this study,a new operating performance assessment method based on hierarchical multiblock performance-relevant kernel independent component analysis(HMPRKICA) is proposed for nonlinear and non-Gaussian plant-wide *** proposed approach divides the process variables into several sub-processes according to the physical structure and incorporates sub-process information into high level for operating performance assessment of plant-wide *** the new method,the difficulty of process analysis is reduced and the interpretability of the assessment results are *** process operating performance is nonoptimal,the responsible sub-process is first located by the sub-process assessment result,and then the variable contribution rates are calculated in the corresponding sub-process to narrow the search for the nonoptimal ***,a case study of gold hydrometallurgy process evaluates the efficiency of the proposed method.

关键词： Plant-wide process operating performance assessment HMPRKICA non-Gaussianity nonlinearity

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids with Coupled Electricity-Hydrogen-Ammonia under Uncertainties

arXiv

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arXiv 2024年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2024, CC BY.

关键词： Ammonia

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