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Multi-objective Optimal Scheduling of Stacker–reclaimers Using the RPC-NSGA II Algorithm

Recent Patents on Mechanical engineering 2023年第3期16卷 222-234页

作者： Yi, Lingzhi Liu, Qiankun Wang, Yahui Zhang, Huiting Peng, Xinlong College of Automation and Electronic Engineering Xiangtan University & Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan411105 China College of Electrical and Information Engineering Hunan University Hunan Changsha410082 China

Background: The stacker-reclaimer is a device for transporting bulk materials in ironmaking raw material yards. An excellent scheduling plan can provide a good raw material supply basis for steel enterprises. It is of great significance to improve the efficiency of steel production, reduce unnecessary operating waste and management costs, and realize scientific management of steel production. Objective: This patent aims to optimize the total material transportation time and equipment utilization balance within a single operation plan of the stacker-reclaimer involved in the raw material yard. Methods: A multi-objective optimization model for the stacker reclaimer is established, and the Reverse learning and Population Competitive-NSGA II (RPC-NSGA II) algorithm is introduced for solving. This algorithm uses reverse learning and population competition mechanism to improve the convergence and diversity of the algorithm. Results: The proposed method was experimentally verified in a raw material yard with a 360m2 sintering machine and a bulk material port. The method converges well and obtains a Pareto front with a uniform distribution. Compared with the actual scheduling plan, the scheduling plan under the optimal compromise solution reduces the maximum completion time by 11.23 minutes and increases the equipment utilization balance rate by 11.70%. Conclusion: The proposed method can consider the material transportation time and equipment utilization balance, which is of great significance for the optimized use of the stacker reclaimer in steel enterprises and the quality assurance of raw material supply. © 2023 Bentham Science Publishers.

关键词： Multiobjective optimization

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Data-driven consensus control of fully distributed event-triggered multi-agent systems

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Science China(Information Sciences) 2023年第5期66卷 147-161页

作者： Yifei LI Xin WANG Jian SUN Gang WANG Jie CHEN School of Automation Beijing Institute of Technology The State Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Department of Control Science and Engineering Tongji University

This study investigates the consensus control issue in discrete-time linear multi-agent systems(MASs) using data-driven control under undirected communication networks. To alleviate the communication burden, an adaptive event-triggered control strategy involving only local information is proposed and a model-based stability condition is derived that guarantees the asymptotic consensus of MASs. Furthermore,a data-based consensus condition for unknown MASs is established by combining a data-based system representation with the model-based stability condition, using only pre-collected noisy input-state data instead of the accurate system information a priori. Specifically, both model-based and data-driven event-triggered controllers can be utilized without requiring any global information. The validity and correctness of the controllers and associated theoretical results are demonstrated via numerical simulations.

关键词： distributed control event-triggered control data-driven control discrete-time MASs

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Enhanced UAV Pursuit-Evasion Using Boids Modelling:A Synergistic Integration of Bird Swarm Intelligence and DRL

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Computers, Materials & Continua 2024年第9期80卷 3523-3553页

作者： Weiqiang Jin Xingwu Tian Bohang Shi Biao Zhao Haibin Duan Hao Wu School of Information and Communications Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China China Academy of Electronics and Information Technology China Electronics Technology Group Corporation(CETC)Beijing100041China Department ofAutomatic Control School of Automation Science and Electrical EngineeringBeihangUniversityBeijing100191China

TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and *** address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our *** integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion *** further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic *** also leverage self-play training to continuously optimize the performance of pursuit *** experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each *** simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed *** overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.

关键词： UAV pursuit-evasion swarm intelligence algorithm Boids model deep reinforcement learning self-play training

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Feedback-Based Quantum Algorithm for Constrained Optimization Problems 15th

Feedback-Based Quantum Algorithm for Constrained Optimizati...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Abdul Rahman, Salahuddin Karabacak, Özkan Wisniewski, Rafal Automation and Control Section Department of Electronic Systems Aalborg University Aalborg Denmark Department of Mechatronics Engineering Kadir Has University Istanbul Turkey

ISBN: (纸本)9783031856990

The feedback-based algorithm for quantum optimization (FALQON) has recently been proposed to find ground states of Hamiltonians and solve quadratic unconstrained binary optimization problems. This paper efficiently generalizes FALQON to tackle quadratic constrained binary optimization (QCBO) problems. For this purpose, we introduce a new operator that encodes the problem’s solution as its ground state. Using control theory, we design a quantum control system such that the state converges to the ground state of this operator. When applied to the QCBO problem, we show that our proposed algorithm saves computational resources by reducing the depth of the quantum circuit and can perform better than FALQON. The effectiveness of our proposed algorithm is further illustrated through numerical simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Constrained optimization

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Object recognition and grasp detection in complex scenarios for home service robot

Object recognition and grasp detection in complex scenarios ...

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2024 China automation Congress, CAC 2024

作者： Xu, Shengjun Bai, Liang Zhong, Huangwei Hu, Yuji Sun, Xiangcai Lyu, Hongqiang School of Information and Control Engineering Xi'an University of Architecture and Technology Xi’an China School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9798350368604

With the development of artificial intelligence, home service robots have been widely applied in daily life. As one of the fundamental functions of service robots, object grasping helps improve the level of unmanned and intelligent household. However, traditional robots still face challenges in flexibility and accuracy in complex home environments. In this paper, an object recognition and grasp detection system in complex scenarios for home service robots is proposed. The system consists of four modules: image acquisition, human-machine interaction software, RGB-D image processing, and robot control. The image acquisition module captures RGB and depth images of the work area and sends them to the software. The RGB-D image processing module begins with object recognition to obtain object categories and bounding box with the help of YOLOv8. The target point cloud is produced by combining the target subimage and corresponding depth information, followed by depth completion and plane expansion. Then the processed point cloud is fed into a Graspnet pose estimation network to calculate the 6D grasping pose for service robot. And finally, the robot control module is invoked to carry out the corresponding target grasping action. To evaluate the performance, an experimental platform for home service robot is built to grasp various daily necessities. The results tell that the average accuracy for object recognition reaches up to 98.3%, the success rate for single-target grasping is 94.4%, and the success rate for multi-target grasping can achieve 80%. © 2024 IEEE.

关键词： Image acquisition

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A Real-Time Semantic Segmentation Method Based on Transformer for Autonomous Driving

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Computers, Materials & Continua 2024年第12期81卷 4419-4433页

作者： Weiyu Hao Jingyi Wang Huimin Lu Donald Bren School of Information and Computer Sciences University of CaliforniaIrvineCA 92612USA Department of Control Engineering Kyushu Institute of TechnologyKitakyushu804-8550Japan School of Automation Southeast UniversityNanjing210096China

While traditional Convolutional Neural Network(CNN)-based semantic segmentation methods have proven effective,they often encounter significant computational challenges due to the requirement for dense pixel-level predictions,which complicates real-time *** address this,we introduce an advanced real-time semantic segmentation strategy specifically designed for autonomous driving,utilizing the capabilities of Visual *** leveraging the self-attention mechanism inherent in Visual Transformers,our method enhances global contextual awareness,refining the representation of each pixel in relation to the overall *** enhancement is critical for quickly and accurately interpreting the complex elements within driving sce-narios—a fundamental need for autonomous *** experiments conducted on the DriveSeg autonomous driving dataset indicate that our model surpasses traditional segmentation methods,achieving a significant 4.5%improvement in Mean Intersection over Union(mIoU)while maintaining real-time *** paper not only underscores the potential for optimized semantic segmentation but also establishes a promising direction for real-time processing in autonomous navigation *** work will focus on integrating this technique with other perception modules in autonomous driving to further improve the robustness and efficiency of self-driving perception frameworks,thereby opening new pathways for research and practical applications in scenarios requiring rapid and precise decision-making *** experimentation and adaptation of this model could lead to broader implications for the fields of machine learning and computer vision,particularly in enhancing the interaction between automated systems and their dynamic environments.

关键词： Visual transformer semantic segmentation autonomous driving

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Multi-objective Optimization of the Interior Permanent Magnet Synchronous Motor for Electric Vehicles 6

Multi-objective Optimization of the Interior Permanent Magne...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Huang, Changchuang Kou, Baoqaun Zhao, Xiaokun Harbin Institute of Technology Electrical Engineering and Automation Heilongjiang China Northeast Forestry University Computer and Control Engineering Heilongjiang China

ISBN: (纸本)9798350346671

Permanent magnet synchronous motors (PMSMs) are widely used in various fields due to their high efficiency, high power factor and small volume. In this paper, a segmented permanent magnet interior permanent magnet synchronous motor (IPMSM) is proposed and optimized. First, the topology of the IPMSM is introduced. Second, multi-objective optimization is performed by non-dominated sorting genetic algorithm-II (NSGA-II) using rotor structure parameters as optimization variables, saliency ratio as constraint, electromagnetic torque and constant-power speed range as objective functions. Then, the electromagnetic characteristics of the initial IPMSM and the optimized IPMSM are analyzed and compared. The results show that the torque and constant-power speed range of the optimized motor are improved, which verifies the validity of the optimization method. The proposed motor is suitable for electric vehicles, which require large torque at low speeds and wide constant-power speed range at high speeds. © 2023 IEEE.

关键词： Multiobjective optimization

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Development of Artificial Intelligence Supported Tool for Anomaly Detection in Cloud Computing Systems 4

Development of Artificial Intelligence Supported Tool for An...

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4th International Conference on Electrical, Communication and Computer engineering, ICECCE 2023

作者： Aktas, Gorkem Ipek, Batuhan Konukoglu, Emin Ali Aydin, Yucel Control and Automation Engineering Istanbul Technical University Istanbul Turkey Electronics and Communication Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9798350369694

In the rapidly evolving landscape of Internet of Things (IoT) and Cloud systems, the complexity and scale of these technologies demand innovative solutions for efficient error detection and management. This paper proposes a robust framework for anomaly detection in cloud computing systems, emphasizing the critical role of automation and artificial intelligence (AI). The approach integrates Continuous Integration, Continuous Deployment (CICD) principles with AI-driven anomaly detection techniques to enhance system reliability and minimize risks. The study introduces a novel method employing one-dimensional convolutional layers and Autoencoder architecture to preprocess and analyze system metrics, enabling the development of an adaptive and customized anomaly detection system. The proposed framework is implemented through a Python-based agent, utilizing specific libraries and Continuous Integration/Continuous Deployment (CI/CD) processes executed via Jenkins. The agent is tested and deployed on Kubernetes, demonstrating its effectiveness in dynamic and complex environments. The results highlight the significance of iterative optimization, emphasizing continuous monitoring and adaptation for ensuring the robustness and reliability of cloud computing systems. This research contributes valuable insights into enhancing the efficiency of anomaly detection in large-scale cloud systems, paving the way for further advancements in the field of IoT and cloud technologies. © 2023 IEEE.

关键词： Anomaly detection

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Deep reinforcement learning guidance with impact time control

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Journal of Systems engineering and Electronics 2024年第6期35卷 1594-1603页

作者： LI Guofei LI Shituo LI Bohao WU Yunjie School of Astronautics Northwestern Polytechnical UniversityXi’an 710072China Beijing Institute of Control and Electronic Technology Beijing 100038China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

In consideration of the field-of-view(FOV)angle con-straint,this study focuses on the guidance problem with impact time control.A deep reinforcement learning guidance method is given for the missile to obtain the desired impact time and meet the demand of FOV angle *** basis of the framework of the proportional navigation guidance,an auxiliary control term is supplemented by the distributed deep deterministic policy gradient algorithm,in which the reward functions are developed to decrease the time-to-go error and improve the terminal guid-ance *** numerical simulation demonstrates that the missile governed by the presented deep reinforcement learning guidance law can hit the target successfully at appointed arrival time.

关键词： impact time deep reinforcement learning guidance law field-of-view(FOV)angle deep deterministic policy gradient

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Dynamic load altering attack detection based on adaptive fading Kalman filter in smart grid

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IET Generation, Transmission and Distribution 2024年第2期18卷 303-313页

作者： Li, Jian Sun, Chaowei Yang, Shuxian Su, Qingyu School of Automation Engineering Northeast Electric Power University Jilin Jilin China Jilin Provincial Key Laboratory of Advanced Control Technology of Smart Energy Northeast Electric Power University Jilin Jilin China

This paper mainly studies a detection method of dynamic load altering attacks (D-LAAs) in smart grids. First, communication factors are considered, and a smart grid discrete system model under D-LAA attack is established. Second, for closed-loop D-LAAs, an adaptive fading Kalman filter (AFKF) is designed to estimate the states of smart grids with Gaussian noise in real time, and a Euclidean distance ratio detection algorithm based on AFKF is proposed to detect D-LAAs. Moreover, the proposed detection algorithm can identify D-LAAs even in the presence of noise in the measurement data, significantly enhancing the speed of attack detection. Finally, take a smart grid with three generators and six buses as an example. Its feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified by simulations. The simulations are carried out through the real-time hardware-in-the-loop simulation platform, which is mainly composed of StarSim and multi-tasking devices. © 2024 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Cyber Physical System

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