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Adaptive control Scheme for Clustering of Nodes Based on the Signs of Connections in Dynamical Signed Networks 19th

Adaptive Control Scheme for Clustering of Nodes Based on ...

19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Wang, Qi Wang, Yinhe Gao, Zilin Gao, Peitao Xiong, Jianbin Cen, Jian Gao, Ying School of Automation Guangdong Polytechnic Normal University Guangzhou510665 China School of Automation Guangdong University of Technology Guangzhou510006 China College of Computer Science and Engineering Chongqing Three Gorges University Chongqing404020 China School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou510665 China Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory Guangzhou510665 China School of Computer Science and Engineering South China University of Technology Guagnzhou510641 China

ISBN: (纸本)9783031651229

The concept of clustering has garnered significant attention within the field of complex networks. Numerous clustering algorithms have been documented in the extant academic literature. However, most of them consider static signed networks, which are not suitable for dynamical networks. For dynamical signed networks, there is a class of algorithms called cluster dynamics. Nevertheless, they regard the connection to be time-invariant, which is a constraint. In this paper, for a kind of dynamical signed networks with time-varying connections, we discuss the adaptive control scheme for clustering of nodes, such that the network evolves asymptotically into a classifiable network that contains several friends’ groups with clear sign boundaries. In other words, through the careful design of the controller for the nodes and the establishment of coupling relationships between the nodes and their connections, we ensure that the connection of the dynamical signed network can approximate asymptotically the connection of a given classifiable network, as measured by the concept of uniformly ultimately bounded (UUB). Finally, the simulation is used to illustrate the validity of the method proposed in this paper. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Adaptive control Classifiable networks Multi-clustering

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Differentiator-Based Adaptive H∞ Tracking control of Fully Actuated systems

Differentiator-Based Adaptive H∞ Tracking Control of Fully ...

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Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Yuxin Feng Zhiqiang Li Yang Liu Zhaoshui He Hongyi Li School of Automation and Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China School of Automation and Electronics Engineering Qingdao University of Science and Technology Qingdao China School of Automation Guangdong University of Technology Guangzhou China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H ∞ technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study.

关键词： Backstepping Manipulators Stability analysis Closed loop systems Adaptive control Lyapunov methods

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Model predictive control of event triggering in silicon single crystal growth processes

Model predictive control of event triggering in silicon sing...

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Chinese Automation Congress (CAC)

作者： Yankai Li Biao Ren Ding Liu Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

In this paper, the control issue of the semiconductor silicon monocrystalline growth process is studied through an event triggered model prediction approach. Firstly, a state-space model of silicon monocrystalline growth process is developed to describe the energy conversion and the dynamics and the geometry of the isodiametric stage silicon monocrystalline via the Czochralski method. Secondly, a small perturbation linearization is used to obtain a linear continuous model, which is translated into a linear discrete model under zero-order retainers. Finally, an event triggered model prediction controller is established to decrease the calculation of model prediction in the rolling optimization process. Based on experimental results, the event-triggered model prediction control algorithm is confirmed to be an effectively control scheme for the silicon monocrystalline growth process.

关键词： Crystal growth Semiconductor device modeling Computational modeling Photonic crystals Predictive models Prediction algorithms Silicon

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Scheduling optimization of silicon single crystal production process based on adaptive improved particle swarm optimization

Scheduling optimization of silicon single crystal production...

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2021 China Automation Congress, CAC 2021

作者： Kang, Lu Liu, Ding Wu, Yali Ping, Guozheng School of Automation and Information Engineering Xi'an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China School of Automation and Information Engineering Xi'an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China School of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China

ISBN: (纸本)9781665426473

Aiming at the potential safety hazards of silicon single crystal production enterprises in the process of multi-furnace silicon single crystal production, and considering the maximum power load requirements in actual enterprises, a silicon single crystal production process scheduling model with the goal of minimizing the maximum completion time was established. Based on this model, an adaptive improved particle swarm optimization algorithm (Adaptive GPSO) is proposed to solve it. The algorithm retains the individual optimal value and global optimal value of the particle swarm algorithm, and introduces the crossover operation and mutation operation in the genetic algorithm to improve the new individual generation mechanism. At the same time, it performs dynamic iterative calculations on the mutation probability and crossover probability to avoid the algorithm fall into the local optimum, and improve the diversity and convergence of the algorithm. By solving different scales scheduling problems of silicon single crystal production process, the experiments on real-world data shows that the feasibility and effectiveness of the proposed algorithm. © 2021 IEEE

关键词： Genetic algorithms

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A 4-Channel Ultrafast Wavelength-Swept REC-DFB Laser Array for Fiber Bragg Grating Interrogation

A 4-Channel Ultrafast Wavelength-Swept REC-DFB Laser Array f...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Meng, Lingxin Dai, Pan Ban, Qilu Xu, Kaichuan Wang, Jiacheng Li, Zhen Wang, Feng Zeng, Jie Li, Shaobo Ma, Xiang Chen, Xiangfei Res. Center of Precision Photonics Integration and System Application of the Ministry of Education College of Engineering and Applied Sciences Nanjing University Key Lab. of Intelligent Optical Sensing and Manipulation of the Ministry of Education Engineering National Laboratory of Solid State Microstructures Nanjing210023 China Nanjing University of Aeronautics and Astronautics State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing210016 China The 54th Research Institute of China Electronics Technology Group Corporation Optical Communication Research and Development Center Shijiazhuang050081 China

ISBN: (纸本)9798350312614

We present a four-channel ultrafast wavelength-swept distributed feedback (DFB) semiconductor laser array based on the reconstruction-equivalent-chirp (REC) technique using instantaneous injection current modulation. With the aid of our designed laser driving circuit system consists of the microcontroller unit (MCU), field-programmable gate array (FPGA) and negative metal-oxide-semiconductor field effect transistors (n-MOSFETs), the precise pulse driving current for the 4-channel wavelength-swept REC-DFB laser array can be generated and a fast tuning of 16.424 nm within 10 μs is achieved. To further investigate the tuning performance of the proposed wavelength-swept laser source, we then applied it to the fiber Bragg grating (FBG) interrogation system and four FBG temperature sensor arrays were successfully demodulated. The development of high-efficient optical sensing systems heavily relies on the significance of the ultrafast wavelength-swept laser source, which has the potential to greatly enhance FBG interrogator performance in the future. © 2023 IEEE.

关键词： DFB laser array fast wavelength-swept fiber Bragg grating interrogation REC technique

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An Improved Ship Classification Method Based on YOLOv7 Model with Attention Mechanism

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Cen, Jian Feng, Hao Liu, Xi Hu, Yongjian Li, Haoliang Li, Haisheng Huang, Weisheng School of Automation Guangdong Polytechnic Normal University China Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory China School of Electronic and Information Engineering South China University of Technology China Department of Electrical Engineering City University of Hong Kong Hong Kong Hong Kong Guangdong Xixun Intelligent Technology Co. Ltd. China

Deep learning (DL) is widely used in ship detection, but there are still some problems in the effective classification, such as inaccurate object feature extraction and inconspicuous feature information in deep layers. To address these problems, we propose a YOLOv7-residual convolutional block attention module (YOLOv7-RCBAM) by combining the convolutional attention mechanism and residual connections to the YOLOv7. First, to accelerate the training speed, the parameters in the backbone network of the pretrained model are frozen by using transfer learning, and the model is fine-tuned for training. Second, to enhance the information relevance of channel dimensional features, an attention mechanism with residual connectivity is adopted. Finally, a feature fusion attention mechanism is introduced to improve the effective feature extraction. The effectiveness of the proposed method is fully validated on the SeaShips dataset. The results show that the YOLOv7-RCBAM model achieves better performance with a 97.59% in mAP and effectively extracts object feature in deep layers. Meanwhile, the YOLOv7-RCBAM model can accurately locate ship in complex environments with darkness and noise with the mAP reaching 96.13% to achieve effective ship classification detection. © 2023 Jian Cen et al.

关键词： Feature extraction

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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-Objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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Chinese control Conference (CCC)

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China System Integration and Industrial Robotics Laboratory Wuhan P. R. China

The rational design of weighting factors in the cost function for finite control set model predictive torque control (FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor (PMSM) based on the adaptive multi-objective black hole algorithm (AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multi-objective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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Chinese control Conference (CCC)

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China System Integration and Industrial Robotics Laboratory Wuhan P. R. China

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network (TL-DNN) was proposed in this paper. Its specific implementation steps include (1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor; (2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models (random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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Deep Reinforcement Learning Based HVAC Optimization control Algorithm Application

Deep Reinforcement Learning Based HVAC Optimization Control ...

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Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Linzhe Zeng Jian Cen Xi Liu Yinbo Wu Xiaowei Tan Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory School of Automation Guangdong Polytechnic Normal University Guangzhou China SINRO Air-Conditioning (Fogang) Co. Ltd Guangdong Sinro Air-conditioning Technology Co. Ltd Guangzhou China

To address the shortcomings of Heating, Ventilation, and Air Conditioning systems (HVAC) with low energy efficiency, this paper introduces the application and effectiveness of deep reinforcement learning in HVAC systems. Deep reinforcement learning mainly includes model-based algorithms and model-free algorithms. Model-based algorithms require a large amount of system and environment knowledge, which is usually difficult to obtain, while model-free algorithms do not need this knowledge to model the environment, so model-free deep reinforcement learning is of high research value. The applications of model-free deep reinforcement learning algorithms in the HVAC domain are reviewed and divided into three categories: value function-based methods, policy gradient-based methods, and actor-critic-based methods, and the three categories are also described in detail. Finally, the shortcomings of current deep reinforcement learning in HVAC control systems are summarized, and the future application directions are prospected.

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Hierarchy-based extended CBBA for multi-robot task assignment

Hierarchy-based extended CBBA for multi-robot task assignmen...

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Chinese Automation Congress (CAC)

作者： Jiajun Liu Weiguo Xia Fuqiang Li Peng Wang China National Aeronautical Radio Electronic Research Institute and National Key Laboratory of Science and Technology on Avionics System Integration China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology China

This paper studies the task assignment problem of a multi-robot system with complex constraints including the robot speed limit, the task execution time window constraints, the arrival time, the trip cost, and different priorities of the tasks. A new bidding function is then designed for each robot. Based on the k-means algorithm, a hierarchy-based extended consensus-based bundle algorithm (CBBA) is proposed to find the optimal solution for the task allocation problem. Finally, simulation examples are presented to show the feasibility of the proposed algorithm and comparisons with existing algorithms show that the proposed algorithm can reduce the task assignment time with guaranteed reward values.

关键词： Costs Automation Heuristic algorithms Numerical simulation Time factors Resource management Multi-robot systems

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