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A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

Energy and AI 2024年第4期18卷 368-379页

作者： Weipeng Li Yuting Chong Xin Guo Jun Liu School of Automation and Information Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an710048ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing No.5 Jinhua South RoadXi’an710048ShaanxiChina

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power *** data-driven forecasting methods are regarded as an effective ***,the inherent randomness and nonlinearity of wind power systems,along with the abundance of redundant information in measurement data,present challenges to forecasting *** integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced driven data-forecasting *** on the seasonal variation characteristics of wind energy,a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is *** effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

关键词： Wind power prediction Data-driven Hybrid model Seasonal feature decomposition Gated recurrent networks Attention mechanism

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A robust internal model control design for low-quality waste heat power generation systems

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 7969-7987页

作者： Xiao, Yanjun Li, Shifang Zhang, Kun Zhang, Yameng Xiao, Yanchun School of Mechanical Engineering Hebei University of Technology Tianjin China Career Leader Intelligent Control Automation Company Jiangsu Province Suqian China Tianjin Key Lab Power Transmiss & Safety Technol Dept State Key Lab Reliabil & Intellectual Elect Tianjin China

Recovering low-quality waste heat using industrial waste heat is challenging, and the reuse technology needs to erupt. Moreover, the gas source of low-quality waste heat is relatively volatile, which makes it challenging to keep the actual working condition of the plant stable. Therefore, it is inspiring to research the robustness of root-waste heat power generation processed measurement and control system to improve the stability of the plant operation. Hence, in this paper, we have applied uncertainty theory to analyze it and formulate the uncertainty model based on the Bode diagram. We also proposed a control method based on the uncertainty model, which combines robust control and internal model control to make the roots waste heat power generation system operate stably under the effect of external disturbances and changes of internal structure or parameters in actual operation. Experimental results show that the robust internal model control method has a speed deviation of no more than 7.9 r/min compared with the PID control method. The adjustment time to track the set value does not exceed 73.1 seconds within the allowed fluctuation range. The fluctuation variance is 30.95%of that of the PID controller. The dynamic performance is better, with strong anti-interference capability and significantly improved tracking performance. It ensures the stability of the roots-type waste heat utilization system, which is essential for future intelligent grid-connected power generation. © 2024 – IOS Press.

关键词： Waste heat

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Deep learning-based digital twin for intelligent predictive maintenance of rapier loom

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9409-9430页

作者： Xiao, Yanjun Li, Rui Zhao, Yue Wang, Xiaoliang Liu, Weiling Peng, Kai Wan, Feng School of Mechanical Engineering Hebei University of Technology Tianjin China Career Leader Intelligent Control Automation Company Jiangsu Province Suqian China Tianjin Key Lab Power Transmiss & Safety Technol Department State Key Lab Reliabil & Intellectual Elect Tianjin China

The rapier loom works in a complex environment and operates at high speeds. It is inevitable that its performance will deteriorate during the production process, which in turn will cause faults. The development of maintenance has undergone the transition from "regular maintenance" and "post-event maintenance" to "predictive maintenance". In order to achieve the synergistic optimization goal of ensuring operational safety and reducing operational costs, a predictive maintenance method driven by the fusion of digital twin and deep learning is proposed based on the idea of "combining the real with the virtual and controlling the real". Firstly, a digital twin system structure model of rapier weaving machine is constructed, and the overall architecture of digital twin is proposed according to the full operation cycle of rapier weaving machine. Then, the digital twin-driven process parameter evaluation and prediction and health state evaluation and prediction are investigated separately. In order to achieve the evaluation and prediction of process parameters to ensure the efficiency of weaving machine operation, the prediction method of IWOA optimized BP neural network driven by twin data is proposed and the model is updated and optimized based on the martingale distance approach. In order to achieve health state assessment and prediction, we use health index as an evaluation index to characterize the health condition of spindles, and use BiLSTM network to achieve prediction of remaining spindle life and then make maintenance decisions. The results show that there are greater advantages to combining deep learning and digital twin technology for intelligent predictive maintenance of rapier loom. © 2024 – IOS Press.

关键词： Forecasting

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Dynamic Blockchain-Empowered Trustworthy End-Edge Collaborative Computing via Rotating Multi-Agent DRL

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4864-4878页

作者： Xu, Chi Zhang, Peifeng Yu, Haibin Li, Yonghui Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China University of Chinese Academy of Sciences Beijing China University of Sydney School of Electrical and Information Engineering Sydney Australia

Blockchain-empowered end-edge collaborative computing is a promising technology for enhancing the timeliness and trustworthiness of Industrial Internet of Things (IIoT). However, integrating task offloading with blockchain consensus inevitably escalates resource consumption across communication, computation, and energy domains. Thus, the joint optimization of task offloading, resource allocation and blockchain consensus is very important for IIoT. This paper studies a general end-edge collaborative computing scenario with multiple end devices and multiple edge servers. We first propose a novel dynamic blockchain (DBC) scheme by developing a dynamic leader election mechanism and designing a dynamic consensus waiting time window. Then, by fully considering the constraints of multi-task size and deadline, communication bandwidth, computing frequency, battery capacity, Byzantine fault tolerant and trustworthiness, we formulate the trustworthy processing efficiency (TPE) maximization problem with respect to end-edge task division, communication and computation resource allocation, leader election and consensus waiting window. To address this problem, we transform it into a Markov decision process and design a compound reward by fully considering the penalty for computing timeout and consensus failure. After that, we propose a rotating multi-agent deep reinforcement learning (R-MADRL) algorithm tailored to the proposed DBC scheme, where an entropy-based dual-critic DRL algorithm is proposed for rotating multi-agent training and decentralized execution. Extensive experiments validate the effectiveness and superiority of the proposed DBC with R-MADRL, where three benchmark DRL algorithms and three blockchain consensus schemes are compared. The results demonstrate that R-MADRL achieves stable convergence with more than 60.32% TPE reward than other algorithms while the task timeout ratio of DBC is reduced by more than 66.49% compared with other schemes. © 2025 Institute of El

关键词： Markov processes

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Interleaved Flyback Photovoltaic Microinverters with PI and Repetitive control

Interleaved Flyback Photovoltaic Microinverters with PI and ...

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2024 CPSS and IEEE International Symposium on Energy Storage and Conversion, CPSS and ISESC 2024

作者： Tian, Minwei Zeng, Hanchao Zheng, Xiangrong Zhang, Damin Zheng, Xueqin School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen University of Technology Xiamen China

ISBN: (纸本)9798350380514

The grid-connected current output from a flyback photovoltaic microinverter is prone to distortion due to nonlinear factors such as switching tube dead zones, driving asym-metry and grid voltage disturbances. Aiming at the traditional PI control which cannot effectively improve the influence of nonlinear factors, this paper proposes an interleaved flyback photovoltaic microinverter based on PI and repetitive control, and in-depth research on the circuit topology, control strategy and the design method of the PI and repetitive controller of this inverter is carried out. Simulation results show that the Photo-voltaic micro-reverse grid-connected current with PI and repet-itive control is of high quality, with better dynamic performance and anti-interference capability, while realizing the maximum power point tracking. © 2024 IEEE.

关键词： Electric power transmission networks

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Robust H∞ Consensus control for Heterogeneous Multi-agent Systems Based on Time-delay and Switching Topology 4

Robust H∞ Consensus Control for Heterogeneous Multi-agent S...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Du, Jiayi Liu, Jia Cao, Runxi Zhang, Yijing Zhang, Yunxi Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350363586

This study proposes a consensus control strategy for heterogeneous multi-agent systems characterized by uncertain parameters and external disturbances, to address the intricate robust control challenges posed by time-varying delays and dynamic network topologies. A distributed control protocol is design to make the states of the heterogeneous multi-agent system be consistent. Then the stability of the control protocol is analyzed by Lyapunov-Krasovskii stability theory and integral inequality techniques, and the sufficient conditions for the system to achieve robust H∞ consistency are obtained. Finally, the results of the simulations verify the accuracy of the theoretical analysis presented in this paper. © 2024 IEEE.

关键词： Multi agent systems

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Trajectory control of Multi-Axis Robotic Arms Based on Physics-Informed Neural Networks and Nonlinear Model Predictive control

Trajectory Control of Multi-Axis Robotic Arms Based on Physi...

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2024 China automation Congress, CAC 2024

作者： Wang, Yiqing Xia, Changqing Jin, Xi Xu, Chi Zhang, Yanzhu School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350368604

Multi-axis robotic arms are extensively utilized in intelligent manufacturing scenarios, with trajectory control in flexible scenarios constituting a primary challenge. Physics-Informed Neural Networks (PINNs) represent advanced methods that integrate physical laws with data-driven approaches. Despite their outstanding performance in integrating theory and data, research on their application for controlling robotic arms in complex scenarios remains limited. This paper establishes a nonlinear dynamic model based on the physical relationships between the axes of robotic arms. In the absence of prior data, Sobol sequence random sampling is employed to generate data, which are subsequently trained using PINNs. Considering system noise, the Extended Kalman Filter (EKF) is utilized to predict the next state in noisy environments, and Nonlinear Model Predictive control (NMPC) is implemented to control the robotic arm for trajectory tracking, achieving real-time control. Simulation results demonstrate that the proposed Discrete Physics-Informed Predictive control (DPIPC) method exhibits smaller position and velocity errors with less fluctuation compared to the method in the reference, indicating superior control capabilities. © 2024 IEEE.

关键词： Extended Kalman filters

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Research on SRM control strategy for looms based on multi-Algorithm fusion

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 7933-7957页

作者： Xiao, Yanjun Pei, Eryue Shi, Linhan Peng, Kai Liu, Weiling School of Mechanical Engineering Hebei University of Technology Tianjin China Career Leader Intelligent Control Automation Company Jiangsu Province Suqian China Tianjin Key Lab Power Transmiss Safety Technol Dept State Key Lab Reliabil Intellectual Elect Tianjin China

In order to solve the problem that Switched Reluctance Motor (SRM) generates torque pulsation phenomenon during operation, which reduces the stability of loom spindle operation, this paper proposes and designs a multi-Algorithm fusion-based SRM control strategy from the point of view of control strategy research. Combined with the operating characteristics of the loom, the causes of SRM torque pulsation are analyzed from the point of view of SRM control strategy, and combined with the spindle control indexes, the voltage chopper control and torque distribution function are introduced to construct the SRM control strategy scheme for the loom. On this basis, an optimization strategy based on the fusion of fuzzy control algorithm, particle swarm algorithm and simulated annealing algorithm is proposed to optimize the torque distribution function, and the algorithmic process of SRM control strategy is verified through comparative tests. The results show that the control strategy can make its torque pulsation reduced to less than 10%, the speed rise time is less than 0.1s, and the relative error of the speed is less than 0.05%, which meets the index requirements of the spindle drive. This proves that the SRM torque pulsation can be reduced by the multi-Algorithm fusion control strategy without increasing the hardware cost, which provides a useful reference for solving the SRM torque pulsation problem under the requirement of low cost. © 2024-IOS Press. All rights reserved.

关键词： Distribution functions

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Autoregressive Minimum Entropy Deconvolution Enhanced Resonance Features for Fault Diagnosis of Wind Turbine Gearbox

Autoregressive Minimum Entropy Deconvolution Enhanced Resona...

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2023 International Conference on automation, control and Electronics engineering, CACEE 2023

作者： Shi, Xiaoyu Sun, Kang Henan Polytechnic University School of Electrical Engineering and Automation Jiaozuo China Henan Polytechnic University Dianrong Intelligent Technology Co. Ltd. School of Electrical Engineering and Automation Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment Jiaozuo China

ISBN: (纸本)9798350309201

Addressing the problem that the pulse component often submerges in other frequency components during the failure of the wind turbine gearbox, it is difficult to extract effectively the early fault characteristics. This paper proposed autoregressive minimum entropy deconvolution enhanced resonance features for wind turbine gearbox fault diagnosis. The method is combined with 1DCNN to achieve high-accuracy fault diagnosis. Firstly, the resonance-based sparse signal decomposition was utilized to decompose the vibration signals into a high resonance component containing noise and harmonic components and a low resonance component containing fault impulse components. Secondly, the AR-MED was employed to enhance the weak impulse features in the low resonance component, thus further enhancing the periodic impulse components of the gearbox. Finally, a feature enhanced 1DCNN was constructed to fuse the decomposed harmonic and periodic impulse components for feature integration, targeted training, and classification. The outcomes of the experiment indicated the efficacy and supremacy of the suggested approach in extracting information about fault characteristics and enhancing the accuracy of fault diagnosis in wind turbine gearbox systems in contrast to current fault diagnosis models. © 2023 IEEE.

关键词： Failure analysis

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Path Tracking Motion control Method Of Tracked Robot Based On Improved LQR control 41

Path Tracking Motion Control Method Of Tracked Robot Based O...

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第41届中国控制会议

作者： Jianyun Ni Yong Wang Hao Li Helei Du Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems School of Electronic Engineering and Automation Tianjin University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In order to enhance the trajectory tracking control performance of tracked mobile robot,an improved LQR lateral motion control method is ***,the robot tracking error kinematics model is constructed by using Lagrange function method,and the linear quadratic regulator controller(LQR) and feedforward controller of the tracked mobile robot are ***,the parameters of LQR controller are analyzed,the Q and R weight coefficient matrix of LQR controller is optimized,and the fast rolling optimization algorithm of lateral tracking control based on path tracking error is *** this way,the improvement of the LQR controller is realized,and the adaptability and control accuracy of the controller and the dynamic performance of the system are ***,test the designed controller through Matlab/Simulink *** simulation results show that the improved LQR controller can track the target path well,and can control the distance deviation and heading deviation in a small range.

关键词： Tracked Mobile Robot LQR Path Tracking Motion control

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