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2022 6th International Conference on Artificial Intelligence, automation and Control Technologies, AIACT 2022

作者： Zhao, Bingjie Liu, Yang Jia, Danping Zhang, Tao Zhang, Hualiang Yan, Hongyu Lu, Xu School Of Artificial Intelligence Shenyang University Of Technology Shenyang China Key Laboratory Of Networked Control Systems Chinese Academy Of Sciences Shenyang China Shenyang Institute Of Automation Chinese Academy Of Sciences Shenyang China Institutes For Robotics And Intelligent Manufacturing Chinese Academy Of Sciences Shenyang China

The requirements of modern industry on the response speed and accuracy of voice coil motors has been the gradual growth, and PID control algorithms have become increasingly unable to meet their requirements. The use of sliding mode control can speed up the response time of the motor, and it also can improve the response speed, overshoot and instability problems in some degree. However, the sliding mode control algorithm will bring jitter to the entire system. This paper combines fuzzy control and sliding mode control to adjust the parameters of the sliding mode control algorithm in real time, which greatly reduces the jitter problem caused by the sliding mode. According to the actual parameters of the voice coil motor, the transfer function of the voice coil motor is deduced, a simulation model of fuzzy sliding mode control is built, and the sliding mode control and fuzzy sliding mode control algorithms are compared. Finally, two signals of step and sine are used as inputs for simulation model. It can be seen through the simulation waveform that the fuzzy sliding mode control algorithm can improve the response speed of the control system and reduce the tracking error. © Published under licence by IOP Publishing Ltd.

关键词： Sliding mode control

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Semantic-Based Reasoning Engine for Service Orchestration in Mass Customization Manufacturing 4

Semantic-Based Reasoning Engine for Service Orchestration in...

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2022 4th International Conference on Electronic Engineering and Informatics, EEI 2022

作者： Liu, Yang Zhang, Tianshi Zhao, Yanling Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110000 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Tsinghua University Beijing100084 China

ISBN: (纸本)9783800759231

In the mass customization manufacturing environment, manufacturing companies can connect to the customers directly via Internet and receive users manufacturing request to adjust the production resource and patterns. In this new situation, the allocation of manufacturing resources and the adjustment of production configuration should have the capability of timely responsing the demands from a large amount of customers. Therefore, efficient matching and processing of dynamic data from customer requirement and customized rules become increasingly important. This paper presents a semantic-based reasoning engine (hereinafter referred to as SBRE) in improving performance of rule engine. The paper uses a BP neural network to classify customer parameters from users requirements based on the semantic web interface. In this stage, for the customer parameters, in order to find a suitable rule engine, a semantic matching method is proposed, which can greatly mitigate complexity and improve the efficiency of matching compared with traditional matching process. The experiment results show that the method can be performed efficiently and flexibly with high data throughput. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Data handling

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State-of-Charge (SOC) Estimation of Lithium-Ion Battery Based on Unscented Kalman Filtering

State-of-Charge (SOC) Estimation of Lithium-Ion Battery Base...

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IEEE Advanced Information Management,Communicates,Electronic and automation Control Conference (IMCEC)

作者： Mengfan Miao Ligang Li Zhongfeng Wang Jing Jiang College of Automation and Electrical Engineering Shenyang Ligong University Liaoning China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

Real-time estimation of State of Charge (SOC) for lithium-ion batteries is one of the core technologies in Battery Management systems (BMS). Accurate SOC estimation is fundamental for managing lithium-ion batteries. In this study, a second-order RC equivalent circuit model is established, and parameter identification methods are employed to determine the model parameters. The identified parameters are then incorporated back into the second-order RC model. The accuracy of these parameters and the model is validated through Hybrid Pulse Power Characteristic (HPPC) tests. Finally, the Unscented Kalman Filtering algorithm (UKF) is utilized to estimate battery SOC. Experimental results demonstrate that SOC estimation based on UKF exhibits high precision and stability.

关键词： Lithium-ion batteries Accuracy Parameter estimation Simulation Estimation Data models Stability analysis

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Improved YOLOv5-based Method for Recognition of Insulator String Pins 12

Improved YOLOv5-based Method for Recognition of Insulator St...

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12th International Conference on CYBER Technology in automation, Control, and intelligent systems, CYBER 2022

作者： Du, Jingbo Jiang, Yong Shenyang Ligong University Shenyang110158 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Aiming at the problem that the substation insulator replacement robot cannot accurately identify insulator pins in the complex outdoor light environment, an improved YOLOv5-based insulator pin identification method YOLOv5-CBP is proposed. For the substation insulator pin recognition task, the backbone of the network is replaced by the lightweight EfficientNet-lite0;the Neck of the network is optimized and a more efficient feature fusion module BiFPN combined with an attention mechanism is introduced. The bounding box regression loss function was changed to improve the accuracy of inspection frame detection. Finally, the improved algorithm is compared with the original YOLOv5 algorithm on a self-built dataset for experiments. The results show that the insulator pin recognition accuracy is improved by 8.3%, the size of the algorithm model is reduced to 54% of the original, and the number of parameters is reduced by 49.4%. The improved algorithm model more lightweight. © 2022 IEEE.

关键词： Degradation Substations Inspection Insulators Control systems Pins Neck

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Research on Industrial Control Network Security Measurement Method Based on GRU-SVM

Research on Industrial Control Network Security Measurement ...

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2022 Chinese automation Congress, CAC 2022

作者： Chen, Hengfei Liu, Xianda Sheng, Chuan Pan, Hao College of Information Engineering Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665465335

Since the industrial Internet entered people's field of vision, industrial control network security has gradually received attention. Traditional network security measurement methods rely on the accuracy of situation sequences and historical situation values, and various factors in the network have a great influence on the results. However, compared with industrial control networks, the number and types of affected factors are less than those of the modern Internet. Therefore, based on the research on network security situational awareness, this paper proposes an industrial control network security measurement method model based on GRU-SVM. First, the data sets with threat type labels are classified, and then the similarity scoring mechanism and softmax function are introduced, to process the classification results. For the GRU-SVM classification module, the analysis of the simulation results shows that the convergence is better and the robustness is stronger. For the safety prediction measurement part, by comparing with different models, the mean absolute error and root mean square error are smaller and the reliability is higher. © 2022 IEEE.

关键词： Network security

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A New Recursive Composite Adaptive Controller for Robot Manipulators

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Space(Science & Technology) 2021年第1期2021卷 77-83页

作者： Jianfei Li Yaobing Wang Zhiyong Liu Xin Jing Chengwei Hu The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System EngineeringBeijing 100094China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

In this paper,a new recursive implementation of composite adaptive control for robot manipulators is *** investigate the recursive composite adaptive algorithm and prove the stability directly based on the Newton-Euler equations in matrix form,which,to our knowledge,is the first result on this point in the *** proposed algorithm has an amount of computation O(n),which is less than any existing similar algorithms and can satisfy the computation need of the complicated multidegree *** manipulator of the Chinese Space Station is employed as a simulation example,and the results verify the effectiveness of this proposed recursive algorithm.

关键词： Robot manipulators robot

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UAV Path Planning Based on Simultaneous Optimization of Monitoring Frequency and Security

UAV Path Planning Based on Simultaneous Optimization of Moni...

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第34届中国控制与决策会议

作者： Yifei Shu Yang Chen Mian Hu Huaiyu Wu Xingang Zhao Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

UAVs have great potential when applied to persistent monitoring,but there are still problems such as difficulty in ensuring the monitoring frequency and easy leakage of monitoring path ***,it is necessary to increase the UAV monitoring frequency of targets and the randomness of monitoring paths as much as possible on the premise of covering all monitoring *** response to the above problems,this paper studies the UAV path planning problem of simultaneous optimization of monitoring frequency and path *** the evaluation of monitoring frequency and path security based on monitoring overdue time and path entropy,a mathematical model for UAV path planning is *** improved ant colony algorithm based on the monitoring overdue time(Overdue-aware Ant Colony Optimization,OACO) is designed,and finally the UAV flight trajectory with high monitoring frequency and the high monitoring path security is *** simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node to be accessed,improve the security of the UAV monitoring path,which is of great significance for enhancing monitoring security and preventing intrusion.

关键词： UAV persistent monitoring path planning monitoring frequency overdue time security

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WindowMixer: Intra-Window and Inter-Window Modeling for Time Series Forecasting

arXiv

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arXiv 2024年

作者： Liu, Quangao Li, Ruiqi Jiang, Maowei Yang, Wei Liang, Chen Pang, LongLong Zou, Zhuozhang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shengyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Time series forecasting (TSF) is widely applied in various fields such as economic forecasting, weather prediction, traffic flow analysis, and public health surveillance. The core of TSF is to capture the underlying temporal patterns in the data. However, real-world time series data exhibit complex temporal patterns and may include additional noise, outliers, missing values, etc., making accurate forecasting highly challenging. Current mainstream methods typically naively treat each time point as an isolated entity, attempting to model point-to-point relationships and uncover temporal patterns within the data, which has limitations when dealing with complex time series data. Specifically, point-by-point modeling fails to take advantage of the abundant contextual information in time series data. This results in a reduced ability to capture temporal patterns and an increased susceptibility to noise. To address these issues, we examine temporal variations from a window-based perspective and present the WindowMixer model, which is built on an all-MLP framework. WindowMixer stems from a simple observation: Time series are recorded in a continuous manner, and the information at any given time point relies on the preceding and succeeding time points for a complete representation. WindowMixer decomposes time series into trend and seasonal components via a sequence decomposition and fusion architecture, handling them individually. For the trend component, we employ a straightforward fully connected (FC) layer to make predictions. For the seasonal component, we first introduce a time window at each time point and project each time window to produce window tokens. Then, we apply the Intra-Window-Mixer and Inter-Window-Mixer to further process these tokens. The Intra-Window-Mixer models relationships within each window token, while the Inter-Window-Mixer models relationships between window tokens. These dual modules allows us to capture more intricate patterns and long-range de

关键词： Time series

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Research on Optimization of Aluminum Electrolysis Product Tending Machine Queuing System with Non-preemptive Priority

Research on Optimization of Aluminum Electrolysis Product Te...

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2022 International Conference on Mechanisms and robotics, ICMAR 2022

作者： Song, Libo Shi, Haibo Liu, Chang Zhu, Jun Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510657328

Product tending machine (PTM) is one of the most important equipment in the aluminum electrolysis production process, for the problem of PTM optimization, a queuing system model of aluminum electrolysis PTM is proposed for the first time. According to the actual production situation of the aluminum electrolysis industry, a non-preemptive priority M/G/1 aluminum electrolysis PTM queuing system model is defined, and the general calculation formula of the operating index of the non-preemptive priority PTM queuing system is given, including queuing system length, waiting time, optimal service rate, etc. According to the actual production data of an aluminum factory, the relevant indicators were calculated and analyzed, and the relationship between the operating indicators and the number of electrolysis cells was drawn. Finally, under the conditions of meeting different operating indicators, the optimal matching scheme of PTM and aluminum electrolysis cells was given. © 2022 SPIE.

关键词： Queueing theory

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Key Parameters Prediction for intelligent Control and Energy Optimization Based on MIC-BWO-XGBoost 2

Key Parameters Prediction for Intelligent Control and Energy...

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2nd International Conference on Cyber-Energy systems and intelligent Energy, ICCSIE 2024

作者： Lv, Changyao Wang, Yaqi Chen, Yudong Yu, Zhuo Hu, Guoliang Wu, Hongliang Li, Shuai Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China Shanghai Aircraft Manufacturing Co. Ltd. Shanghai201324 China

ISBN: (纸本)9798350373318

In the aviation industry, anodic oxidation serves as a core process for surface modification of aluminum alloy components, albeit accompanied by high energy consumption. Accurate prediction of key parameters can offer decision support for the intelligent control and energy optimization of anodic oxidation. However, the limited sample size resulting from the high cost of data collection and the complex nonlinear patterns caused by the synergy of multiple factors pose a challenge in accurately predicting key parameters. Therefore, we propose a key parameters prediction method based on the Maximum Information Coefficient (MIC), Beluga Whale Optimization algorithm (BWO), and XGBoost algorithm. Considering the adverse impact of low correlation parameters on prediction accuracy, we first use MIC to extract features from the original data. Then, XGBoost is used to integrate multiple decision trees to ensure the strong fitting ability for nonlinear relationships. Compared to deep learning models, XGBoost performs well even in a limited sample size. In addition, considering XGBoost is prone to overfitting training data as an ensemble learning algorithm, we employ the BWO to optimize four key parameters of the model that are difficult to choose, thereby enhancing model's generalization. The model's performance is evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with values of 0.04388, 0.09410, and 0.20730%, respectively. The experimental results demonstrate that the proposed method outperforms traditional approaches, enabling precise prediction of key parameters and holding significant value for the intelligent control and energy optimization of anode oxidation processes. © 2024 IEEE.

关键词： Anodic oxidation

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