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2022 4th International Conference on Energy systems and Electrical Power, ICESEP 2022

作者： Lai, GuangZhi Wang, Dong Li, HaoRan Zhao, Yi Ni, WeiChen Wen, JiaHao State Grid e-commerce Co. Ltd Beijing100053 China State Grid Tianjin Electric Power Company Tianjin300392 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

Energy is the focus of recent years. As one of the most representative new energy sources, solar energy has the characteristics of large reserves and no pollution. The main way to use solar energy is photovoltaic (PV) power generation, and the PV module in the PV power generation system is the component that converts solar energy into electric energy. In the actual power generation process, PV modules often receive uneven solar illumination due to the shadow caused by clouds, trees, buildings, etc., resulting in changes in the output characteristics of PV modules and reduced output efficiency. Therefore, modeling of PV modules under shading condition is very important. This paper presents two simple methods for solving the parameters of PV module models, which require few parameters and are easy to obtain. A modeling method of PV modules under shading condition is also presented. And then the modeling method is verified with the experiment result. Finally, an error criterion for PV module modeling under shading condition is proposed to measure the goodness of the modeling. Combining with the two parameter solving methods, the errors of this PV module modeling method under shading condition of the error criterion proposed in this paper are 3.24% and 2.51%, respectively, which meet the requirements for engineering use. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Solar energy

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Finite Time Disturbance Observer-Based Tube Model Predictive control for Vehicle Platoon systems with Guaranteed L∞ String Stability

Finite Time Disturbance Observer-Based Tube Model Predictive...

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Chinese control and decision Conference, CCDC

作者： Qian Chen Jinhao Liu Jun Yang Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781665478977

This paper investigates optimal longitudinal control problems for a vehicle platoon in presence of parameter uncertainties and external disturbances. First, a multi-constraint multi-objective optimization model is developed, where physical limits, safety constraints, driving comfort, and fuel economy are taken into account. To reduce communication burden and avoid network congestion, the preceding vehicle’s acceleration is obtained by employing a finite time disturbance observer (FTDO). As for the parameter uncertainties as well as external disturbances, they are estimated as a lumped disturbance by exploiting a FTDO. Then, under a predecessor following communication topology, a FTDO-based tube model predictive control method with explicit consideration of string stability is contrived. Finally, numerical simulations illustrate the effectiveness and superiority of the proposed control approach.

关键词： Uncertain systems Network topology Fuel economy Numerical simulation Stability analysis Topology Safety

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Multi-Task Learning-Enabled Automatic Vessel Draft Reading for intelligent Maritime Surveillance

arXiv

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arXiv 2023年

作者： Qu, Jingxiang Liu, Ryan Wen Zhao, Chenjie Guo, Yu Xu, Sendren Sheng-Dong Zhu, Fenghua Lv, Yisheng The Hubei Key Laboratory of Inland Shipping Technology School of Navigation Wuhan University of Technology Wuhan430063 China The State Key Laboratory of Maritime Technology and Safety Wuhan430063 China The Automation and Control Center The Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei106335 Taiwan The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

The accurate and efficient vessel draft reading (VDR) is an important component of intelligent maritime surveillance, which could be exploited to assist in judging whether the vessel is normally loaded or overloaded. The computer vision technique with an excellent price-to-performance ratio has become a popular medium to estimate vessel draft depth. However, the traditional estimation methods easily suffer from several limitations, such as sensitivity to low-quality images, high computational cost, etc. In this work, we propose a multi-task learning-enabled computational method (termed MTL-VDR) for generating highly reliable VDR. In particular, our MTL-VDR mainly consists of four components, i.e., draft mark detection, draft scale recognition, vessel/water segmentation, and final draft depth estimation. We first construct a benchmark dataset related to draft mark detection and employ a powerful and efficient convolutional neural network to accurately perform the detection task. The multi-task learning method is then proposed for simultaneous draft scale recognition and vessel/water segmentation. To obtain more robust VDR under complex conditions (e.g., damaged and stained scales, etc.), the accurate draft scales are generated by an automatic correction method, which is presented based on the spatial distribution rules of draft scales. Finally, an adaptive computational method is exploited to yield an accurate and robust draft depth. Extensive experiments have been implemented on the realistic dataset to compare our MTL-VDR with state-of-the-art methods. The results have demonstrated its superior performance in terms of accuracy, robustness, and efficiency. The computational speed exceeds 40 FPS, which satisfies the requirements of real-time maritime surveillance to guarantee vessel traffic safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Neural network structure design based on N-gauss activation function1

arXiv

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arXiv 2021年

作者： Lu, Xiangri Ma, Hongbin Zhang, Jingcheng State Key Laboratory of Intelligent Decision and Control for Complex Systems Beijing Institute ofTechnology Beijing China Institute of Pattern Recognition and Intelligent Systems School of Automation Beijing Institute ofTechnology China Institute of Navigation Guidance and Control School of Automation Beijing Institute of

Recent work has shown that the activation function of the convolutional neural network can meet the Lipschitz condition, then the corresponding convolutional neural network structure can be constructed according to the scale of the data set, and the data set can be trained more deeply, more accurately and more effectively. In this article, we have accepted the experimental results and introduced the core block N − Gauss, N − Gauss, and Swish (Conv1, Conv2, FC1) neural network structure design to train MNIST, CIFAR10, and CIFAR100 respectively. Experiments show that N − Gauss gives full play to the main role of nonlinear modeling of activation functions, so that deep convolutional neural networks have hierarchical nonlinear mapping learning capabilities. At the same time, the training ability of N − Gauss on simple one-dimensional channel small data sets is equivalent to the performance of ReLU and Swish. Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian distribution

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Industrial Wireless Network Access Selection Based on Multilevel Fuzzy Neural Network

Industrial Wireless Network Access Selection Based on Multil...

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Chinese control and decision Conference, CCDC

作者： Yan Xu Zhijun Gao Sichao Zhang School of Electrical and Control Engineering Shenyang Jianzhu 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 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In order to solve the issues in the heterogeneous industrial wireless network access selection such as delay and reliability, this paper present an industrial wireless network access selection based on multilevel fuzzy neural network. The algorithm divides the input attributes into three categories: network attributes, terminal attributes, and environment attributes. It synchronously inputs them into the fuzzy neural network to obtain the candidate network scores, which effectively reduces the number of fuzzy rules and reduces the algorithm complexity. Furthermore, this paper also consider the influence of the industrial environment on network selection and quantitatively reduce the number of switching and blocking rate. The simulation experimental results show that this algorithm is able to achieve fast network access selection in noisy and occluded industrial heterogeneous wireless networks, and the blocking rate can remain low in the presence of dense terminals.

关键词： Wireless networks Neural networks Switches Mean square error methods Interference Fuzzy neural networks Delays

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Short - Term Prediction of Coke Pushing Current Peak Based on Improved ARIMA Model

Short - Term Prediction of Coke Pushing Current Peak Based o...

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Industrial Cyber-Physical systems (ICPS)

作者： Haiyang Wei Luefeng Chen Jie Hu Yi Ren Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan

Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. The higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accurate prediction of current peak during future coke pushing operation can provide more time for production personnel to adjust production status and avoid difficult coke pushing in carbonization chamber. In this paper, a combination prediction model based on VMD (Variational Mode Decomposition) and improved ARIMA (Autoregressive Integrated Moving Average) models is proposed. Firstly, VMD algorithm is used to decompose time series of coke pushing current peak, de-noising data, and extracting main information of time series. Then, ARIMA model is used to predict mean change of linear elasticity and GARCH (Generalized Autore-gressive Conditional Heteroskedasticity) model is introduced to predict ARIMA model residual and improve heteroscedasticity of nonlinear part of time series, and then ARIMA-GARCH model is established. Finally, predicted value is obtained by the sum of each component prediction. The experimental results show that the proposed prediction model has a high prediction accuracy in the short-term prediction of coke pushing current peak. The scheme is applied to actual coking production to guide production of coke.

关键词： Resistance Ovens Time series analysis Noise reduction Production Predictive models Prediction algorithms

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control

Trajectory Tracking for Directional Drilling in Underground ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Jiajun Wu Chengda Lu Hao Li Hongchao Wei Min Wu School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Automation China University of Geosciences Wuhan China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China China Coal Research Institute Beijing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the $H_{\infty}$ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment.

关键词： Drilling Uncertainty Trajectory tracking Stability criteria Predictive models Taylor series Coal mining Trajectory Thermal stability Predictive control

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Optimal Carbon Capture, Utilization and Storage Planning for Multi-Energy System

Optimal Carbon Capture, Utilization and Storage Planning for...

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Chinese control Conference (CCC)

作者： Aoyu Hu Da Xu Menglu Gao School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China DC Company of State Grid Hubei Electric Power Co. Ltd. Yichang China

In response to the national dual carbon policy, carbon capture, utilization and storage (CCUS) are currently attracting much attention. This paper proposes optimal CCUS planning for multi-energy system. In this multi-energy system, transmission network and natural gas network are coupled via conversion and storage devices in the energy hubs. The CCUS planning is formulated based on a source-sink matching method to analyze the CO2 capture points, CO 2 storage points and CO 2 transportation routes. Furthermore, the original CCUS planning problem is reformulated as a mixed integer second order cone programming (MISOCP) and subsequently solved to guarantee a satisfactory convergence performance. Numerical studies on an urban multi-energy system are implemented to verify the effectiveness and superiority of the proposed methodology over conventional scheme.

关键词：

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Safe RL-based Adaptive Cooperative Game control of Wing Deformation and Flight State Tracking for Morphing Hypersonic Vehicles

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Wang, Jun-Wei Wang, Feng Wu, Huai-Ning Liu, Zhou-Yang University of Science and Technology Beijing School of Intelligence Science and Technology Beijing100083 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Hangzhou International Innovation Institute Hangzhou311115 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing100074 China

This study addresses the issue of adaptive cooperative game control (ACGC) design for wing deformation and flight state tracking of morphing hypersonic vehicles (MHVs) with variable wing shapes, input constraints and state constraints. To access a better flight control performance, the wing deformation parameter is extended in this work as an auxiliary control input. An oriented-coordinated-control nonlinear differential equation model is first established to describe the complicated longitudinal flight state tracking error dynamics of the MHV. Then, a control barrier function (CBF) is introduced and incorporated into the value function as a risk penalty term of state security constraint. Based on the differential game (DG) theory, the optimal coordinated control problem for the MHV with input constraints and state constraints is treated as a constrained cooperative DG that is further formulated as a Hamilton-Jacobi-Bellman (HJB) equation with barrier and hyperbolic functions. A safe RL algorithm is proposed to solve the above HJB equation and an online Pareto joint control strategy is achieved thereby. Hereafter the stability analysis of the closed-loop state tracking error system is done by the Lyapunov technique. Meanwhile, both state and input constraints are met during the the parameter learning regulation process. At last, a simulation study is performed to support the proposed safe RL-ACGC algorithm. © 2025 IEEE.

关键词： Hyperbolic functions

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Fast accurate position control of light-responsive liquid crystal elastomer actuator

Fast accurate position control of light-responsive liquid cr...

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Asian control Conference

作者： Xinyu Nie Jundong Wu Yawu Wang Chun-Yi Su Daiki Sato Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Gina Cody School of Engineering and Computer Science Concordia University Montreal Quebec Canada Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST) Tokyo Institute of Technology Yokohama Kanagawa Japan The School of Engineering Tokyo University of Technology Tokyo Japan

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Light-responsive liquid crystal elastomers (LCEs) are promising for soft robot actuators due to their ability to respond to light stimuli and produce deformation. In order to realize their application in soft robots, fast and accurate position control is essential. Based on the intrinsic physical nature of LCE actuator's deformation, a terminal sliding mode controller (TSMC) is designed to efficiently adjust the temperature of the actuator, thus directly controlling the actuator deformation. In order to achieve the target temperature, a prediction model is established to predict the target temperature corresponding to the target displacement. Moreover, a proportional integration controller is applied to eliminate model errors and disturbances. The feasibility of the control strategy is experimentally verified, and compared with conventional PID controller. The results prove that the proposed control strategy can achieve fast and accurate control of displacement.

关键词： Heating systems Actuators Accuracy Deformation Position control Process control Elastomers

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