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SSRN 2024年

作者： Li, Weipeng Chong, Yuting Guo, Xin Liu, Jun School of Automation and Information Engineering Xi’an University of Technology No. 5 Jinhua South Road Shaanxi Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing No. 5 Jinhua South Road Shaanxi Xi’an710048 China

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system operation and management. The integration of advanced data-driven forecasting methods with precise and efficient data feature decomposition and extraction techniques is necessary to improve the accuracy of wind power generation prediction, due to the inherent randomness and nonlinearity of physical states impacting wind power output, as well as the abundance of redundant information in their original measurement data. This paper proposes a novel hybrid wind power prediction model based on seasonal feature decomposition of wind energy data and enhanced feature extraction. Its effectiveness and superiority are demonstrated through comprehensive multi-model comparisons. © 2024, The Authors. All rights reserved.

关键词： Wind power

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Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

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IFAC-PapersOnLine 2023年第2期56卷 7071-7076页

作者： Min Li Luefeng Chen Min Wu Min Ding Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students’ intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

关键词： Convolution Neural Networks Broad Learning Emotional Intention Human-Robot Interaction

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control 26

Trajectory Tracking for Directional Drilling in Underground ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wu, Jiajun Lu, Chengda Li, Hao Wei, Hongchao Wu, Min School of Future Technology China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an710077 China China Coal Research Institute Beijing100013 China

ISBN: (纸本)9798331521950

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∞ 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. © 2025 IEEE.

关键词： Predictive control systems

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An-Improved $\alpha-\mathbf{Shapes}$ Algorithm for Solving Collaborative Workspaces of Dual SCARA Robots Based on Monte Carlo Method

An-Improved $\alpha-\mathbf{Shapes}$ Algorithm for Solving C...

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Chinese control Conference (CCC)

作者： Feng-Yu Zhang Gang Yu Shu-Han He Wang-Yong He 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 Huaxin Cement Technology Management (Wuhan) Co. Ltd. Wuhan P. R. China

Given the dual robots play a key role for collaborative work. In this paper, an improved method is proposed to find the kinematic positive solution based on its D-H parameters, model the dual SCARA robot in MATLAB environment, and solve the collaborative workspace point cloud map by Monte Carlo method. The proposed method extracts and plots the boundary curves of the collaboration space by improved the $\alpha-\text{Shapes}$ algorithm, and obtains the shape of the common region of the collaboration space of the dual SCARA robots and the limit position parameters in the collaboration space. It provides a theoretical basis for the research of path planning, trajectory planning, dynamics analysis, mechanism parameter optimization, self-avoidance, and anti-collision problems of the dual-robot collaborative system.

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Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

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Journal of Safety Science and Resilience 2021年第3期2卷 146-156页

作者： Yunrong Yang Zhidong Cao Pengfei Zhao Dajun Daniel Zeng Qingpeng Zhang Yin Luo The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China Shenzhen Artificial Intelligence and Data Science Institute(Longhua) ShenzhenChina School of Data Science City University of Hong Kong-Hong Kong SARChina

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of *** unstructured COVID-19 publications make it challenging for policymakers to obtain relevant *** graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making ***,we introduce a novel framework that can ex-tract the COVID-19 public health evidence knowledge graph(CPHE-KG)from papers relating to a modelling *** screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment *** define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset(CPHIE).We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++based on the *** the model on the new corpus,we construct CPHE-KG containing 60,967 entities and 51,140 ***,we seek to apply our KG to support evidence querying and evidence mapping *** SS-DYGIE++(SpanBERT)model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution *** has also shown high performance in the relation identification *** evidence querying,our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and *** evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 *** demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.

关键词： Evidence-based public health Knowledge graph COVID-19 decision-making support

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Spirometer and sEMG respiratory patterns for clinical decision support system

Spirometer and sEMG respiratory patterns for clinical decisi...

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IEEE Instrumentation and Measurement Technology Conference

作者： Amit Krishan Kumar Mansour H. Assaf Voicu Z. Groza Emil M. Petriu Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Republic of Fiji School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

To improve support system for subjects with respiratory ailments, this work analyses the respiratory patterns from spirometer and surface electromyography (sEMG). The patterns are integrated with risk and biochemical systems model to optimise the clinical decision support system (CDSS). The work considers terminal events for the spirometer and sEMG. The events and their associated patterns are correlated and projected on a hyperspace prior implementing it in decision support system. The proposed method shows that the different characteristics of breathing indicate some essential hidden information which when used as 4D scattered projection, optimises the decision making in CDSS. The analysis shows the effectiveness of bimodal respiratory patterns for different breathing capacities, which is promising for the modelled conditions.

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A Reinforcement Learning Approach for Robust Restoration of Generators in Power System

A Reinforcement Learning Approach for Robust Restoration of ...

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Chinese automation Congress (CAC)

作者： Huimiao Li Xiaozhou Yang Chao Zhai School of Automation China University of Geosciences Wuhan Campus Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Singapore-ETH Centre CREATE Campus Future Resilient Systems Singapore

In the past decades, cascading blackouts have caused serious damages to power systems and affected the normal operation of society, so it is crucial to quickly restore the damaged power system to normal state. In this paper, a reinforcement learning (RL) approach is developed to achieve the robust restoration of generators in power systems. Firstly, the performance recovery of damaged power system is modeled as markov decision process, and constraints such as line voltage and active power output of generators are considered. Secondly, a RL algorithm is proposed to search the optimal control strategy for generator units recovery. Based on the proposed restoration approach, Q-learning algorithm is employed to obtain the optimal strategy for power supply of generaors in power grids. Finally, numerical simulations are carried out on IEEE 9-bus system under different scenarios of external disturbances and certainties. The simulation results demonstrate the effectiveness and feasibility of the proposed approach.

关键词： Q-learning Power supplies Simulation Power system protection Generators Explosions Power systems

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Motion control Strategy Based on Integrated Trajectory for the Pendubot

Motion Control Strategy Based on Integrated Trajectory for t...

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European control Conference (ECC)

作者： Lejun Wang Xuzhi Lai Pan Zhang Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

The control objective of a Pendubot is always to swing its endpoint up from the vertical downward equilibrium point (VDEP) and to stay it at the vertical upward equilibrium point (VUEP). This paper proposes a motion control approach based on the integrated trajectory to achieve the above control objective with only one controller. First, an integration trajectory with two parts is designed for the Pendubot. We plan the first part trajectory with variable parameters, and optimize these variable parameters by applying the genetic algorithm (GA) to make all links move simultaneously from their starting states to their target states. We plan the second part trajectory to make all links remain at the target states. Then, we build the linear time-varying error system based on the integration trajectory, and design a global tracking controller by using the linear system control method to track the integration trajectory. By this way, the control objective of the Pendubot is realized. Finally, the simulations illustrate the validity and superiority of the proposed method.

关键词： Linear systems Target tracking Torque Simulation Europe control systems Trajectory

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Modeling and Tracking control for Dielectric Elastomer Actuator with Sliding Mode Feedback controller

Modeling and Tracking Control for Dielectric Elastomer Actua...

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IEEE International Conference on control Science and systems Engineering (CCSSE)

作者： Yue Zhang Peng Huang Pan Zhang Yawu Wang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

This paper proposes the modeling and tracking control methods for the dielectric elastomer actuator (DEA). A dynamic model of the DEA is built, which is composed of an asymmetric hysteresis model, a creep model and a linear system. To compensate the hysteresis and creep nonlinearities of the DEA, an inverse compensation feedforward controller (ICFC) is designed based on the creep model and the inverse of the asymmetric hysteresis model. Besides, an extended state observer (ESO) is designed to estimate the unmeasurable state of the DEA, as well as uncertainties including the modeling errors, parameter perturbations and external disturbances. Then, a sliding mode feedback controller (SMFC) is designed cooperating with the ICFC to realize the high precision tracking control of the DEA. The convergence of the ESO and the whole control system are proved through the Lyapunov method. Finally, some experiments are executed to verify the effectiveness of the proposed modeling and control methods.

关键词： Linear systems Uncertainty Creep control systems Modeling Adaptive control Feedforward systems

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Online Optimal Transmission Scheduling of an Energy Harvesting Sensor for Remote State Estimation

Online Optimal Transmission Scheduling of an Energy Harvesti...

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第40届中国控制会议

作者： Haoxiang Xiang Chenwei Zhong Jingwen Yang Xi’nan Qiu Hongxia Rao Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control School of Automation Guangdong University of Technology

We investigate an online optimal sensor transmission scheduling problem for remote state estimation. We consider the energy harvesting sensor, which can absorb external energy and use the energy to transmit the local state estimates to the remote estimator over an unreliable wireless channel. Furthermore, we consider that the sensor has two different transmission energy levels, where different energy levels correspond to different data reception ratios. Considering the trade-off between estimation performance and energy consumption, we hope to design an optimal transmission policy that minimizes the convex combination of the estimation error covariance and the energy for data transmission. We formulate the optimization problem as a Markov decision process, which can be solved by dynamic programming. Then, we introduce the concept of submodularity to prove that there is an optimal threshold policy on the error covariance, given sensor battery and collected energy. Eventually, simulations are given to illustrate the aforementioned results.

关键词： Remote estimation energy harvesting sensor scheduling Markov decision process threshold structure

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