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TechRxiv 2023年

作者： Yang, Songnan Zhang, Xiaohui Liu, Ding Bai, Wenqi Liu, Fan The School of Automation and Information Engineering Xi’an University of Technology Shaanxi 710048 China The Institute of Advanced Navigation and Electromagnetics Xi’an University of Technology Shaanxi 710048 China The State Key Laboratory of Eco-Hydraulics in the Northwest Arid Region of China Xi’an University of Technology Shaanxi 710048 China The Shaanxi Key Laboratory of Complex System Control with the Intelligent Information Processing Xi’an University of Technology Shaanxi 710048 China

Numerous studies have demonstrated that numerous animal species are capable of goal-directed navigation using environmental information for dead reckoning. The stable magnetic field of the earth provides important information for the migration of animals over long distances. Inspired by the goal-directed navigation of animals, a novel Geomagnetic Navigation with Temporal Attention-based Data-Driven Dead Reckoning (Attention-DR) method for Autonomous Underwater Vehicles (AUV) is presented in this article, which only utilizes the inclination angle (I) and the declination angle (D) of the Geomagnetic Field (GF) for underwater navigation without any prior knowledge of the geographical location or geographic map. This article proposes a Temporal Attention-based Long Short-Term Memory (TA-LSTM) neural network by combining history, GF information, and location in time series to achieve the optimization heading angle of underwater dead reckoning. Due to its ability to adjust the utilization weights of local and global temporal information in the path, TA-LSTM possesses the capability to continually update neural network model parameters through a data-driven process during the navigation process. This enables the Attention-DR to achieve efficient navigation in regions without prior magnetic maps and overcome navigation failures caused by magnetic field anomalies. The simulation outcomes confirm the efficiency, precision, and practicability of the proposed algorithm. © 2023, CC BY.

关键词： Navigation

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Multi-segment Bidding Behaviors of Power Units: Price-capacity-pair Analysis and Modeling 36

Multi-segment Bidding Behaviors of Power Units: Price-capaci...

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36th Youth Academic Annual Conference of Chinese Association of automation, YAC 2021

作者： Hou, Shuangshuang Wang, Ying Li, Lili Zhao, Jing Ning, Jia Zhang, Kaifeng Southeast University College of Software Suzhou China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China State Grid Electric Power Research Institute Nari Group Corporation Nanjing China Nanjing Institute of Technology School of Electric Power Engineering Nanjing China

ISBN: (纸本)9781665437127

With the deregulation of the power market, power units are allowed to make strategic biddings. The bidding results of the strategic bidding market are substantially different from the ones in the previous regulated market. Such differences in the bidding results will further have impacts on the power flow and system security. Thus, it is important to analyze and establish a model to simulate the bidding behaviors of the power units. This paper proposes a price-capacity-pairs model to simulate multi-segment bidding behavior. We select five vital factors that affect biddings behaviors as input: load, capacity, supply-demand ratio, bidders' bidding tendency and unit type. Based on the three-segment bidding rule, the price-capacity pair is used. For uncertain price or capacity, we choose a truncated normal distribution model. Scenario generation and reduction are achieved by Monte Carlo simulation and fuzzy C-means clustering algorithm. We apply our method to a case study based on Guangdong's market in China. The simulation of wind power units, coal power units and gas power units has verified the correctness of the model. © 2021 IEEE.

关键词： Power markets

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Accelerating exploitation and integration of global renewable energy

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Innovation 2025年

作者： Wang, Jianxiao Chen, Xinjiang Zhuang, Minghao Li, Yan Ruan, Ziwen Wang, Yuhan Zhang, Ning Song, Jie He, Kebin Lu, Xi National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China Peking University Ordos Research Institute of Energy Ordos017000 China State Key Laboratory of Nutrient Use and Management College of Resources and Environmental Sciences China Agricultural University Beijing100193 China College of Environmental Science and Engineering Tongji University Shanghai200092 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing100084 China State Key Lab of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China

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Bayesian optimization assisted meal bolus decision based on gaussian processes learning and risk-sensitive control

arXiv

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arXiv 2021年

作者： Cai, Deheng Liu, Wei Ji, Linong Shi, Dawei State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Department of Endocrine and Metabolism Peking University People’s Hospital Beijing China

Effective postprandial glucose control is important to glucose management for subjects with diabetes mellitus. In this work, a data-driven meal bolus decision method is proposed without the need of subject-specific glucose management parameters. The postprandial glucose dynamics is learnt using Gaussian process regression. Considering the asymmetric risks of hyper- and hypoglycemia and the uncertainties in the predicted glucose trajectories, an asymmetric risk-sensitive cost function is designed. Bayesian optimization is utilized to solve the optimization problem, since the gradient of the cost function is unavailable. The proposed approach is evaluated using the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the standard insulin bolus calculator. For the case of announced meals, the proposed method achieves satisfactory and similar performance in terms of mean glucose and percentage time in [70, 180] mg/dL without increasing the risk of hypoglycemia. Similar results are observed for the case without the meal information (assuming that the patient follows a consistent diet) and the case of basal rate mismatches. In addition, advisory-mode analysis is performed based on clinical data, which indicates that the method can determine safe and reasonable meal boluses in real clinical settings. The results verify the effectiveness and robustness of the proposed method and indicate the feasibility of achieving improved postprandial glucose regulation through a data-driven optimal control method. © 2021, CC BY.

关键词： Gaussian distribution

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A Parallel control Method for Zero-Sum Game with Unknown Time-Varying System

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The International Journal of Intelligent control and systems 2024年第1期29卷 37-41页

作者： Qinglai Wei Zhenhua Zhu Jie Zhang Fei-Yue Wang 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 100049China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

In this paper,based on ACP(ACP:artificial societies,computational experiments,and parallel execution)approach,a parallel control method is proposed for zero-sum games of unknown time-varying *** process of constructing a sequence of artificial systems,implementing the computational experiments,and conducting the parallel execution is *** artificial systems are constructed to model the real *** experiments adopting adaptive dynamic programming(ADP)are shown to derive control laws for a sequence of artificial *** purpose of the parallel execution step is to derive the control laws for the real ***,simulation experiments are provided to show the effectiveness of the proposed method.

关键词： Zero-sum games parallel control ACP(ACP:artificial societies computational experiments and parallel execution) adaptive dynamic programming(ADP)

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Design and demonstration of a dynamic wireless power transfer system for electric vehicles

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Science China(Information Sciences) 2019年第12期62卷 223-241页

作者： Ze ZHOU Liyan ZHANG Zhitao LIU Longhua MA Miao HUANG Hongye SU School of Automation Wuhan University of Technology State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University Ningbo Institute of Technology Zhejiang University

The usage of electric vehicles is being globally promoted with the development of clean energy. However, the development of electric vehicles is limited by the battery capacity and the charging tech-

关键词： cycle Design and demonstration of a dynamic wireless power transfer system for electric vehicles

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A New Self-Learning Optimization management Scheme for Smart Grid systems

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The International Journal of Intelligent control and systems 2024年第2期29卷 66-70页

作者： Qinglai Wei Zehua Liao School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

This paper presents a new energy optimization management scheme for the smart grid system(SGS),which considers not only the inclusion of renewable energy in energy management but also the feedback sale of redundant renewable energy to the grid,so as to obtain better optimization management results and avoid energy waste.A new performance index function is constructed,which aims to save users’electricity costs and protect energy storage *** construct a periodic iterative self-learning method based on the characteristics that the relevant data of SGS are approximately *** proposed self-learning optimization method is based on adaptive dynamic programming(ADP).The initial iterative control law is obtained by pre-training,and the designed method can be realized by neural *** experiments are carried out by using the relevant data of SGS for four *** experimental results show that the new energy management scheme can help users save a lot of electricity costs,and is more effective than some common methods.

关键词： Smart grid system energy management renewable energy self-learning adaptive dynamic programming

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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Model-Based Prediction of Grinding Surface Roughness With Error Correction via a Knowledge-Based Fuzzy Broad Learning System

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zewen Hu Hongcai Chen Kanjian Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China Southeast University Shenzhen Research Institute Shenzhen China

Surface roughness is a key indicator of product quality. Traditional measurement methods generally rely on manual trials and inspections, which are time-consuming and costly. Therefore, developing an accurate and reliable prediction model is vital for automatic grinding, as it enables more informed decision-making, reduces waste, and improves efficiency. However, current model- and data-driven methods suffer from either low accuracy or poor interpretability. To address these challenges, this article proposes an enhanced hybrid framework that integrates a mechanistic model-based predictor with a knowledge-based fuzzy broad learning system (KBFBLS) for error correction. The mechanistic model offers a physically interpretable baseline estimate of surface roughness, while KBFBLS enhances prediction accuracy by learning the mapping from process parameters to the residual errors of the mechanistic model. Built upon the fuzzy broad learning system (FBLS)—an emerging neuro-fuzzy model for efficient nonlinear modeling—KBFBLS integrates expert knowledge-guided fuzzy partition and variance-determined Gaussian membership function (MF) widths, two novel strategies that further improve the system’s expressiveness and adaptability, making it a powerful error corrector. Experimental results on real-world robotic disk grinding tasks show that the proposed framework outperforms representative model-driven, data-driven, and hybrid methods. Furthermore, its adaptability to other machining processes is validated using the wheel grinding and milling datasets.

关键词： Surface roughness Rough surfaces Surface treatment Predictive models Adaptation models Learning systems Training Accuracy Milling Error correction

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QMIX Algorithm for Coordinated Welding of Multiple Robots

QMIX Algorithm for Coordinated Welding of Multiple Robots

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作者： Liangchuang Liao Yang-Yang Chen School of Electronic Science & Engineering Southeast University Robot Department Jiangsu Automation Research Institute School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

This paper considers the coordinated welding control based on deep multi-agent reinforcement *** discrete-time states and actions with local observation for the welding robots and the characteristics of the weld lines(e.g.,the starting points of the weld lines and the ending points of the weld lines) are defined,which is suitable to use the monotonic value function factorisation for deep multi-agent reinforcement learning(QMIX) algorithm.A novel reward composed of trajectory optimization,collision avoidance and the task done is designed,which is proved by the simulation in the grid-world environment.

关键词： Deep multi-agent reinforcement learning Welding robots QMIX

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