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第40届中国控制会议

作者： Fan Jiang Huai-Ning Wu The Science and Technology on Aircraft Control Laboratory and School of Automation Science and Electrical Engineering Beihang University

Dynamic Movement Primitives(DMPs),which represent movement plans based on a set of nonlinear differential equations with well-defined attractor dynamics,have been adapted for movement *** this paper,we present a trajectory generation method for mobile agent from multiple demonstration using *** original DMPs can be only used to learn from single *** order to adapt multiple demonstrations of different tasks,we use DMPs with Takagi-Sugeno fuzzy model to realize autonomous trajectory *** proposed scheme is capable to regenerate trajectory in the presence of obstacles even when the goal position is altered.A simulation example is given to confirm the effectiveness of the proposed scheme.

关键词： Trajectory generation Takagi-Sugeno fuzzy model Dynamic movement primitives Mobile agent

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin 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 Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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Deep Reinforcement Learning-Based Cooperative Guidance Law with Multi-Constraint

Deep Reinforcement Learning-Based Cooperative Guidance Law w...

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Chinese automation Congress (CAC)

作者： Zhihao Gao Junfang Fan Denghui Dou Yi Ji Yafeng Li Jinmin Wu Beijing Key Laboratory of High Dynamic Navigation Technology School of Automation Beijing Information Science and Technology University Beijing China Beijing Key Laboratory of UAV Autonomous Control School of Aerospace Engineering Beijing Institute of Technology Beijing Institute of Technology Beijing China School of Automation Beijing Information Science and Technology University Beijing China

To enhance the destructive capabilities of small air-to-surface missiles, it is necessary to address the challenges related to terminal attack angle constraints and multi-missile coordinated attacks, we propose a cooperative guidance law with multi-constraint based on deep reinforcement learning in this paper. Firstly, an intelligent control term is introduced into the biased proportional navigation law with angle constraints. Secondly, a reward function is designed to reduce terminal attack angle error and flight time error, ensuring that the missile successfully reaches the target within the expected flight time, while simultaneously converging the attack angle to the desired value. Finally, the deep deterministic policy gradient algorithm is employed for the purpose of training and generating intelligent guidance instructions. Simulation results substantiate the efficacy of the devised guidance method in achieving accurate target strikes, both for individual missiles and in scenarios involving multiple missiles. And exhibiting better stability and guidance accuracy compared to the traditional biased proportional navigation law.

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Parallel control for Optimal Tracking via Adaptive Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2020年第6期7卷 1662-1674页

作者： Jingwei Lu Qinglai Wei Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078 Qingdao Academy of Intelligent Industries Qingdao 266109China

This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear *** existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback ***,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied *** address this problem,an augmented system and an augmented performance index function are proposed ***,the general nonlinear system is transformed into an affine nonlinear *** difference between the optimal parallel control and the optimal state feedback control is analyzed *** is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index ***,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function *** stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference ***,the effectiveness of the developed optimal parallel control method is verified in two cases.

关键词： Adaptive dynamic programming(ADP) nonlinear optimal control parallel controller parallel control theory parallel system tracking control neural network(NN)

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Parallel Treasury for TRUE DAOs: Model, Indicators and Mechanism*

Parallel Treasury for TRUE DAOs: Model, Indicators and Mecha...

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control Conference (ANZCC), Australian and New Zealand

作者： Sangtian Guan Juanjuan Li Wenwen Ding Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

In response to concerns over the centralization tendency in the decentralized autonomous organizations (DAOs), TRUE autonomous organizations and operations (TAOs or TRUE DAOs) have been proposed recently. TAOs aim at spreading equitable value distribution and democratized decision-making, distinguishing them from their DAOs counterparts. This study focuses on the treasury within TAOs, which acts as a central fund pool and a crucial element in the decentralized economy (DeEco) system. First, against a backdrop of potential black swan events and other long-tail unforeseen challenges, a reference model for the intelligent treasury management of TAOs is proposed. Then, an evaluation system, namely VALID, is presented with metrics including verifiability, anti-volatility, legitimacy, inclusiveness, and decentralization. Furthermore, a novel parallel treasury management mechanism is proposed to demonstrate a virtual-real interactive closed-loop management and control paradigm of the treasury, thereby fostering the formulation and development of DeEco. This research provides a comprehensive perspective on intelligent treasury management of TAOs and their role in sustainable advancement of DeEco.

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Space Target Surveillance Based on Non-linear Model Predictive control

Space Target Surveillance Based on Non-linear Model Predicti...

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Chinese automation Congress (CAC)

作者： Jiancheng Zhang Yutao Chen Jie Huang College of Electrical Engineering and Automation Fuzhou University Fuzhou China Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (纸本)9781665426480

Space target surveillance plays an important role in maintaining space advantages. Trajectory planning and attitude control are the key technologies of space target surveillance. We propose a nonlinear model predictive control framework to plan the trajectory of the surveillance satellite, track the surveillance target and keep a certain distance. In particular, the orbit dynamics and environmental disturbance of surveillance satellite are taken into account. A MatLab based real-time nonlinear model predictive control toolbox is used to solve these problems. The toolbox uses an efficient numerical method to calculate the maneuvering solution of MPC, which means that it allows the prediction horizon length to be greatly extended and save calculation time. The developed controller is simulated in a simulation scene, including the interference of near ground environment. We show that the proposed MPC method can be successfully applied to the satellite orbit planning and attitude control and the MATMPC toolbox can greatly speed up the solution of MPC optimization functions.

关键词： Space vehicles Satellites Target tracking Surveillance Aerospace electronics Predictive models Orbits

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Autonomous Crowdsensing: Operating and Organizing Crowdsensing for Sensing automation

arXiv

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arXiv 2024年

作者： Wu, Wansen Yang, Weiyi Li, Juanjuan Zhao, Yong Zhu, Zhengqiu Chen, Bin Qiu, Sihang Peng, Yong Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Hunan Province Changsha410073 China Hunan Institute of Advanced Technology Hunan Province Changsha410073 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

—The precise characterization and modeling of Cyber-Physical-Social Systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This letter reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by a range of technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the "6A-goal" of ACS and propose potential avenues for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Cyber Physical System

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Short-term wind power forecast with turning weather based on DBSCAN-RFE-LightGBM

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Renewable Energy 2025年 251卷

作者： Wu, Zhenlong Fan, Xinyu Bian, Guibin Liu, Yanhong Zhang, Xiaoke Chen, YangQuan School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang471000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Electric Power Research Institute of State Grid Henan Electric Power Company Zhengzhou450052 China Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

At present, the wind power prediction technology is becoming more and more mature, however, the turning weather brings a decrease in prediction accuracy due to its suddenness and instability. In this paper, a wind power prediction method considering the turning period and non-turning period respectively is proposed to weaken the adverse effects caused by turning weather. Firstly, the prediction effects of multiple models are compared, density-based spatial clustering of applications with noise (DBSCAN) is selected as the outlier processing method, recursive feature elimination (RFE) is used as the feature selection method, and Light gradient boosting machine (LightGBM) is used for prediction. The combined prediction method based on DBSCAN-RFE-LightGBM can reduce the influence of abnormal data and redundant features and improve the prediction effect. Then, the sliding window is set to detect the turning period. Considering that the turning weather is an emergency and does not happen frequently, the amount of data is small, which leads to the inability to train the model well. Generative adversarial networks (GAN) are applied to expand the turning period data. The LightGBM is trained and predicted by using the expended turning period data. Finally, the time-division prediction results are merged. Using the data collected from wind farms for short-term power prediction experiments, the time-segment prediction method proposed in this paper with GAN reduces MAE by 1.913 and RMSE by 3.351 on a single unit compared with the non-differentiated period. © 2025 Elsevier Ltd

关键词： Merging

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CMAC-Based Sliding-Mode control for Large-Scale Fuzzy Descriptor Systems

CMAC-Based Sliding-Mode Control for Large-Scale Fuzzy Descri...

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IEEE International Conference on Robotics and Biomimetics

作者： Zhixiong Zhong Zhenghua Shao Zhijie Huan Xingyi Wang Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China

ISBN: (纸本)9781665481106

This paper addresses the problem of sliding-mode control for large-scale fuzzy descriptor systems subject to unknown uncertainties. First, a CMAC neural network is used to approximate the unknown uncertainties, and the CMAC-based sliding-mode controller is proposed. Then, a compensation technique is employed to eliminate the influence on the approximation error stemmed from CMAC model. We use the gradient-descent method to online tune the parameters of CMAC and the Lyapunov function is introduced to ensure the stability of the closed-loop control system and the CMAC's convergence. Finally, the effectiveness and correctness of the obtained theories are verified by the tracking control of multi-autonomous underwater vehicles (AUVs).

关键词： Uncertainty Biomimetics Neural networks Approximation error Stability analysis Sliding mode control Underwater vehicles

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Distributed Adaptive Fault-tolerant Formation control of Uncertain Nonholonomic Mobile Robots

Distributed Adaptive Fault-tolerant Formation Control of Unc...

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第33届中国控制与决策会议

作者： Zhen Han Wei Wang Huijin Fan School of Automation Science and Electrical Engineering Beihang University Department of Control Science and Engineering Huazhong University of Science and Technology

In this paper,the distributed adaptive fault-tolerant formation control problem is investigated for a group of nonholonomic mobile robots(NMRs).The communication flow is described by a digraph and it is supposed that only a subset of the NMRs has direct access to the common desired ***,the actuators of each NMR may suffer from intermittent type of actuator *** estimate the unknown trajectory information and topology parameters,distributed estimators are proposed based on the neighboring available *** addition,estimators are also introduced to estimate the lower bounds of the actuator fault *** on the estimation values and Young's inequality,nonlinear damping terms are ***,a novel fault-tolerant control scheme is proposed by applying the adaptive backstepping *** is guaranteed that the formation errors will converge to an adjustable compact set and the control inputs are globally uniformly *** results validate the effectiveness of the proposed control scheme.

关键词： Fault-tolerant Distributed adaptive control Formation Nonholonomic mobile robots

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