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Chinese Automation Congress (CAC)

作者： Kai Zhou Weihua Cao Chao Gan Yupeng Li 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

Troublesome incidents like sudden water inflows increase the risk of collapse accidents in tunnel excavation. In this study, a data-driven underground water prediction method is proposed based on trend features extracted from apparent resistivity. A novel framework is developed for extracting trend features from the contour lines of apparent resistivity. These trend features are subsequently integrated with numerical features from the resistivity matrix for classification. The effectiveness of the proposed method is demonstrated by apparent resistivity data from real tunnel engineering. The result indicates that the classification accuracy of the proposed method outperforms the method without feature extraction.

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An Active Suppression Method Considering Input Time-Delay for Stick-Slip Vibration in the Drilling Procedure

An Active Suppression Method Considering Input Time-Delay fo...

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Chinese control Conference (CCC)

作者： Hao Sun Chengda Lu Zhaoqing He Shengnan Tian Zhejiaqi Ma 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 Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

This paper uses the wave equation to explain the torsional motion of the drill-string system. Solving the wave equation with the D'Alembert method, a neutral time-delay model of the drill-string system is obtained. The disturbance input, caused by the bit-rock interaction, is given consideration, and an equivalent-input-disturbance (EID) based controller is designed to mitigate the disturbance in the established model. In the actual drilling procedure, the system input time-delay increases as the length of the drill columns increases. If the influence of system input time-delay in the drilling procedure is ignored, it will most likely lead to the drill-string system instability and cause serious consequences. The essential contribution of this paper is the incorporation of input time-delay into the EID based control structure. Considering the system's input time-delay, the proposed model is more practical and has significant implications for stick-slip vibration assessment and control in drilling procedures.

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Optimal consensus control for partially unknown multi-agent systems with switching communication topology*

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IFAC-PapersOnLine 2023年第2期56卷 1210-1216页

作者： Wenpeng He Xin Chen Menglin Zhang Yipu Sun Akinori Sekiguchi Jinhua She 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 School of Engineering Tokyo University of Technology Hachioji Tokyo 192-0982 Japan

Since the local neighborhood tracking error system for switching multi-agent systems (MASs) is time-varying, the existing optimal consensus method for fixed MASs cannot be used in switching MASs. This paper proposes a new approach to solve the optimal consensus problems of linear switching MASs. Firstly, a new augment local neighborhood tracking error (ALNTE) system, which is based on the joint communication topology, is developed to construct the control object, and the open-loop estimation method is used to estimate the ALNTE for individuals. Secondly, based on the ALNTE system, the performance index is designed, and the value iteration method is used to solve the coupled Hamilton-Jacobi-Bellman equations and obtain the optimal control. Finally, theoretical analysis is given to prove the learning stability and control stability of the value iteration method, and a numerical simulation is used to verify the effectiveness of the proposed method.

关键词： Switching topology multi-agent systems (MASs) optimal control value iteration augment local neighborhood tracking error (ALNTE)

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Temperature Compensation Method of Fluxgate Sensor Based on Polynomial Fitting

arXiv

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arXiv 2024年

作者： Yang, Ruiping Liu, Huan Ge, Jian Chugo, Daisuke Dong, Haobin 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 Faculty of Engineering Kwansai Gakuin University Osaka669-1330 Japan

Fluxgate sensors are widely used in the field of low frequency and weak vector magnetic field measurement because of their good performance, such as high resolution and low power consumption. However, during the long-term continuous observation, the drift errors of the fluxgate sensor will occur due to the variable ambient temperature. This paper proposes a temperature compensation method for fluxgate sensors based on polynomial fitting. First, a physical model of the temperature & fluxgate sensor was established on the COMSOL Multiphysics simulation platform, and the influence of temperature on the measurement performance of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature compensation model of the fluxgate sensor was constructed. And compared it with other temperature compensation method, the result shows that the proposed temperature compensation method is relatively simple and can better achieve real-time compensation for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were *** temperature drift is reduced from more than 500 nT before compensation to about 1 nT. The results show that the proposed method has a good temperature compensation effect on the data measured by the fluxgate sensor within a variable temperature background. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulation platform

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Robust Data-Driven control Against Actuator FDI for Unknown Linear systems

Robust Data-Driven Control Against Actuator FDI for Unknown ...

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International Conference on control and Automation (ICCA)

作者： Wenjie Liu Jian Sun Fang Deng Gang Wang Jie Chen National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Department of Control Science and Engineering Tongji University Shanghai China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper designs a data-driven controller for unknown linear systems whose actuators suffer from false data injection (FDI) attacks, using only noisy input-state data. To achieve this objective, a general FDI attack model is introduced, which imposes constraints only on the switching frequency of attack channels and the magnitude of attack matrices. A time-varying state feedback control law is designed based on offline and online input-state data, which adapts to the channel switching of FDI attacks. This is achieved by solving a data-based semi-definite programs (SDPs) on-the-fly such that stabilizing the set of subsystems consistent with both offline clean data and online attack-corrupted data. It is shown that under mild conditions on the attack and the noise, the feasibility of the proposed SDP guarantees that the controller stabilizes the attack-corrupted system. A numerical example is presented to validate the effectiveness of the proposed method.

关键词： Linear systems Switched systems Actuators State feedback Switching frequency Switches control systems

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Research on Urban Air Quality Prediction Method Based on the Fusion of Wide-area Spatiotemporal Distribution Data

Research on Urban Air Quality Prediction Method Based on the...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Zixing Tao Weihua Cao Chao Gan School of Future Technology China University of Geosciences (Wuhan) Wuhan China School of Future Technology School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences (Wuhan) Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Air quality data exhibit nonlinearity, sensitivity to environmental factors, and long-term dependencies. Numerous factors influence air quality, making accurate predictions based on a single-dimensional dataset impractical. This study proposes a method for urban air quality prediction that integrates wide-area spatiotemporal data, designed to address the unique characteristics of air quality datasets. First, relevant wide-area spatiotemporal data are selected, and their correlations with air quality are systematically analyzed. Second, a Long Short-Term Memory (LSTM) network-based Transformer model is utilized to capture the long-term dependencies in the air quality sequences. Finally, the model successfully generates hourly multi-step predictions for six air pollutants. The experimental results show that the proposed method outperforms the method that relies on air quality data alone for multi-step air quality prediction.

关键词： Deep learning Sensitivity Atmospheric modeling Time series analysis Predictive models Air quality Transformers Environmental factors Spatiotemporal phenomena Long short term memory

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Consensus of Uncertain Euler-Lagrange Multi-agent systems under Switching Digraphs and Disturbances

Consensus of Uncertain Euler-Lagrange Multi-agent Systems un...

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Automation, control and Robotics engineering (CACRE), International Conference on

作者： Lei Ju Zhiwei Wang Anqing Wang Xiaotong Zhou College of Marine Electrical Engineering Dalian Maritime University Dalian China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China

This paper focuses on exploring the problem of achieving leader-follower consensus in uncertain Euler-Lagrange multi-agent systems, which are subjected to disturbance, and operate on switched digraph. To be more precise, the system dynamics are characterized by uncertainties that can be linearly parameterized, and the disturbances that are considered are those originating from the leader system. A novel adaptive distributed control strategy is proposed to overcome the difficulties of suppressing disturbances and achieving consistency under the condition of joint connectivity of switched digraphs. By using Lyapunov stability theory and the generalized Barbalat's lemma, it is proved that the uncertain Euler-Lagrange multi-agent systems achieves state consensus asymptotically with the proposed distributed adaptive control protocol. The efficacy of the adaptive distributed control strategy proposed in the paper is validated by presenting a case study involving a system composed of four double-link manipulators.

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Recurrent Attentional Reinforcement Learning for Machinery Fault Diagnosis

SSRN

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SSRN 2024年

作者： Tang, Zhenhui Wang, Jingcheng Wu, Shunyu The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University No.800 Dongchuan Road Shanghai200240 China The SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya572024 China The Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an710021 China

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although remarkable progress has been attained by learning fault features and predicting the corresponded fault type with deep neural networks, the existing fault diagnosis pipelines usually learn non-discriminative information. Contextual sequence dependency between the localized time fragments is often ignored. To deal with these problem, a recurrent attention framework with reinforcement learning is proposed to find a series of informative features of local time fragments related to different faults in an iterative manner. Fault labels are further predicted on these time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on two typical machinery systems, bearing system and hydraulic system, show that the proposed framework achieves superior diagnosis performance and increases no computation burden on CPU. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning

arXiv

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arXiv 2024年

作者： Zhu, Yuanyang Wang, Zhi Zhu, Yuanheng Chen, Chunlin Zhao, Dongbin The Department of Control Science and Intelligent Engineering School of Management and Engineering Nanjing University Nanjing210093 China The Research Center for Novel Technology of Intelligent Equipment Nanjing University Nanjing210093 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

For on-policy reinforcement learning, discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without considering the inherent ordering between the discrete atomic actions, the explosion in the number of discrete actions can possess undesired properties and induce a higher variance for the policy gradient estimator. In this paper, we introduce a straightforward architecture that addresses this issue by constraining the discrete policy to be unimodal using Poisson probability distributions. This unimodal architecture can better leverage the continuity in the underlying continuous action space using explicit unimodal probability distributions. We conduct extensive experiments to show that the discrete policy with the unimodal probability distribution provides significantly faster convergence and higher performance for on-policy reinforcement learning algorithms in challenging control tasks, especially in highly complex tasks such as Humanoid. We provide theoretical analysis on the variance of the policy gradient estimator, which suggests that our attentively designed unimodal discrete policy can retain a lower variance and yield a stable learning process. © 2024, CC BY.

关键词： Contrastive Learning

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