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检索条件"任意字段=10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021"

共 359 条记录，以下是281-290 订阅

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Adaptive Containment control for Uncertain Networked Agent systems with Second-Order Dynamics 13

Adaptive Containment Control for Uncertain Networked Agent S...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： You, Long Lin, Boxian Yue, Jiangfeng Shi, Mengji Qin, Kaiyu Ren, Zili School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China National Laboratory on Adaptive Optics Chengdu611731 China

ISBN: (纸本)9798350361674

this article proposes a neural network-based adaptive distributed containment controller for second-order integral multi-agent systems with nonlinear dynamics and external disturbances. Firstly, the proposed controller effectively suppresses the effects of nonlinear dynamics and external disturbances while achieving containment control. Secondly, for the adaptive update law of neural networks, unlike existing studies, we simultaneously consider the influence of position error and velocity error on the convergence speed of the weight matrix. Further, two positive definite matrices are constructed to prove the stability of the system based on Lyapunov theory, and the upper bound for the containment errors is provided. Finally, the effectiveness of the proposed method is verified through numerical simulation experiments. © 2024 ieee.

关键词： Adaptive control systems

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Attention-based Stacked Supervised Poisson Autoencoders for Defects Prediction in Casting-rolling process 11

Attention-based Stacked Supervised Poisson Autoencoders for ...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Zhang, Xinmin Song, Zhihuan Ge, Zhiqiang College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, we propose an attention-based stacked supervised Poisson autoencoders (ASSPAE) modeling framework to predict the occurrence of defects in the casting-rolling process. ASSPAE is a novel deep learning model, which is designed based on the stacked supervised Poisson autoencoders and attention-based multi-feature fusion strategy. Stacked supervised Poisson autoencoders are a novel deep neural networks model, which is designed by integrating the Poisson regression network layer into the deep autoencoders framework. Compared with the conventional autoencoder-based models, the proposed stacked supervised Poisson autoencoders not only can capture the quality-relevant deep features of the data, but also is suitable for predicting the count-type quality variable. Furthermore, in order to efficiently leverage information present in different layers of the network, an attention-based multi-feature fusion strategy is integrated into the stacked supervised Poisson autoencoders modeling framework. the effectiveness of the proposed ASSPAE method is evaluated by its application to the cast-rolling process of a steelmaking plant. the application results show that the prediction accuracy of the proposed ASSPAE modeling method was improved by 32%, 31%, 24%, and 13% in Root Mean Square Error (RMSE) compared with the partial least square regression, support vector regression, Poisson regression, and stacked autoencoders-Poisson regression, respectively. © 2022 ieee.

关键词： Network layers

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An Approach to the Extraction of Intersecting Pipes Weld Seam Based on 3D Point Cloud 11

An Approach to the Extraction of Intersecting Pipes Weld Sea...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Yang, Shuai Shi, Xiaorui Tian, Xincheng Liu, Yan Center for Robotics School of Control Science and Engineering Shandong University Jinan250061 China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China SINOTRUK Jinan Power Co. Ltd. Jinan250220 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, a method based on point cloud for weld seam extraction of intersecting pipes workpiece is proposed. Firstly, a lightweight clustering algorithm based on the normal vector direction was proposed to extract the feature points of the weld seam. After filtering the feature points, a novel key points extraction algorithm was proposed to extract the key points of the weld seam. After that, a non-Uniform Rational B-Spline curve was used to approximate the key points and the preliminary spatial weld curve was obtained. Finally, an iterative algorithm was adopted to optimize the generated weld curve. the proposed method is verified on the ideal and real intersecting pipes point cloud models. Experiments show that the proposed method has high accuracy and robustness, and can accurately extract the intersecting pipes weld seam curve, providing guidance for the subsequent accurate automatic welding. © 2022 ieee.

关键词： Extraction

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A Semantic-Consistent Conditional Generative Adversarial Model for Zero-Shot learning in Bearing Fault Diagnosis 13

A Semantic-Consistent Conditional Generative Adversarial Mod...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Chen, Jizheng Yang, Rui School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China School of Electrical Engineering Electronics and Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9798350361674

the scarcity of labeled data remains one of the most significant challenges in industrial fault diagnosis, where the industrial processes lack readily sampled in many fault types. this issue is often called the zero-shot learning problem in industrial fault diagnosis. To address this challenge, this paper proposes a novel fault diagnosis model for rotating machinery, leveraging Generative Adversarial Networks (GANs) and semantic consistent loss. Our approach aims to generate a rich set of samples for fault types that are difficult to collect, thereby enabling the training of an accurate classification model. the proposed model introduces a one-dimensional convolutional neural network to enhance the quality of samples and the accuracy of the model. A semantic consistent discriminator is proposed to ensure that the distribution of generated samples closely aligns with the distribution of actual samples. the performance of the model is validated using the CWRU bearing fault dataset, and the results demonstrate its efficacy in addressing the zero-shot fault diagnosis challenge. © 2024 ieee.

关键词： Generative adversarial networks

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Model Reconstruction Based on Physical-Informed Polynomial Neural ODEs 13

Model Reconstruction Based on Physical-Informed Polynomial N...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Chen, Zhang Zhu, Wei Li, Jingsui Bian, Hanlin Pei, Chao School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing400065 China

ISBN: (纸本)9798350361674

Neural networks are capable function approximators for data, but their lack of interpretability poses challenges. Physical-informed Polynomial Neural Ordinary Differential Equations (PIPNODEs) are proposed. they combine the constraints of prior knowledge of the data with interpretable Polynomial Neural Ordinary Differential Equations (PolyNODEs) to discover or approximate the governing equations. In this work, Neural Ordinary Differential Equations (NODEs) are categorized into two layers: a physical-informed term, which represents the priori knowledge of the data, and a discrepancy term, which is derived from the approximation of PolyNODEs. Pruning strategies are also used to remove redundant terms with coefficients lower than a certain threshold. We demonstrate the effectiveness and flexibility of this framework in a series of numerical algorithms with varying levels of prior knowledge. We found that having correct but incomplete physical information accelerates training and approaching thresholds compared to having incorrect physical information. © 2024 ieee.

关键词： data modeling discprepancy modeling interpretability Neural Ordinary Differential Equations physics-informed machine learning polynomial neural network

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A data-driven Prediction Method for Lithium-ion Batteries based on Variational Bayes and Particle Filtering 13

A Data-Driven Prediction Method for Lithium-ion Batteries ba...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wang, Hao Deng, Yi Hou, Ru Xin Liu, Qie School of Automation Chongqing University Chongqing400044 China

ISBN: (纸本)9798350361674

the remaining useful life (RUL) prediction and state of health (SOH) assessment of lithium-ion batteries are the key technologies for achieving equipment prediction and health management (PHM). Accurate RUL prediction results can serve as evaluation indicators for battery management systems(BMS), ensuring their safe and stable operation. this paper proposes a model parameter estimation method based on particle filtering (PF) and variational Bayesian (VB) to address the issues of high degradation noise and small data sample size in lithium-ion batteries, which lead to large prediction model errors and poor real-time performance. Considering the impact of uncertainty in noise, this paper uses a hybrid model to describe the data degradation process of lithium-ion batteries at different time intervals and probabilistically describes the measurement noise and model parameters. On this basis, the variational Bayes method is used to perform posterior estimation on each parameter, and the particle filter optimization method is used to dynamically update the values of the model parameters. this paper conducts experimental verification using publicly available datasets from NASA. the results indicate that this method can achieve good predictive performance. © 2024 ieee.

关键词： Particle Filtering Remaining Useful Life Prediction Variational Bayes

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Multi-Player Robust control of Stackelberg Games via Adaptive Dynamic Programming 11

Multi-Player Robust Control of Stackelberg Games via Adaptiv...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Zhang, Yongwei Zhang, Shunchao Zhao, Bo Liu, Derong Guangdong University of Technology School of Automation Guangzhou510006 China Beijing Normal University School of Systems Science Beijing100875 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, the hierarchical optimization problem of multi-player systems with matched uncertainties is investigated via adaptive dynamic programming. In the hierarchical optimization problem, there exist one leader and multiple followers, the leader chooses a policy in advance based on the actions of the followers, and the followers make optimal responses to the leader's policy. the optimal policies of the leader and the followers form the Stackelberg equilibrium. By designing appropriate value functions for the leader and the followers, the hierarchical optimization problem is formulated as a Stackelberg game and the robust stabilization problem is transformed into an optimal regulation problem. Moreover, the critic-only structure is established to obtain the approximate Stackelberg equilibrium. theoretical analysis shows that the developed ADP-based robust control guarantees the closed-loop system to be asymptotically stable. Finally, simulation example is adopted to verify the effectiveness of the present scheme. © 2022 ieee.

关键词： Robust control

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Adaptive Predefined-Time SMC for Uncertain Robotic systems 13

Adaptive Predefined-Time SMC for Uncertain Robotic Systems

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Qiao, Chaoqian Zong, Guangdeng Chang, Zhenyu School of Electronics and Information Engineering Tiangong University Tianjin300387 China School of Control Science and Engineering Tiangong University Tianjin300387 China

ISBN: (纸本)9798350361674

this paper presents a novel adaptive predefined-time sliding-mode control (SMC) strategy for the tracking problem of uncertain robotic manipulators. Firstly, the unknown dynamic parameters and the bound vector of the disturbances are integrated into a compounded uncertainty. Considering that the boundary of the compounded uncertainty is not readily available in practical applications, the adaptive neural networks (NNs) are adopted to compensate for the compounded uncertainty of the robot. Moreover, to enhance the robustness and accelerate the system convergence, a novel nonsingular sliding surface approach is integrated into the controller design. then, an NNs-based predefined-time controller is proposed to guarantee that the actual trajectory tracks the reference trajectory, allowing the maximum convergence time to be pre-adjusted using explicit parameters without losing robustness property. At last, simulation results conducted with a two-joint manipulator confirm both the exceptional performance and effectiveness of the developed method. © 2024 ieee.

关键词： Neural networks

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AMING:Adaptive-learning Mechanism Based Missing-Value Imputation Networks Using GAN in Industrial Processes 13

AMING:Adaptive-Learning Mechanism Based Missing-Value Imputa...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Jiale Yuan, Xiaofeng Wang, Kai Wang, Yalin Yang, Chunhua Gui, Weihua School of Automation Central South University Changsha410083 China

ISBN: (纸本)9798350361674

In industrial production, uncontrollable factors can lead to the inability to collect or transmit data, resulting in the widespread presence of missing values in industrial datasets. these missing data may contain crucial information for production and serve as a vital source for diagnosing equipment malfunctions or issues. Incomplete datasets directly impact subsequent modeling, prediction, and optimization tasks. However, traditional imputation methods struggle to capture complex data distributions and structures, making them unsuitable for production tasks that require high imputation accuracy. Due to its proficiency in simulating data distributions, generative networks have emerged as effective tools for handling missing or corrupted data. the classic GAIN model introduces a prompting mechanism, providing fixed prompts to the discriminator, but this may lead to issues such as model degradation, pattern collapse, and convergence difficulties. In this paper, we propose a novel unsupervised adaptive imputation framework based on GANs, referred to as AMING. It aims to impute missing data in industrial production processes in an unsupervised adaptive manner. Furthermore, we introduce an adaptive learning mechanism framework that dynamically provides learning information to the discriminator during the training process. Finally, we conduct experimental validation on a hydrocracking dataset, demonstrating the superior performance of the AMING method. © 2024 ieee.

关键词： data handling

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End-to-End Autonomous Driving Algorithm Based on PPO and Its Implementation 13

End-to-End Autonomous Driving Algorithm Based on PPO and Its...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhao, Jianfeng Zhao, Yang Li, Weixia Zeng, Chaoqun School of Automative and Transportation Engineering Shenzhen Politechnic University Shenzhen518000 China

ISBN: (纸本)9798350361674

With the commercialization of vehicles equipped with partial autonomous driving capabilities, achieving fully autonomous driving remains a hot topic among researchers. In contrast to modular autonomous vehicle control, end-to-end autonomous driving aims to replicate human driving by directly translating sensor inputs into vehicle control outputs, making it the ultimate goal in autonomous driving. In this study, we propose an end-to-end autonomous driving algorithm based on the Proximal Policy Optimization (PPO) reinforcement learning algorithm and verify its feasibility. Firstly, we construct an autonomous driving algorithm based on the PPO algorithm, where pre-encoded images and vehicle state information are input to a policy based on deep neural networks, directly generating speed and steering controls for the vehicle, thus achieving true end-to-end autonomous driving. Furthermore, using the CARLA simulation environment for autonomous vehicles, we define a reward mechanism for driving behavior and establish a reinforcement learning environment to train and validate the proposed algorithm. Finally, we analyze the algorithm's feasibility based on rewards, cumulative average rewards, average episode length, and average driving distance. While the algorithm still has a long way to go before practical application, it lays the foundation for further implementation of end-to-end autonomous driving algorithms. © 2024 ieee.

关键词： Deep reinforcement learning

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