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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是301-310 订阅

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Quantitative Relationship in Terms of Time-Delay Tolerance of Two Kinds of Extended State Observers 6

Quantitative Relationship in Terms of Time-Delay Tolerance o...

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2017 ieee 6th data driven control and learning systems conference (ddcls’17)

作者： Minnan Piao Kai Zhu Mingwei Sun Zenghui Wang Zengqiang Chen College of Computer and Control Engineering Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University

ISBN: (纸本)9781509054626

Active Disturbance Rejection control(ADRC), an innovative control method, has been applied successfully in dealing with internal uncertainties and external disturbances. However, ADRC for time-delay plants is still a challenge due to the restriction on the bandwidth of the extended state observer(ESO). When designing the ESO for time-delay plants, both fullorder and reduced-order extended state observers can be utilized and the selection of the proper order and the bandwidth of the ESO is necessary. therefore we seek to compare the two kinds of observers in terms of the time-delay tolerance of the closedloop stability and find an appropriate bandwidth to ensure the robustness to the time-delay uncertainty. First, the quantitative bandwidth relationship between the two kinds of observers is established with the equivalent effective bandwidth, and then the time-delay tolerance of the closed-loop stability are calculated for the first-and second-order plants to conduct the comparison,whilst the effects of the bandwidth and other parameters on the time-delay tolerance is analyzed. Furthermore, simulation results for a second-order plant are provided to verify the meaning of this investigation.

关键词： ADRC Extended State Observer Effective Bandwidth Time-Delay Tolerance

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A Novel Rotor Position Detection Method Using Morphological Filter Algorithm for Brushless DC Motor 6

A Novel Rotor Position Detection Method Using Morphological ...

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2017 ieee 6th data driven control and learning systems conference (ddcls’17)

作者： Yin Zhang Xiao-Hua Zhou Yu-Wei Zhang Xi-Sheng Dai School of Electrical and Information Engineering Guangxi University of Science and Technology Key Laboratory of Industrial Process Intelligent Control Technology of Guangxi Higher Education Institute Guangxi key laboratory of Automobile Components and Vehicle technology

ISBN: (纸本)9781509054626

In this paper, by analyzing the relationship between rotor position and phase back electromotive forces(PB-EMFs) of brushless DC motor(BLDCM), a novel rotor position detection method based on morphological filter algorithm(MFA) for BLDCM is proposed. MFA is applied to identify the signal turning points(STPs) of the input stator PB-EMFs, which provides technical support for the current commutations of PWM control system. In addition, accurate current commutation point detection of BLDCM promises rotor field to keep perpendicularity with armature magnetic field, to achieve accurate detection of rotor position and position sensorless control of BLDCM. Results of simulation studies proved that, the proposed detection method can realize the position sensorless control for BLDCM by obtaining the STPs of PB-EMFs in BLDCM, which greatly improves the static and dynamic performance of speed regulation system of BLDCM, and obtaining a outstanding control effect.

关键词： Brushless DC Motor Detection of Rotor Position Phase Back Electromotive Force Morphological Filter Algorithm Position Sensorless control

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Adaptive Optimal control of Completely Unknown systems with Relaxed PE Conditions 11

Adaptive Optimal Control of Completely Unknown Systems with ...

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

作者： Luo, Rui Peng, Zhinan Hu, Jiangping Ghosh, Bijoy Kumar School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China Texas Tech University Department of Mathematics and Statistics LubbockTX79409-1042 United States

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper proposes a novel identifier-critic (IC) learning control strategy for completely unknown nonlinear system. Different from the existing results, the proposed IC control is capable of obtaining the optimal control under relaxed persistence of excitation (PE). A neural network (NN) based identifier is established to approximate the unknown system dynamics. After that, an only-critic NN framework is proposed to solve the Hamiltonian-Jacobi-Bellman (HJB) equation such that the control policy is obtained. To estimate the unknown weights of both identifier NN and critic NN simultaneously without strictly PE limitation, the dynamic regressor extension and mixing (DREM) technique is introduced to design the NN weight update laws. Meanwhile, new easy-to-check online convergence conditions for the proposed adaptive laws are given to ensure the unknown weights converge to their ideal values. In addition, theoretical analysis is also given to prove the significant relaxation of the proposed convergence conditions compared with the standard PE assumption. © 2022 ieee.

关键词： Adaptive control systems

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learning-based Modeling and control of Underactuated Balance Robotic systems 13

Learning-based Modeling and Control of Underactuated Balance...

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13th ieee conference on Automation Science and Engineering (ieee CASE)

作者： Chen, Kuo Yi, Jingang Liu, Tao Rutgers State Univ Dept Mech & Aerosp Engn Piscataway NJ 08854 USA Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9781509067817

Underactuated balance robots represent a broad class of mechanical systems, ranging from Furuta pendulum, autonomous motorcycles, and robotic bipedal walkers, etc. the control tasks of these systems include trajectory tracking and balancing requirements. We present a data-driven modeling and control framework of the underactuated balance robots. A machine-learning method is used to capture the dynamics and the balance equilibrium manifold that represents balancing task target. We combine the learning-based models with the structural properties of the external/internal convertible form of these underactuated systems. Applications of the proposed learning-based models and control design are applied to the Furuta pendulum by simulation and experiments.

关键词： Analytical models Legged locomotion control design Manifolds Aerodynamics

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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 ...

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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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Power-Grid controller Anomaly Detection with Enhanced Temporal Deep learning 18

Power-Grid Controller Anomaly Detection with Enhanced Tempor...

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18th ieee International conference on Trust, Security and Privacy in Computing and Communications (ieee TrustCom) / 13th ieee International conference on Big data Science and Engineering (ieee BigdataSE)

作者： He, Zecheng Raghavan, Aswin Hu, Guangyuan Chai, Sek Lee, Ruby Princeton Univ Princeton NJ 08544 USA SRI Int Princeton NJ USA

ISBN: (纸本)9781728127767

controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid system, especially zero-day attacks, can be catastrophic. Earlier detection of the anomalies can prevent further damage. However, detecting zero-day attacks is extremely challenging because they have no known code and have unknown behavior. Furthermore, if data collected from the controller is transferred to a server through networks for analysis and detection of anomalous behavior, this creates a very large attack surface and also delays detection. In order to address this problem, we propose Reconstruction Error Distribution (RED) of Hardware Performance Counters (HPCs), and a data-driven defense system based on it. Specifically, we first train a temporal deep learning model, using only normal HPC readings from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller. then, we run this model using real-time data from commonly available HPCs. We use the proposed RED to enhance the temporal deep learning detection of anomalous behavior, by estimating distribution deviations from the normal behavior with an effective statistical test. Experimental results on a real power-grid controller show that we can detect anomalous behavior with high accuracy (>99.9%), nearly zero false positives and short (<360ms) latency.

关键词： Hardware Sensors Process control Substations Actuators Deep learning Malware

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Safe HVAC control via Batch Reinforcement learning 13

Safe HVAC Control via Batch Reinforcement Learning

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13th ACM/ieee International conference on Cyber-Physical systems (ICCPS )

作者： Liu, Hsin-Yu Balaji, Bharathan Gao, Sicun Gupta, Rajesh Hong, Dezhi Univ Calif San Diego La Jolla CA 92093 USA Amazon Seattle WA USA

ISBN: (纸本)9781665409674

Buildings account for 30% of energy use worldwide, and approximately half of it is ascribed to HVAC systems. Reinforcement learning (RL) has improved upon traditional control methods in increasing the energy efficiency of HVAC systems. However, prior works use online RL methods that require configuring complex thermal simulators to train or use historical data-driven thermal models that can take at least 10(4) time steps to reach rule-based performance Also, due to the distribution drift from simulator to real buildings, RL solutions are therefore seldom deployed in the real world. On the other hand, batch RL methods can learn from the historical data and improve upon the existing policy without any interactions with the real buildings or simulators during the training. With the existing rule-based policy as the priors, the policies learned with batch RL are better than the existing control from the first day of deployment with very few training steps compared with online methods. Our algorithm incorporates a Kullback-Leibler (KL) regularization term to penalize policies that deviate far from the previous ones. We evaluate our framework on a real multi-zone, multi-floor building-it achieves 7.2% in energy reduction cf. the state-of-the-art batch RL method, and outperforms other BRL methods in occupants' thermal comfort, and 16.7% energy reduction compared to the default rule-based control.

关键词： HVAC control Batch Reinforcement learning Deep Reinforcement learning

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Prediction of Ship Propulsion Power based on Bayesian optimized LightGBM Using Ship In-Service data 13

Prediction of Ship Propulsion Power based on Bayesian optimi...

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13th ieee International conference on Communications, Circuits and systems (ICCCAS)

作者： Liu Xu Zhang Antong Sun Qiang Guo Yu Yin Wenhui Yang Yunfan Bao Jiawei China Ship Sci Res Ctr Wuxi Jiangsu Peoples R China

ISBN: (数字)9798350386271

ISBN: (纸本)9798350386288;9798350386271

In maritime operations, accurate prediction and control of the propulsion power of the ship's main engine are crucial, as they directly impact the fuel economy, speed performance and carbon emission control. To enhance prediction accuracy, this paper proposes a regression prediction method for ship propulsion power based on the LightGBM model, specifically designed for vessels equipped with air lubrication drag reduction systems. First, domain knowledge is used to select sensor monitoring data related to main engine propulsion power, preprocess it as input variables, and use main engine propulsion power as output. Bayesian optimization is then used to adjust the hyper-parameters of the model. Finally, in order to evaluate the performance of this method, comparisons are made with multiple linear regression and support vector machine (SVM) regression in predicting ship propulsion power. the results show that the LightGBM model based on Bayesian optimization has strong generalization ability, short training time and superior performance.

关键词： ship propulsion power VLCC machine learning LightGBM Bayesian optimization

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Emotion-driven Intelligent Product Design Based on Deep learning 13

Emotion-Driven Intelligent Product Design Based on Deep Lear...

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13th ieee International conference on Communication systems and Network Technologies, CSNT 2024

作者： Zhang, Mengmeng Polus International College Chengdu China

ISBN: (纸本)9798350305463

this research investigates the integrated application of deep learning technologies in the field of emotion-driven interior soft decoration product design. through comprehensive online surveys conducted with over 1000 participants and coupled with biometric feedback from 300 individuals, this study offers a multi-faceted analysis of user emotions during interactions with interior soft decoration products. the employment of a finely-tuned Convolutional Neural Network (CNN) model facilitated the extraction of key emotional design elements, indicating that around 520 of the surveyed participants were satisfied with certain interior soft decoration designs, while a minority of 80 expressed dissatisfaction. Moreover, the biometric data revealed a significant correlation between increased heart rates (exceeding 85 beats per minute) and intense emotional responses. the integration of biometric and questionnaire textual data suggested that users indeed experienced strong positive emotions in interactions with specific soft decoration products. the study also addresses potential challenges, including possible biases in data collection and the inherent limitations of deep learning models. the findings underscore the significant potential of deep learning in capturing the complex patterns of user emotions, providing valuable insights for the future design of emotion-centric interior soft decoration products. through this interdisciplinary approach, the study charts a promising direction for the advancement of artistic disciplines such as interior design and soft decoration product design, emphasizing the importance of understanding and incorporating user emotion in the design process. © 2024 ieee.

关键词： Deep learning

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Practical Research on Intelligent Evaluation of Generative learning Resource Quality 13

Practical Research on Intelligent Evaluation of Generative L...

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13th International conference on Educational and Information Technology, ICEIT 2024

作者： Li, Ruikun Li, Kangkang School of Smart Education Jiangsu Normal University Jiangsu Xuzhou China

ISBN: (纸本)9798350372663

this study is based on the intelligent evaluation method of large language model, aiming at the evaluation problem of generative learning resources. An empirical study is conducted on the effect of intelligent evaluation on improving the quality of generative learning resources among 31 postgraduate subjects. Specifically, this study set up a control group evaluated by experts and an experimental group involved in intelligent evaluation, and obtained data of generative learning resources created within eight weeks for analysis and comparison. the results showed that intelligent evaluation can achieve a comparable level with expert evaluation in evaluating resources and continuously improving resource quality. this study suggests that by using intelligent evaluation systems, we can quickly and accurately evaluate a large number of learning resources and provide evidence for their improvement. © 2024 ieee.

关键词： Quality control

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