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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211-220 订阅

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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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Research on Blood Glucose Nonlinear controller Based on Backstepping Adaptive control Algorithm 13

Research on Blood Glucose Nonlinear Controller Based on Back...

引用

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

作者： Liu, Shuya Song, Ruizhuo Lu, Xiang Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Automation and Electrical Engineering Beijing Institute of Technology Beijing100083 China

ISBN: (纸本)9798350361674

Closed-loop control of blood glucose regulation in type 1 diabetes patients can be achieved through an artificial pancreas. In this article, a nonlinear controller based on backstepping adaptive approach is proposed for the integration of the artificial pancreas, with a focus on improving the effectiveness of postprandial blood glucose control. By extending the Bergman physiological model using a digestion kinetics equation, an extended blood glucose dynamic model is established to effectively capture the relationship between meal intake and blood glucose variations. the blood glucose model, which includes personalized unknown parameters, serves as the basis for the derivation of the control algorithm using a combination of backstepping control and adaptive control algorithms. By accurately calculating the required dosage and timing of insulin injections for individual patients, the designed control algorithm enables precise blood glucose control. the resulting control signal, capable of adapting to unknown parameters, effectively regulates fasting and postprandial blood glucose levels in type 1 diabetes patients. the effectiveness and robustness of the proposed blood glucose control algorithm are demonstrated through the implementation of various experimental protocols. © 2024 ieee.

关键词： Physiological models

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Resource-efficient Model-free Adaptive Sliding-mode Heading control for Unmanned Surface Vehicle with data Dropouts 13

Resource-efficient Model-free Adaptive Sliding-mode Heading ...

引用

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

作者： Chen, Yutong Zhao, Huarong Xu, Dezhi Yu, Hongnian Dan, Chenshi School of Internet of Things Engineering Jiangnan University Jiangsu Wuxi China School of Electrical Engineering Southeast University China School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEHI0 5DT United Kingdom

ISBN: (纸本)9798350361674

this paper addresses the issue of heading control for an unmanned surface vehicle (USV) in the presence of un-certainties. We propose a model-free adaptive sliding-mode heading control method considering resource-efficient control and dual-channel data dropouts. First, a dynamic linearization model with a pseudo partial derivative is constructed, the value of the pseudo partial derivative is estimated, and a model-free sliding-mode heading controller is formulated for USV. Second, an event-triggered mechanism allows successful triggering only when the event trigger threshold is reached, thus significantly saving communication resources. Furthermore, a comprehensive compensatory strategy is devised to solve the data loss issues in both the forward and feedback channels. Ultimately, the convergence of heading control error of USV governed by the proposed method is rigorously proved, and the effectiveness of the proposed approach is verified through simulations. © 2024 ieee.

关键词： Unmanned surface vehicles

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A Deep learning Soft Sensor Based on Domain-Invariant Features Extraction and Online Local Adaptation 13

A Deep Learning Soft Sensor Based on Domain-Invariant Featur...

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

作者： Wang, Yelei Jin, Huaiping Wang, Bin Yang, Biao Kunming University of Science and Technology Faculty of Information Engineering and Automation Department of Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming650500 China

ISBN: (纸本)9798350361674

data-driven approaches, especially those based on deep learning, have received much attention in quality prediction in the modern process industry. However, in practice, industrial data are often generated in the form of data streams. As operating conditions and process parameters change with the environment, industrial data are dynamic, resulting in degraded model prediction performance. Although the method based on deep transfer learning can automatically extract domain-invariant features in various tasks to adapt to data distribution changes. However, current deep transfer learning methods cannot handle online data streams with dynamic distribution change characteristics. To solve this problem, a deep learning soft sensor method based on domain-invariant features and online local adaptation (DIF-OLA) is proposed. the implementation of the method consists of an offline phase and an online phase. In the offline phase, domain-invariant features are extracted to build a domain-adaption model. In the online phase, a local fine-tuned update strategy is introduced to make the prediction model better adapted to the new data streams. the effectiveness of the method is verified by the dataset of an actual industrial debutanizer column process. the experimental results indicate that compared to traditional adaptation soft sensor methods, the model has improved performance metrics of MAE, RMSE and R2 by 41.14%,40.94%, and 30.94% respectively. © 2024 ieee.

关键词： Contrastive learning

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data-driven Soft Sensor Modelling with Time Delay Estimation through Bayesian Optimization 13

Data-Driven Soft Sensor Modelling with Time Delay Estimation...

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

作者： Yang, Jun Wang, Bo Liang, Yangyang Zhang, Weiyong Ding, Peng Shang, Chao Methanol Branch Ningxia Coal Co. Ltd. Yinchuan750411 China Technologies Co. Ltd. Beijing100085 China Tsinghua University Department of Automation Beijing100084 China

ISBN: (纸本)9798350361674

the soft sensor technology is an effective solution to online estimation of primary variable that is difficult to measure via hardware sensors in industrial processes. By selecting several easy-to-measure auxiliary variables that are closely related to the primary variable, a mathematical model can be established to predict the primary variable online. However, there is typically a time-delayed correlation between auxiliary variables and primary variable, which is usually neglected in state-of-the-art soft sensing approaches. In this study, we propose a new data-driven soft sensing approach by considering time-delayed inputs in Support Vector Machine (i.e. TD-SVM). Both discrete-valued time delays and hyperparameters of SVM are jointly optimized by using Bayesian Optimization (BO) algorithm based on Gaussian process. We carry out case studies on process data from a real-world methanol plant in China, which demonstrate the usefulness of the proposed soft sensor model and parameter optimization strategy. It is shown that the BO can effectively optimize the estimation of time delays and hyperparameters of SVM jointly, thereby helping to improve the model accuracy. © 2024 ieee.

关键词： Support vector machines

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Affine Ultra-Local Based Model-Free Predictive control on PMSM Drives with ϵ varepsilon-Approximation State Compensation 13

Affine Ultra-Local Based Model-Free Predictive Control on PM...

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

作者： Wei, Yao Ma, Weiyuan Xie, Haotian Ke, Dongliang Wang, Fengxiang Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Jinjiang362200 China College of Mechanical and Electronic Engineering Fujian Agriculture and Forestry University Fuzhou350002 China

ISBN: (纸本)9798350361674

the accuracy of a data-driven model directly affects the control performance of model-free predictive control (MFPC). To take into account the affine motion of the plant, a model-free predictive current control (MF-PCC) strategy is proposed in this paper and applied to a permanent magnet synchronous motor (PMSM) driving system. To enhance model accuracy, we adopt an affine ultra-local model, which is designed to accurately represent the system. Online estimation of all affine operators is performed using the recursive least squares (RLS) algorithm to ensure a robust fit. To improve control performance, we integrate a state compensation technique with the model. the compensation gain is selected using the ϵ varepsilon-approximation approach to meet terminal constraints. the stability of the proposed method is theoretically analyzed, and its effectiveness is verified through experimental results. the proposed method offers improved model accuracy and current quality, along with enhanced robustness compared to conventional methods. © 2024 ieee.

关键词： Predictive control systems

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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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Yolo-S: Texture-Less Object Pose Estimation with Shape Prior 13

Yolo-S: Texture-Less Object Pose Estimation with Shape Prior

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

作者： Wang, Danzhu Liu, Shan Polytechnic Institute Zhejiang University Hangzhou310015 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

this paper aims to address the challenge of pose estimation for texture-less objects under conditions of stacking, occlusion, and complex backgrounds. the proposed method, called Yolo-S, consists of two parts: a pre-estimation network based on Yolov8 backbone and a key point post-refinement network based on Graph Neural Network. the first part uses off-the-shelf detectors result as inputs (e.g. MaskRCNN) and outputs the pre-estimate keypoints at the pixel coordinates. the second part encodes the roughly estimated keypoints and the priori knowledge of the object's CAD model, uses a graph neural network to intergrate feature and predict the final keypoints. PNP method is used to obtain the object's pose. the main advantage of Yolo-S is that is has a prior knowledge embedded GNN model for estimating the pose of texture-less objects, which greatly improves prediction quality and accuracy and achieves zero-shot sim-to-real model transfer. Experimental results on the LINEMOD dataset demonstrate the effectiveness of the proposed method and its significant competitiveness with other state-of-the-art methods. © 2024 ieee.

关键词： Graph neural networks

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Full Wavelength Selection and Deep Spectra Feature Extraction Network for Near-Infrared Spectroscopy Quantitative Analysis 13

Full Wavelength Selection and Deep Spectra Feature Extractio...

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

作者： Liu, Jinlong Luan, Xiaoli Liu, Fei Jiangnan University Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Jiangsu Wuxi214122 China

ISBN: (纸本)9798350361674

Near-infrared spectroscopy (NIRS) has been widely used in the chemical industry and has attracted much attention because of its fast and non-destructive characteristics. However, spectra are susceptible to interference from external environmental factors, leading to spectral overlap and noise problems. To solve these problems, spectral wavelength selection can be performed. Traditional selection methods require manually choosing the appropriate method based on spectral data and combining it with appropriate regression modeling, which is complicated and time-consuming. In contrast, this study proposes a novel spectral wavelength selection method that intelligently filters the entire wavelength based on the correlation between spectra and output variables. Simultaneously, it utilizes a deep spectra feature extraction module to obtain a more abstract spectral representation applied in regression prediction. Evaluation results on the training and test sets demonstrate the outstanding performance of this model: RMSE of 0.007796 and 0.01036 for the training and test sets, respectively, R2 of 0.9918 and 0.9842, with an impressive RPD of 7.9703. these metrics indicate that this model meets the rigorous requirements for high-performance models in the chemical engineering domain. © 2024 ieee.

关键词： Near infrared spectroscopy

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Iterative Fault-Tolerant control Strategy for Urban Traffic Signals under Signal Light Failure 13

Iterative Fault-Tolerant Control Strategy for Urban Traffic ...

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

作者： Liang, Shuo Yan, Fei College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan030024 China College of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China

ISBN: (纸本)9798350361674

Most existing signal control methods in urban transportation areas have not taken into account the impact of signal light failures in actual road networks. By introducing a signal fault model on the basis of a macroscopic traffic flow model, a traffic signal control strategy based on iterative learning fault-tolerant control is proposed to address the overall signal control problem of urban road networks in the presence of traffic signal faults. When a signal light in the road network malfunctions and the fault is restored, by controlling the traffic signal, the queue length of vehicles in each lane at the intersection of the road network tends to be balanced, thereby improving road traffic efficiency. At the same time, state feedback is introduced into the control law to reduce the number of vehicles entering the intersection with faulty signal lights, reduce the impact on traffic flow when the signal light fails, and suppress the impact caused by periodic disturbances within the region. Finally, the convergence of the method was proved through mathematical derivation, and a linear matrix inequality was given to determine the controller gain. the simulation experiment results show that the proposed method can effectively reduce the impact of signal light failures on urban traffic flow. © 2024 ieee.

关键词： State feedback

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