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

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Process Monitoring of Operational Cost for Wastewater Treatment Processes Using Variants of ARMA Models Based Soft-sensors 11

Process Monitoring of Operational Cost for Wastewater Treatm...

引用

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

作者： Lu, Yu Liu, Yiqi Li, Dong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Technical University of Denmark Department of Chemical and Biochemical Engineering Lyngby Denmark

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Process monitoring of operation cost index (OCI) is of great importance for wastewater treatment plants (WWTPs), which is not only able to support financial budget, but also to optimize local operation. this paper proposed four variants of auto-regressive and moving average (ARMA), based on recursive least squares algorithm (RLS), ARMA based on recursive extended least squares algorithm (RELS), nonlinear auto-regressive neural network (NARNN), and nonlinear auto-regressive neural network with external input (NARXNN) respectively, to predict the operating cost in WWTPs. the proposed methods were validated in the simulation platform, Benchmark Simulation Model No.2-P (BSM2-P). On account of the strong nonlinearity of the wastewater treatment process, the nonlinear model, like NARXNN, achieved better performance in terms of mean square error (MSE) and correlation coefficient (R). © 2022 ieee.

关键词： Wastewater treatment

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Robust Zonotopic-based Interval Fault Estimation for Multi-agent systems with Unknown but Bounded Noise 10

Robust Zonotopic-based Interval Fault Estimation for Multi-a...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zhou, Meng Xue, Tonglai Wang, Jing Wang, Chang School of Electrical and Control Engineering North China University of Technology Beijing100190 China Beijing Aerospace Automation Research Institute Beijing100040 China

ISBN: (纸本)9781665424233

this paper investigates the issue of robust interval fault estimation for multi-agent systems with unknown but bounded uncertainty by zonotopic method, instead of obtaining the accurate fault estimation results by point-based strategy, this paper also achieve the estimation set containing the real augmented state that is consistent with the measurement noises and disturbances, which can provide more estimation information in practice. First, an augmented multi-agent system is obtained by treating the actuator fault as an auxiliary state vector. then, a unified global system description is generated based on undirected graph theory and an unknown input observer is designed using the global relative output estimation error vector. Under the assumption that the uncertainties are bounded in zonotopes, the observer matrices are achieved by zonotopic method which makes the P-radius of the global augmented state estimation error zonotope is not increased. Next, the upper and lower boundaries of the augmented stated are calculated at each time instant via interval hull technique. Finally, simulation results illustrated by a multi-agent systems with four linearized model of VTOL aircraft are given. © 2021 ieee.

关键词： Multi agent systems

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Multi-Sensor Fusion Perception System in Train 10

Multi-Sensor Fusion Perception System in Train

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Gao, Hongfei Huang, Yongzhen Li, Haoran Zhang, Qichao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Watrix Technology Beijing Co. Ltd. China

ISBN: (纸本)9781665424233

Environment perception is one of the most crucial modules in a self-driving system. In an open subway environment, pedestrians, equipment boxes and other unknown obstacles often appear. the perception module is required to quickly and accurately recognize the obstacle and measures the corresponding distance data. this paper proposes a detection and ranging fusion method based on one Lidar and two different focal length cameras, which be applied in the subway system, to alert drivers to possible obstacles and assist brake. First, the relative transformation between Lidar and camera is calibrated to find the intrinsic and extrinsic matrices. Modified SSD network is trained on the self-built dataset to detect the potential obstacle and produces the final detection bounding box. then, rail track segmentation network RailNet is applied to obtain rail shape features, which offers a region of interest (ROI) for sensing systems in point cloud clustering algorithms. Further, the fusion information is utilized to estimate the distance of objects detected by the SSD detector. the proposed method can realize real-time pedestrian detection and range on the onboard embedded platform Jetson Xavier, which satisfies the subway environment's perception requirements. © 2021 ieee.

关键词： Optical radar

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Development of dual feedback Active Disturbance Rejection control driver 10

Development of dual feedback Active Disturbance Rejection Co...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Yang, Zhijun Ke, Bangwei Kuang, Junpeng Sun, Han State Key Laboratory for Precision Electronics Manufacturing Technology and Equipment Guangzhou510006 China Guangdong Provincial Key Laboratory for Micro-Nano Manufacturing Equipment and Technology Guangzhou510006 China

ISBN: (纸本)9781665424233

Friction is the main reason that affects the accuracy of the mechanical bearing positioner. the rigid-flexible coupling stage adopts an active disturbance rejection control strategy that regards elastic vibration as a disturbance. It can convert unmeasurable friction into easily measurable elastic deformation to ensure the positioning of the micro-stage accuracy. When the Active Disturbance Rejection control (ADRC) algorithm is used to deal with the errors caused by mass manufacturing and the geometric nonlinearity of large deformations, dual-channel synchronous feedback and low-delay disturbance calculation compensation are required. therefore, this article develops a hybrid ARM+FPGA architecture active disturbance rejection control driver for the rigid-flexible coupling stage's needs. the integrated drive and control system includes FPGA to realize dual-channel synchronous feedback, ARM to realize position loop, speed loop, active disturbance rejection calculation, and Intelligent Power Module(IPM) to realize current amplification. FPGA has the characteristics of high-speed real-time data acquisition and parallel work, which can realize dual-channel real-time synchronous feedback. Coupled with ARM's powerful computing and flexible control capabilities, the active disturbance rejection control driver of the hybrid ARM+FPGA architecture can meet the control needs of the rigid-flexible coupling stage. this paper verifies the feasibility of the drive system scheme by controlling the rotary stage and the motor. © 2021 ieee.

关键词： Feedback

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Physics-informed Recurrent Neural Networks for the Identification of a Generic Energy Buffer System 10

Physics-informed Recurrent Neural Networks for the Identific...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Lahariya, Manu Karami, Farzaneh Develder, Chris Crevecoeur, Guillaume IDLab Ghent University - Imec Ghent Belgium Ghent University Core Lab EEDT-DC Systems and Metal Engineering Dept. Electromechanical GentB-9052 Belgium

ISBN: (纸本)9781665424233

Energy storage is ubiquitous in industrial processes and comes in many forms such as material, chemical, electromechanical buffers. System identification of such energy buffers demands proper estimation/prediction of their physical quantities and unknown parameters. Once these parameters are determined, the identified model can be employed to predict the industrial process dynamics, which finally assist to build efficient control for these processes. this paper proposes physics-informed neural networks-based grey-box modeling methods for the identification of energy buffers. the underlying system dynamics are enforced on the neural network structure to ensure that the identified grey-box model follows the approximate physics. We define two novel grey-box models based on simple and recurrent neural network architectures and test these models for a generic energy buffer. Performance and training time for the proposed grey-box models are compared against a black-box baseline model. Results confirm that imposing the dynamic system's physics on the network improves the performance, and utilizing a recurrent architecture leads to a further improvement. © 2021 ieee.

关键词： Recurrent neural networks

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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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Active Disturbance Rejection control Oriented Rigid-flexible Coupling Rotary Stage Design 10

Active Disturbance Rejection Control Oriented Rigid-flexible...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Huang, Jianbin Yang, Zhijun Wei, Yutai Huang, Xiaohong Bai, Youdun State Key Laboratory of Electronics Precision Manufacturing Technology and Equipment Guangdong University of Technology Guangzhou510006 China Guangdong Provincial Key Laboratory of Micro-Nano Manufacturing Technology and Equipment Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9781665424233

High precision rotary stages are widely used in the field of optical radar, laser satellite communications and the national defense. However, due to the influence of bearing friction, the stick-slide phenomenon causes the problem of speed fluctuation and large return error. In this paper, a design method for a rigid-flexible coupling rotary stage is proposed, which is designed as flexible hinges linked rotary shaft and rotary stage, friction is acting on the rotary shaft, and the drive torque and detection elements are installed on the rotary stage. Friction disturbance is converted into elastic deformation disturbance, which reduce the perturbation bandwidth and make it easier to estimate and compensate. On the other hand, the elastic deformation disturbance can be obtained directly according to stiffness and damping by measuring the deformation range and rate of flexible hinges. the deviation caused by manufacturing error and nonlinearity is estimated and compensated by ADRC. Results show that the precision are improved by more 5 gimes only by changing the structure design. © 2021 ieee.

关键词： Error compensation

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Manufacturing Big data Modeling Based on KNN-LR Algorithm and Its Application in Product Design Business Domain 10

Manufacturing Big Data Modeling Based on KNN-LR Algorithm an...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Xiao, Yi Ren, Hongru Lu, Renquan Cheng, Shen Sch. of Automat. and Guangdong Prov. Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9781665424233

In product life cycle, it is very important to use the manufacturing big data to build prediction model and apply it to predict whether the design task of the product can be completed within the specified time. Most of the existing prediction models in manufacturing industry are built by a single algorithm or its improved version, and neglect the limitation of using a single forecasting algorithm, which may lead to poor forecasting accuracy. this paper aims to integrate the K-nearest neighbor classification algorithm and the logistic regression algorithm linearly in parallel to obtain the combined model which is called K-nearest neighbor-logistic regression (KNN-LR) in this paper, and use the combined model to predict whether the design task of the product can be completed within the specified time. Experimental results show that compared with the model built by a single algorithm, the combined model has better performance on model evaluation indicators such as accuracy, precision, F1 value. recall and classification error rate. © 2021 ieee.

关键词： Big data

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An Enhanced Fuzzy Repetitive-control Design and Optimisation Method based on 2D Performance Index 10

An Enhanced Fuzzy Repetitive-Control Design and Optimisation...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zhang, Manli Wu, Min Tian, Shengnan She, Jinhua 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 School of Engineering Tokyo University of Technology Hachioji192-0982 Japan

ISBN: (纸本)9781665424233

this paper concerns the design and optimisation of two-dimensional (2D) repetitive control of nonlinear systems based on the Takagi-Sugeno (T-S) fuzzy model. First, a continuous-discrete 2D model of nonlinear repetitive-control systems based on T-S fuzzy model is constructed by utilizing the 2D characteristics of continuous control and discrete learning actions in the repetitive-control process. Next, a fuzzy Lyapunov-Krasovskii functional derives the linear-matrix-inequality-based stability condition with a low conservatism. Two positive and two nonzero parameters in the fuzzy Lyapunov-Krasovskii functional tune the control and learning actions. then the particle swarm optimisation algorithm based on a 2D performance index searches for the best parameter combination, resulting in the optimal 2D controller gains. A numerical example is given to demonstrate the effectiveness of the method. © 2021 ieee.

关键词： Linear matrix inequalities

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Pattern Recognition of Traction Energy Consumption for Urban Rail Transit by Using Symbolic Aggregate Approximation 10

Pattern Recognition of Traction Energy Consumption for Urban...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zhang, Licheng Xun, Jing Zhang, Wei Li, Xi Zhang, Yanlong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd Beijing100044 China Subway Operation Technology Centre of Beijing Mass Transit Railway Operation Corporation Ltd Beijing100044 China

ISBN: (纸本)9781665424233

In the urban rail transit system, the traction energy consumption accounts for 40%-60% of the total energy consumption. there is a large amount of traction energy consumption data in time series format recorded by energy meters. Accurate analysis of traction energy consumption based on time series is in urgent demand for energy saving. In order to analyze the law of traction energy consumption, this paper proposes a pattern recognition method for traction energy consumption based on SAX (Symbolic Aggregate approXimation). the original time series of traction energy consumption is transformed by SAX and the sub-patterns are obtained. the traction energy consumption patterns are recognized by using K-means algorithm. To show the effectiveness and efficiency, we apply the proposed method to a data set from Beijing Subway, and find 3 representative patterns. We find that the recognized patterns of traction energy consumption appears coherence with the major services prescribed in the rolling stock scheduling plan. By calculating the similarity and comparing with these representative patterns, the days that differ from the typical patterns are judged as anomalies. © 2021 ieee.

关键词： Time series

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