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检索条件"任意字段=9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020"

共 506 条记录，以下是191-200 订阅

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Nonsingular Terminal Sliding Mode control for PMLSM Based on Disturbance Observer 9

Nonsingular Terminal Sliding Mode Control for PMLSM Based on...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Ding, Bo Xu, Dezhi Yang, Weilin Bi, Kaitao Yan, Wenxu Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9781728159225

In order to achieve high precision speed control of the permanent magnet linear synchronous motor (PMLSM), this paper proposed a nonsingular terminal sliding mode control (NTSMC) with nonlinear disturbance observer (NDO). First, the model of PMLSM based on field oriented control (FOC) is given. then, a NTSMC is proposed via backstepping. the controller can make the states reach the manifold in finite time. With the motor moving, it will be disturbed by friction and load, which will affect the system stability. therefor, a NDO is introduced to estimate the unknown disturbance. the observed value is dynamically compensated to the NTSMC, which improves the control precision of the PMLSM system. Finally, the proposed algorithm is applied in dSPACE compared with PI, the experimental results show the proposed controller has better dynamic and static performance.

关键词： permanent magnet linear synchronous motor nonsingular terminal sliding mode control nonlinear disturbance observer field oriented control

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Adaptive Output Feedback control for a Multi-Motor Driving System with Completely Tracking Errors Constraint 9

Adaptive Output Feedback Control for a Multi-Motor Driving S...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Wang, Minlin Dong, Xueming Ren, Xuemei Changcheng Inst Metrol & Measurement Dept Inertia Beijing 100095 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

ISBN: (纸本)9781728159225

this paper proposes an adaptive output feedback controller for the multi-motor driving system (MDS) to achieve the precision motion control with completely tracking errors constraint. By adopting a K-filter observer to estimate the unknown system states, a modified barrier Lyapunov function (MBLF) is integrated into the adaptive output feedback control to make all the tracking errors constrained within the prescribed bounds. Since the MBLF is suitable for both constrained and unconstrained conditions, it expands the application filed of the classical Lyapunov function. Moreover, minimize learning parameter technique is utilized into the adaptive law design, which improves the adaptive learning process greatly. the system stability is proven by Lyapunov theory. the simulations are conducted on a four-motor driving system to illustrate the efficiency of the proposed controller.

关键词： MDS K-filter observer adaptive output feedback control MBLF

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Intrusion Detection based on Non-negative Positive-unlabeled learning 9

Intrusion Detection based on Non-negative Positive-unlabeled...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Lv, Sicai Liu, Yang Liu, Zhiyao Chao, Wang Wu, Chenrui Wang, Bailing Harbin Inst Technol Weihai Sch Comp Sci & Technol Weihai 264200 Peoples R China Harbin Inst Technol Weihai Res Inst Cyberspace Secur Weihai 264200 Peoples R China China Ind Control Syst Cyber Emergency Response T Beijing 100040 Peoples R China

ISBN: (纸本)9781728159225

Due to the diversity of network traffic flow, intrusion detection is usually studied as an anomaly detection problem. In this paper, Positive-unlabeled with Non-negative Risk Estimator(nnPU) learning is introduced for intrusion detection. the cyber attacks is treated as positive samples in PU learning. A risk estimator is raised to estimates the binary classification loss. For data imbalance in intrusion detection, we improve the risk estimator of nnPU through focal loss(FL-nnPU). the dynamic weights in focal loss is used to balance the small class prior. the experiments result show that FL-nnPU have a close performance to binary classification, and it performs better than nnPU under data imbalance problems.

关键词： intrusion detection positive-unlabeled learning risk estimator data imbalance

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data-driven Adaptive PID control of Unknown Quadrotor UAVs 9

Data-Driven Adaptive PID Control of Unknown Quadrotor UAVs

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9th ieee data driven control and learning systems conference (ddcls)

作者： Nan, Dong Li, Jiapeng Weng, Yongpeng Lian, Lian Yu, Cunqian Li, Shaowu Dalian Maritime Univ Coll Marine Elect Engn Dalian 116026 Peoples R China Shenyang Univ Chem Technol Sch Informat Engn Shenyang 110142 Peoples R China Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Peoples R China Hubei Univ Nationalities Coll Informat Engn Enshi 445000 Peoples R China

ISBN: (纸本)9781728159225

In this paper, a novel data-driven adaptive PID control (DAPIDC) scheme is developed for unknown quadrotor UAVs. Main contributions are as follows: 1) By applying the virtual control strategy and arithmatic inverter technique, discrete incremental PID control laws are first designed for position subsystem and attitude subsystem;2) On this basis, an efficient coefficient adjustment scheme based on data-driven strategy is proposed, which can adjust PID controller parameters online, thereby contributing to strong adaptability to the unknown dynamics, uncertainties and disturbances;3) Finally, the simulation studies are carried out and compared with the conventional PID method, so that the effectiveness and superiority of designed DAPIDC scheme is demonstrated.

关键词： data-driven PID control Adaptive control Quadrotor UAVs

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Active Disturbance Rejection control for Discrete systems 9

Active Disturbance Rejection Control for Discrete Systems

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9th ieee data driven control and learning systems conference (ddcls)

作者： Wang, Haiyan Pan, Tianhong Gao, Zhiqiang Jin, Huiyu Anhui Univ Sch Elect Engn & Automat Hefei 230601 Anhui Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China Cleveland State Univ Fenn Coll Engn Cleveland OH 44115 USA Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China

ISBN: (纸本)9781728159225

A form of active disturbance rejection control (ADRC) is proposed for a kind of discrete systems in this paper. the state space form of the system is firstly formulated. then the extended state observers (ESO) with and without model information are presented to estimate states and total disturbance. the control law is formulated to reject disturbance and to track a given trajectory. As a case study, the semiconductor manufacturing process is used to validate the proposed solution. Comparing with the exponentially weighted moving average controller, simulations indicate that the proposed discrete ADRC is effective in cancelling disturbance and in tracking the desired target.

关键词： Active Disturbance Rejection control Discrete System Semiconductor Manufacturing Process Exponentially Weighted Moving Average

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Automatic Generation control of Isolated Two-area Microgrid with Multiple Resources Based on learning Optimization 9

Automatic Generation Control of Isolated Two-area Microgrid ...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Feng, Changyou Wang, Penghu Wu, Haiwei Huang, Li Tang, Hao Natl Elect Power Dispatching & Control Ctr Beijing 100031 Peoples R China Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China State Grid Jiangsu Elect Power CO LTD Nanjing 210024 Peoples R China

ISBN: (纸本)9781728159225

this paper studies the automatic generation control (AGC) problem of isolated two-area multiple source microgrid energies under the influence of stochasticity and volatility of renewable energies and loads. A Load Frequency control (LFC) model of isolated two-area microgrid with multiple distributed energy resources and energy storage device is established. the wind power, the photovoltaic output and load fluctuation are formulated as discrete-time Markov processes based on their stochastic dynamic characteristics. the performance of the proposed learning optimization algorithm combined with the AGC principle of the power grid is demonstrated on microgrid system. At last, comparing SAQ-learning with traditional Q-learning and PI controller, simulation results illustrated the rationality of the proposed microgrid model and the expected performance of the controller.

关键词： Microgrid Distributed energy Automatic generation control Stochastic dynamic characteristic learning optimization algorithm

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Industrial Process Modeling and Fault Detection with Recurrent Kalman Variational Autoencoder 9

Industrial Process Modeling and Fault Detection with Recurre...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Zhang, Zheng Zhu, Jinlin Liu, Yang Ge, Zhiqiang Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639815 Singapore Zhejiang Univ Coll Control Sci & Engn Hangzhou 310058 Peoples R China

ISBN: (纸本)9781728159225

this article proposes the recurrent Kalman variational autoencoder, an end-to-end trainable framework for process modeling and fault detection. Based on the backbone of variational autoencoder, our research focuses on the dynamic and nonlinear properties that implied in the industrial process. For the dynamics describing, the Kalman filter is integrated to estimate the hidden state uncertainty due to process noise, and hence to identify the time-domain correlation in the hidden space. While for the nonlinear evolution in the hidden mapping, the dynamic parameter network is constructed for recurrent updating, so as to further improve the inference accuracy. Finally, for process monitoring, a fault detection panel is designed in the latent and residual domains generated by the deep model. through the simulation of the industrial benchmark Tennessee Eastman Process, the superiority of the proposed method is evaluated and discussed.

关键词： Nonlinear industrial monitoring Fault detection Kalman filter Variational inference Recurrent

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A Deep learning Method for Rolling Bearing Fault Diagnosis through Heterogeneous data 9

A Deep Learning Method for Rolling Bearing Fault Diagnosis t...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Zhou, Wei Hou, Yandong Henan Univ Inst Comp & Informat Engn Kaifeng 475004 Peoples R China

ISBN: (纸本)9781728159225

Vibration signals of rolling bearing have multiple heterogeneous forms. Traditional fault diagnosis methods use 1D time-series signals or converted 2D signals for fault diagnosis. However, using the former will lose the spatial neighborhood features;using the latter will ignore time-series features, which caused information waste. In this paper, a new heterogeneous form of bearing vibration signals is proposed to address the problem. Our contributions of include: First, we proposed dynamic waveform sequences, which is a new heterogeneous form and can simultaneously reflect time-series features and spatial neighborhood features in vibration signals. Second, the CCLSTM (Conv-ConvLSTM) model is designed to extract the above two features layer by layer. Relying on the powerful feature extraction capability of CCLSTM, it is possible to simultaneously extract the time-series features and spatial neighborhood features in a single fault diagnosis network. the experimental verification through real bearing fault data sets shows that this method can effectively improve the diagnostic accuracy.

关键词： fault diagnosis deep learning heterogeneous data feature mining rolling bearing

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Ride Quality Improvement for High-Speed Railway Based on D-Type Iteration learning control 9

Ride Quality Improvement for High-Speed Railway Based on D-T...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Yang, Lu-Ping Zhu, Qiao Ni, Jun Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Sichuan Peoples R China

ISBN: (纸本)9781728159225

though differential-type (D-type) control method and iteration learning control (ILC) are both classic control strategy,ILC is rarely used for lateral control of trains. In this paper,a D-type ILC is proposed to suppress the lateral vibration of train body and improve ride quality. First,the dynamic model of lateral and yaw motions for train body is introduced,where external disturbances caused by bogies are considered. Based on the repeatability of train operation,a iteration learning controller in combination with D-type control method is employed to better suppress the lateral motions of train body. Unlike traditional PID controller, the D-type iterative learning controller can make good use of the train's repeatability to adjust the input force to the actuator. then,the convergence condition is analyzed to ensure the rationality of the proposed algorithm. In order to verify the effectiveness of proposed controller,the co-simulation of a full-scale train model with German high interference track irregularities in SIMPACK and the control flow in SIMULINK is established to evaluate the effectiveness by comparing with a open-loop system.

关键词： High-speed train(HST) iteration learning control(ILC) lateral control ride quality

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Low-rank Shared Dictionary learning with Incoherence Constraint for Endoscopic Gastrointestinal Image Classification 9

Low-rank Shared Dictionary Learning with Incoherence Constra...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Ma, Yue Shen, Zixin Li, Sheng Chang, Liping Zhu, Jinhui He, Xiongxiong Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China Zhejiang Univ Affiliated Hosp 2 Sch Med Hangzhou 310023 Zhejiang Peoples R China

ISBN: (纸本)9781728159225

Endoscope has been widely used in clinical examination of gastrointestinal diseases. Many automatic endoscopic image classification algorithms based on dictionary learning are proposed to assist doctors in diagnosing diseases, where the learning method of shared dictionary and class-specific dictionaries enables training dictionary to be more discriminative. Nevertheless, in the process of dictionary learning, the appearance of common features in class-specific dictionaries may cause low classification accuracy. To remedy this deficiency, herein we introduce a coherence constraint between low-rank shared dictionary and class-specific dictionaries. the proposed dictionary learning method is applied to the classification system of endoscopic gastrointestinal images, including normal, polyp and ulcer images, whose experimental results prove that it has promising classification performance.

关键词： Shared dictionary and class-specific dictionaries Discriminative dictionary learning Image classification Incoherence Low-rank

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