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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是251-260 订阅

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Finite-level Quantized Iterative learning control by Encoding-Decoding Mechanisms

Finite-level Quantized Iterative Learning Control by Encodin...

引用

data driven control and learning systems (ddcls)

作者： Chao Zhang Dong Shen Beijing University of Chemical Technology Beijing P. R. China

ISBN: (纸本)9781538626191

this paper studies the zero-error tacking problem of finite-level quantized iterative learning control using an encoding-decoding method, where both measurement and actuator side quantization and transmission are considered. In particular, the system output is encoded, quantized, transmitted and decoded in sequence for input updating of the next iteration. then the generated input is transmitted through networks following the same procedure as the output transmission for plant input updating. the zero-error convergence of the proposed scheme is strictly proved and a numerical simulation is provided to demonstrate the effectiveness of the proposed scheme.

关键词： Quantization (signal) Decoding Actuators Iterative learning control Convergence Encoding learning systems

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Repetitive learning control for a Class of Nonlinear systems

Repetitive Learning Control for a Class of Nonlinear Systems

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data driven control and learning systems (ddcls)

作者： Mingxuan Sun He Li Qiang Chen College of Information Engineering Zhejiang University of Technology Hangzhou P. R. China

ISBN: (纸本)9781538626191

this paper presents a design method of repetitive learning control for a class of nonlinear uncertain systems. the control design is carried out by the estimation of the desired control and the norm-bounding uncertainty. By the adaptive learning techniques, the desired control is taken as a parametric uncertainty with regressor one. In addition, the variation of the nonlinearity, characterized by the bounding function, can be handled to alleviate the requirement for the knowledge about the system dynamics. the upper bound of the control gain is only required in this scheme. the boundedness of variables in the closed-loop system and the asymptotical convergence of the tracking error are established. And numerical results are presented to demonstrate the effectiveness of the proposed control scheme.

关键词： Uncertainty Upper bound Convergence control design System dynamics Nonlinear systems

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Adaptive Fuzzy Consensus control for Unknown State-Delay Nonlinear Multiagent System

Adaptive Fuzzy Consensus Control for Unknown State-Delay Non...

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data driven control and learning systems (ddcls)

作者： Fei Yan Baolong Guo Xidian University Xian P. R. China

ISBN: (纸本)9781538626191

this paper focuses on the leader-following consensus control problem of nonlinear multiagent systems. the fuzzy logic systems are used to approximate the system uncertainties from the unknown nonlinear dynamics, and a novel adaptive fuzzy controller is presented by combining the Lyapunov-Krasovskii functionals. With the help of Lyapunov-Krasovskii functionals the state-delay of systems is compensated. A major advantage of the proposed adaptive consensus method is that it can greatly reduce the computation burden. Finally, one simulation example is given to verify the effectiveness of the designed algorithm.

关键词： Fuzzy logic Multi-agent systems control systems Nonlinear systems Artificial neural networks Fuzzy control

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Decentralized Iterative learning control for Large-Scale Interconnected Non-Affine Nonlinear Discrete-Time systems

Decentralized Iterative Learning Control for Large-Scale Int...

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data driven control and learning systems (ddcls)

作者： Lili Du Qin Fu School of Mathematics and Physics Suzhou University of Science and Technology P. R. China

ISBN: (纸本)9781538626191

this thesis discusses the decentralized iterative learning control for large-scale discrete-time single-input single-output (SISO) systems, which is interconnected by non-affine nonlinear systems. In view of the structure of the system, the P-type learning algorithm is constructed. Under certain assumptions, the algorithm can make sure that the error precision required in each subsystem is attained through repeated iteration. the given example indicates that the proposed scheme is effective.

关键词： Discrete-time systems Interconnected systems Iterative learning control Trajectory Task analysis Mathematical model

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Unsupervised Fault Detection Based on Laplacian Score and TEDA

Unsupervised Fault Detection Based on Laplacian Score and TE...

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data driven control and learning systems (ddcls)

作者： Chuyue Lou Xiangshun Li Wuhan University of technology Wuhan P. R. China

ISBN: (纸本)9781538626191

the drawback to Typicality and Eccentricity data Analytics(TEDA), a classic unsupervised learning algorithm, is that TEDA requires strict priori knowledge during the stage of data preprocessing. In view of the disadvantage, a method of unsupervised fault detection called Laplacian Score with TEDA (LS-TEDA) is proposed. Features are selected by LS and unsupervised fault detection is realized by using TEDA in this method. LS-TEDA has been applied with Lublin Sugar Factory and the result shows high accuracy in fault detection.

关键词： Fault detection Laplace equations Feature extraction Sugar Production facilities Monitoring Unsupervised learning

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Quantized Iterative learning control for Formation of Multi-agent System

Quantized Iterative Learning Control for Formation of Multi-...

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data driven control and learning systems (ddcls)

作者： Chenlong Li Yong Fang Jialu Zhang Key laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

ISBN: (纸本)9781538626191

this paper investigates the formation control problem for discrete-time multi-agent systems with switching network topologies and data quantization. It is assumed that the tracking error signals of individual agent are quantized before they are transmitted into the iterative learning controller. However, quantification of data can lead to quantization error, which seriously impacts the performance of multi-agent systems. Based on the nearest neighbor interaction rule, a quantized iterative learning approach is given to overcome the quantization error in the occasion of switching network topologies and guarantee the accurate formation of multi-agent systems simultaneously. Simulation results are provided to verify the effectiveness of the proposed method.

关键词： Multi-agent systems Quantization (signal) Network topology Switches Topology Iterative learning control

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Sliding Mode control of A Class of Nonlinear systems

Sliding Mode Control of A Class of Nonlinear Systems

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data driven control and learning systems (ddcls)

作者： Xinxin Liu Feng Hu Xiaojie Su College of Automation Chongqing University Chongqing P. R. China

ISBN: (纸本)9781538626191

this paper concerns the sliding mode control problems for a class of nonlinear systems, named repeated scalar nonlinear systems, with a pre-scribed performance. Firstly, observer based on event-triggered scheme is constructed to well estimate the system states. Corresponding sliding mode dynamics is obtained. then, sliding mode controller is designed to keep that the closed-loop system trajectories to reach the pre-specified sliding region in finite time. Finally, sufficient conditions of sliding mode dynamics and error dynamics to be stochastic stable with a pre-scribed performance are provided.

关键词： Observers Sliding mode control Closed loop systems Nonlinear dynamical systems Trajectory

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Prediction of the Li-Ion Battery Capacity by Using Event-driven Acquisition and Machine learning 7

Prediction of the Li-Ion Battery Capacity by Using Event-Dri...

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7th International conference on Event-Based control, Communication and Signal Processing (EBCCSP)

作者： Qaisar, Saeed Mian AbdelGawad, Amal Essam ElDin Effat Univ Coll Engn Jeddah 22332 Saudi Arabia Effat Univ Energy & Technol Ctr Commun & Signal Proc Lab Jeddah 22332 Saudi Arabia

ISBN: (纸本)9781665436977

the battery is a crucial element of modern power systems and it is utilized habitually in different vital applications such as electric vehicles, drones, avionics and mobile phones. Among various batteries technologies the Li-Ion batteries are widely used. It is mainly because of their compactness, longer life and high power capacity. On the other hand, due to the disadvantage of Li-ion batteries being expensive, their use is monitored using battery management systems (BMSs) to optimize their performance and ensure they last longer. the extensive processing resources that modern BMSs need can result in higher overhead power consumption. this study focuses on upgrading the present Li-ion BMSs through redesigning their associative data acquisition and processing chains differently. It aims at enhancing the data acquisition and estimation mechanisms for the Li-ion batteries' capacities. It utilizes a novel event-driven mechanism for extracting the intended Li-Ion cell parameters. the event-driven approach brings notable compression gain compared to fix-rate conventional counterparts. the mined attributes are onward conveyed to the robust machine learning algorithms for prediction. the 5-fold cross-validation approach is used for prediction performance evaluation. the achieved correlation coefficient and minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are respectively 0.9996, 0.0038 and 0.0054 respectively. It shows the feasibility of incorporating the proposed approach in contemporary BMSs.

关键词： Event-driven processing Li-Ion battery Battery capacity Features extraction Compression Approximation error

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the Position Tracking control System of Induction Motors Based on Stator-Flux-Oriented Vector control

The Position Tracking Control System of Induction Motors Bas...

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data driven control and learning systems (ddcls)

作者： Ke-Yu Zhuang School of Automation and Electrical Engineering Qingdao University of Science & Technology Qingdao China

ISBN: (纸本)9781538626191

Asynchronous motor is a common motor in electric vehicle. In this paper, the position tracking control system based on stator flux oriented vector control (SFOVC) combining advantages of rotor flux oriented vector control and direct torque control is studied. A continuous closed-loop controller is adopted to correct the calculated position angle of stator flux and the torque ripple is small. this method is less affected by the parametric variation of rotor, with accurate stator flux observation and high position tracking accuracy. Simulation results demonstrate the effectiveness of this new control strategy.

关键词： Stators Induction motors control systems Rotors Mathematical model Torque Machine vector control

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A data-driven Process Monitoring Approach with Disturbance Decoupling*

A Data-Driven Process Monitoring Approach with Disturbance D...

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data driven control and learning systems (ddcls)

作者： Hao Luo Kuan Li Mingyi Huo Shen Yin Okyay Kaynak School of Astronautics Harbin Institute of Technology Harbin P. R. China Mechatronics Research and Application Center Bogazici University Istanbul Turkey

ISBN: (纸本)9781538626191

this paper presents the study on the data-driven process monitoring system design for the dynamic processes with deterministic disturbance. the basic idea of the proposed methods are to identify the stable kernel representation (SKR) of the dynamic process by projecting the process data into different subspaces. With the help of the projection, the kernel subspace, which delivers the residual decoupled from the disturbance, can be further determined. Based on the identified data-driven SKRs, process monitoring systems are developed. the performance and effectiveness of the proposed schemes are verified and demonstrated through the numerical study on randomly generated systems.

关键词： Monitoring Mathematical model Kernel Linear systems MIMO communication Aerospace electronics Process control

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