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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261-270 订阅

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Modeling and control of Parafoil systems Based on CFD

Modeling and Control of Parafoil Systems Based on CFD

引用

data driven control and learning systems (ddcls)

作者： Wannan Wu Qinglin Sun Mingwei Sun Zengqiang Chen College of Computer and Control Engineering Nankai university Tianjin P. R. China

ISBN: (纸本)9781538626191

the calculation of canopy aerodynamic parameters plays an important part in the airdrop system. Based on the finite volume method, this paper calculates the aerodynamic parameters of the parafoil systems, and then the deflection and incision factors are estimated by the CFD output data. the obtained lift and drag coefficients instead of the traditional parameters based on lifting-line theory are incorporated into the aerodynamic equation of a parafoil system. the active disturbance rejection control strategy is applied to control the systems. the effectiveness of the proposed method can be demonstrated by the simulation results. the work in this paper may be a reference for the parafoil system design.

关键词： Aerodynamics Computational modeling Mathematical model Atmospheric modeling control systems Shape Force

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Federated learning With Dynamic Staleness Correction for Privacy Protection in Vehicular Networks 23

Federated Learning With Dynamic Staleness Correction for Pri...

引用

23rd ieee International conference on High Performance Computing and Communications, 7th ieee International conference on data Science and systems, 19th ieee International conference on Smart City and 7th ieee International conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Jiajia Wu, Hao Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China School of Electronics and Information Engineering Beijing Jiaotong University Beijing China Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing China

ISBN: (纸本)9781665494571

Edge intelligence combines mobile edge computing(MEC) with artificial intelligence(AI), which has great potential for improving the security and efficiency of data-driven intelligent transportation systems (ITS) and emerging Internet Of Vehicles (IOV) services. However, when big data and AI empower ITS, the information security issue in vehicular networks needs to be paid more attention. In order to protect the privacy and security of vehicular training data, we propose a privacy protection federated learning scheme with staleness asynchronous update to overcome the differences caused by vehicle heterogeneity. Moreover, unlike the traditional weighted average on the server side only by the number of samples, our solution introduces dynamic time weights according to the calculation and communication capabilities of different vehicles, to make full use of the previously trained local model. the proposed scheme is able to reduce traffic load of the network and improve learning performance while enhancing security and privacy. Experiment results demonstrate that in terms of communication cost and model accuracy, the performance of the proposed asynchronous federated learning is better than the benchmark algorithm, and it also achieves good performance under NIID data. © 2021 ieee.

关键词： Privacy data privacy Costs Smart cities Training data Telecommunication traffic Collaborative work

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Networked Iterative learning control for Nonlinear Switched Discrete-time systems with Random Measurement Packet Losses

Networked Iterative Learning Control for Nonlinear Switched ...

引用

data driven control and learning systems (ddcls)

作者： Ang-Ji Lin Shu-Ting Sun Xiao-Dong Li School of Data and Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781538626191

For nonlinear switched discrete-time systems with random measurement packet losses modeled by a Bernoulli-type stochastic sequence, this paper presents a P-type networked Iterative learning control (ILC) algorithm with an attenuating forgetting factor. In this ILC scheme, the random measurement packet losses are replaced by the desired output data. Under a given switching rule, the convergence of ILC tracking error in mathematical expectation in each of subsystems is proved by mathematical induction, and the convergent condition of the proposed networked P-type ILC algorithm is given. An illustrative simulation is used to verify the effectiveness of the proposed ILC algorithm.

关键词： Switches Switched systems Packet loss Discrete-time systems Convergence Iterative learning control

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Optimal Time Allocation of Point-to-Point Iterative learning control with Specifiewed Output Tracking

Optimal Time Allocation of Point-to-Point Iterative Learning...

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data driven control and learning systems (ddcls)

作者： Xingding Zhao Youqing Wang College of Information Science and Technology Beijing University of Chemical Technology Beijing College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao

ISBN: (纸本)9781538626191

this paper studies the optimal time allocation of point-to-point (P2P) iterative learning control (ILC) when tracking selected elements or linear combinations of elements in the outputs. the optimization framework of the problem is a two-stage design algorithm, which is addressed by integrating norm-optimal ILC and the gradient method. To test the performance of the proposed algorithm, we report the results of our simulation test using a gantry robot.

关键词： Resource management Optimization Robots Iterative learning control Hilbert space Target tracking Task analysis

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Fault Detection in the Closed-loop System Using One-Class Support Vector Machine

Fault Detection in the Closed-loop System Using One-Class Su...

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data driven control and learning systems (ddcls)

作者： Zhiang Li Xiangshun Li School of Automation Wuhan University of Technology Wuhan China

ISBN: (纸本)9781538626191

Feedback controller in closed loop usually makes the system more robust to external disturbances and makes faults difficult to detect. Here, an OCSVM based fault detection method is applied to detect faults in closed loops. By training data from normal samples and establishing OCSVM model, the fault detection of closed loop system is realized. this method not only overcomes the influence of non Gauss nonlinear process data to closed-loop process, but also considers the dynamic characteristics of the data. Finally the method is verified with the three-tank system.

关键词： Fault detection Kernel Valves Closed loop systems Fault diagnosis Support vector machines data models

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Markov Parameters Sequence Identification Oriented data-driven LQ=H∞ Robust Preview control

Markov Parameters Sequence Identification Oriented Data-Driv...

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data driven control and learning systems (ddcls)

作者： Kezhen Han Xiju Zong Shi Li School of Electrical Engineering University of Jinan Jinan China

ISBN: (纸本)9781538626191

In this paper, the data-driven robust preview control problem is addressed based on Markov parameters sequence identification and augmented modelling technique. the involved analysis and synthesis are composed of three parts. First, data-based state-space model is established by augmenting input/output data, finite window previewable signals and tracking errors. then, the Markov parameters sequence is identified, which enables the determination of data model matrices. In the following, the mixed linear quadratic (LQ) and H ∞ criterions are used to optimize the robust preview control gains, and the specified preview control policy containing data feedback control, integral operation and preview action is finally obtained. the application to injection velocity control of injection molding process verifies the effectiveness of proposed results.

关键词： Markov processes Covariance matrices control design State-space methods Process control Trajectory

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control Performance Assessment for ILC-controlled Batch Processes Based on MPC Benchmark

Control Performance Assessment for ILC-Controlled Batch Proc...

引用

data driven control and learning systems (ddcls)

作者： Juan Wang Youqing Wang College of Information Science and Technology Beijing University of Chemical Technology Beijing College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao

ISBN: (纸本)9781538626191

In this article, direct at the batch process controlled by iterative learning control (ILC), it proposes a more reasonable benchmark for the control performance assessment (CPA): the model predictive control (MPC) benchmark. First, ILC-controlled batch process is converted to a 2-D Fornasini-Marchesini (FM) model. On this basis, the cost function is constructed and the optimal learning law can be found. then the 2D MPC performance tradeoff surface for assessment is obtained. Finally, a set of simulation experiments prove the effectiveness and feasibility of the proposed method.

关键词： Benchmark testing Batch production systems Mathematical model Frequency modulation Cost function Performance evaluation

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Iterative learning control for Discrete Singular systems with Randomly Varying Trial Lengths

Iterative Learning Control for Discrete Singular Systems wit...

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data driven control and learning systems (ddcls)

作者： Jiahuan Liu Huiping Tian Senping Tian Xiangyang Li School of Automation Science and Engineering South China University of Technology Guangzhou China Guangdong Women’s Polytechnic College Guangzhou China

ISBN: (纸本)9781538626191

this paper researches iterative learning control for a class of singular systems with randomly iteration varying lengths. Based on an equivalence decomposition of discrete singular systems, a new learning algorithm with a stochastic variable and moving average operator is used to cope with the state tracking problem under non-uniform trial lengths circumstance. the stochastic variable is included both in tracking error and control input. Furthermore, the convergence condition of the proposed learning scheme is put forward and strictly proved. In the end, a numerical example is presented to demonstrate the effectiveness of the theoretical results.

关键词： Iterative learning control Convergence Tracking Trajectory Mathematical model Probability distribution

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Iterative learning Fault Estimation Algorithm for Time-delay systems Based on Extended Observer

Iterative Learning Fault Estimation Algorithm for Time-delay...

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data driven control and learning systems (ddcls)

作者： Hongfeng Tao Qiang Wei Key laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan University Wuxi

ISBN: (纸本)9781538626191

For a class of multivariable linear, time-delay systems with actuator fault and measurement bounded disturbances in output, an iterative learning fault estimation algorithm based on extended observer is proposed. the extended observer is designed in terms of the linear matrix inequality technique such that the states and disturbances can be estimated simultaneously in every trials, then the faults and disturbances can be separated for avoiding impact to each other. Afterwards, the iterative learning fault estimation algorithm by defining estimation residual is chosen to adaptively approximate the actuator fault with initial error, then the necessary and sufficient conditions for the existence of the learning algorithm is given through λ norm theory and Bellman-Gronwall inequality, and the uniform convergence criteria of the control algorithm is also discussed. Simulation results verify the feasibility and effectiveness of this algorithm.

关键词： Observers Iterative algorithms Actuators Linear matrix inequalities Approximation algorithms Heuristic algorithms

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An Improved Model-free Adaptive Predictive control Algorithm For Nonlinear systems With Large Time Delay

An Improved Model-free Adaptive Predictive Control Algorithm...

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data driven control and learning systems (ddcls)

作者： Na Dong Yu Feng Xue-shuo Han Ai-guo Wu School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781538626191

For control problem of nonlinear time-delay systems, we improve the control input criteria function of the model-free adaptive control by adding the sum of control output error into the control input criteria function. Also, the concept of predictive control has been incorporated into the improved algorithm. Typical linear and nonlinear large time-delay systems are introduced for simulation comparison tests. the simulation results show that the improved model-free predictive control algorithm can achieve stable output, better control effect and faster response. thus, the effectiveness of this improved model-free predictive control method is fully illustrated.

关键词： Predictive models Prediction algorithms Adaptation models Predictive control Mathematical model data models Heuristic algorithms

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