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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351-360 订阅

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A Nonlinear Self-tuning control Method Based on Neural Wiener Model

A Nonlinear Self-tuning Control Method Based on Neural Wiene...

引用

data driven control and learning systems (ddcls)

作者： Bi Zhang Xin-Gang Zhao Zhuang Xu Ming Zhao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781538626191

In this work, a novel nonlinear self-tuning adaptive control scheme based on the neural Wiener model has been proposed to copy with a class of nonlinear uncertain systems. First the parameterization model with uncertain parameters is derived based on a linear transfer function model followed by neural networks. then based on the performance index, the adaptive control strategy includes the system parameters identification and the control law calculation. Since the networks are linearly described by some basis functions, the closed-loop system stability can be ensured under some realistic assumptions. Finally, the proposed controller is applied to a pH control problem. the simulation results have demonstrated that the proposed nonlinear self-tuning control method is applicable, especially for its reliable set-point tracking and adaptive abilities.

关键词： Adaptation models Adaptive control Stability analysis Radial basis function networks Transfer functions Closed loop systems

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Bayesian Regularized Gaussian Mixture Regression with Application to Soft Sensor Modeling for Multi-Mode Industrial Processes

Bayesian Regularized Gaussian Mixture Regression with Applic...

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data driven control and learning systems (ddcls)

作者： Jingbo Wang Weiming Shao Zhihuan Song State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781538626191

the Gaussian mixture regression (GMR) is an effective approach to predict those difficult-to-measure quality variables for industrial processes with multiple operating modes. However, the GMR easily gets stuck into overfitting in the scenario of insufficient labeled samples, particularly when the dimensionality of the secondary variables is high. To alleviate this issue, this paper proposes the Bayesian regularized GMR (BGMR), and applies it to soft sensor modeling. In the BGMR, an alternative model structure, which explicitly considers the functional dependency between the primary and secondary variables, is presented to facilitate the Bayesian regularization that is widely used for anti-overfitting. In addition, an efficient learning procedure is developed for the BGMR based on the expectation-maximization algorithm. the performance of the BGMR is evaluated through two case studies including a numerical example and a real-life industrial process, which demonstrates the effectiveness of the proposed approach.

关键词： Probability density function Bayes methods Covariance matrices Testing Predictive models Process control Mathematical model

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Network-based Iterative learning control Approaches with Communication Delay Adjustment Factors for LTI systems

Network-based Iterative Learning Control Approaches with Com...

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data driven control and learning systems (ddcls)

作者： Jian Liu Xiaoe Ruan Center for Complex Systems Xidian University China Department of Applied Mathematics Xian Jiaotong University China

ISBN: (纸本)9781538626191

the paper develops two novel network-based iterative learning control approaches with communication delay adjustment factors for SISO LTI systems. Suppose that communication delay is subject to 0-1 Bernoulli distribution. In these two approaches, the actual system input is synchronous at the previous iteration if the system input is delayed at the current iteration, otherwise it is a linear combination of the synchronous system inputs at the current and previous iterations, where the coefficients are dependent upon the input communication delay probability. Two strategies are given for the output signals used by the ILC unit. One is the same as that for the actual system input; as for the other one, the actually utilized output is the synchronous desired output if the system output is delayed, otherwise it is a linear combination of the synchronous system output at the current iteration and the synchronous desired output, where the coefficients are dependent upon the output communication delay probability. It is shown that the expectation of the system output is convergent to the desired output under certain conditions. Finally, we use an example to illustrate the effectiveness of the developed NILC approaches.

关键词： Delays Iterative learning control Linear systems Discrete-time systems Trajectory Electronic mail

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Iterative Identification for A Class of Closed-loop systems Based on A Greedy Algorithm

Iterative Identification for A Class of Closed-loop Systems ...

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data driven control and learning systems (ddcls)

作者： Junyao You Huan Xu Yanjun Liu Jing Chen School of Internet of Things Engineering Jiangnan University Wuxi P. R. China Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi P. R. China

ISBN: (纸本)9781538626191

A compressive sampling matching pursuit (CoSaMP) iterative algorithm is proposed in this paper to identify parameters and time-delays of a class of closed-loop systems where the forward channel is a CARMA model. Due to the unknown time-delays of both the feedback controller and the controlled plant, a high dimensional identification model with a sparse parameter vector is derived by using an overparameterized method. then combining the CoSaMP algorithm with the iterative idea, the parameter vector is estimated and the unmeasurable noise items are updated in each iteration. Finally, the parameters of the feedback controller are extracted based on the model equivalence principle and time-delays are estimated according to the sparse characteristic of the parameter vector. the proposed method can simultaneously estimate the parameters and time-delays from a small number of sampled data. the simulation results illustrate that the proposed algorithm is effective.

关键词： Matching pursuit algorithms Closed loop systems Mathematical model Greedy algorithms Computational modeling Iterative algorithms

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Vector control of Semi-Submerged Ship Dynamic Positioning Based on Model-Free Adaptive Sliding Mode

Vector Control of Semi-Submerged Ship Dynamic Positioning Ba...

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data driven control and learning systems (ddcls)

作者： Wenlong Yao Jiali Wang Ronghu Chi School of Automation and Electrical Engineering Qingdao University of Science and Technology Qingdao China

ISBN: (纸本)9781538626191

the model-free adaptive sliding mode vector control of propulsion motor is proposed for the semi-submerged ship dynamic positioning system for the problem of dynamic positioning propulsion motor control system with uncertain dynamics and load variations during rough sea conditions. the dynamic linear equation of dynamic positioning propulsion motor is derived. the convergence of the model and the sliding mode control method proves that the pseudo-partial derivative can be adjusted online to ensure the uniform and bounded of tracking error for the propulsion motor control system, and the performance of semi-submerged ship dynamic positioning system based on model-free adaptive sliding mode vector control and self-tuning PI vector control are compared. the simulation results show that the improved vector control has the characteristics of faster convergence speed and smaller steady-state error for the dynamic positioning propulsion motor.

关键词： Mathematical model Permanent magnet motors Synchronous motors Adaptation models Propulsion Machine vector control Torque

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An Arm Isolation and Reconfiguration Fault Tolerant control Method Based on data-driven Methodology for Cascaded Seven-level Inverter

An Arm Isolation and Reconfiguration Fault Tolerant Control ...

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data driven control and learning systems (ddcls)

作者： Jiahui Zhang Zhuo Liu Tianzhen Wang M.E.H. Benbouzid Yide Wang Logistics Engineering College Shanghai Maritime University Shanghai China University of Brest Brest France

ISBN: (纸本)9781538626191

Inverts, especially multi-level inverters are widely used in many fields, such as industrial production, transportation, aviation and so on. So great significance should be attached to the diagnosis and fault tolerance of inverters to keep the stability of systems. data-driven approaches make full use of the process data to monitor the systems, so the voltage signals are collected firstly and then preprocessed and processed by specific strategy, fault labels will be produced hereafter. When the fault labels from data-driven fault detection and diagnosis system are generated, relevant fault tolerant control method will be activated in fault tolerant control system. Some measurements are necessary to achieve the higher utilization ratio of healthy IGBTs and sinusoidal output voltage. Based on above consideration, a group isolation and reconfiguration fault tolerant control method based on data-driven methodology for cascaded seven-level inverter is proposed here to reconfigure the SPWM, in which every H-bridge is divided into two groups. the simulation of cascaded seven-level inverter is built and the result indicates that the utilization of healthy IGBTs is improved.

关键词： Insulated gate bipolar transistors Inverters Fault tolerant control Fault tolerant systems Mathematical model Modulation

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Resilient Consensus with Switching Networks and Double-Integrator Agents

Resilient Consensus with Switching Networks and Double-Integ...

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ieee data driven control and learning systems conference

作者： Jinbo Huang Yiming Wu Liping Chang Xiongxiong He Sheng Li College of Information Engineering Zhejiang University of Technology Hangzhou 310023 P. R. China School of Cyberspace Hangzhou Dianzi University Hangzhou 310018 P. R. China

ISBN: (纸本)9781538626191

In this paper, we investigate the resilient consensus problem for the second-order multi-agent system communicating via switching networks. the term resilient means that the control protocols should consider the presence of attacks by some malicious agents. Assuming that the maximum number of malicious agents in the neighborhood of each agent is bounded and known, we propose a local neighbors' information-based on distributed consensus protocol suitable for time-varying topologies to deal with the malicious attacks. It is shown that if the union of communication graphs over a bounded period satisfies certain network robustness property, the states of all normal agents can be guaranteed to reach an agreement resiliently. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.

关键词： Multi-agent systems Resilient consensus Double-integrator systems Time-varying

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Modified P-Type ILC for High-Speed Trains with Varying Trial Lengths

Modified P-Type ILC for High-Speed Trains with Varying Trial...

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ieee data driven control and learning systems conference

作者： Qiongxia Yu Xuhui Bu Ronghu Chi Zhongsheng Hou School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 P. R. China School of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao 266042 P. R. China Advanced Control Systems Laboratory Beijing Jiaotong University Beijing 100044 P. R. China

ISBN: (纸本)9781538626191

High-speed trains always operate from the same departure station to the same terminal station and hence iterative learning control (ILC) is an appropriate approach for automatic train control. However, due to complex environment and unknown uncertainties, the train may not arrive at the terminal station on time, or earlier and later than the schedule time in each operation. To address this problem, a modified proportional-type (P-type) ILC is presented where the trial length in each operation can be randomly varying. Moreover, the convergence condition in 2-norm is also derived through rigorous analysis. the effectiveness of the modified P-type ILC is further verified through simulations.

关键词： High-Speed Train ILC Varying Trial Length

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Filtering Identification for Multivariate Hammerstein systems with Coloured Noise Using Measurement data

Filtering Identification for Multivariate Hammerstein System...

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data driven control and learning systems (ddcls)

作者： Linwei Li Xuemei Ren Yongfeng Lv School of Automation Beijing Institute of Technology Beijing P. R. China

ISBN: (纸本)9781538626191

In this paper, based on the measurement data, the identification of the multivariate Hammerstein controlled autoregressive moving average system is investigated. To facilitate the parameter identification, the considered system is transferred to a regression identification model in which the bilinear parameter and linear parameter are included in the identification model. To solve the bilinear parameter estimation problem, with the help of the hierarchical identification principle, two new identification models are constructed in which the each model is linear to parameter vector. For each identification model, a novel filtering identification algorithm is put forward to interactively estimate the parameters of the each model based on hierarchical identification principle. Filtering technique is used to improve the estimation accuracy of the presented algorithm, and the hierarchical identification idea is exploited to decrease the calculation burden of the proposed method. the conditions of convergence are introduced by using the martingale convergence theorem. Contrast examples indicate that the proposed method has a better identification performance than several existing estimation approaches.

关键词： Estimation Computational modeling Convergence Colored noise Filtering Noise measurement data models

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VISSIM Parameter Calibration Based on Traffic Characteristics Distribution at Signalized Intersections

VISSIM Parameter Calibration Based on Traffic Characteristic...

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data driven control and learning systems (ddcls)

作者： Ning Li Yujie Sun State Nuclear Electric Power Planning Design & Research Institute Co. Ltd Beijing

ISBN: (纸本)9781538626191

In order to increase the accuracy of traffic simulation and better reproduce the real traffic condition at signalized intersections, this paper proposed a parameter calibration method based on the traffic distribution rules at signalized intersections. First, after qualitatively analyzing the traffic condition at signalized intersections based on dynamic traffic features, this paper selected the key parameters that need to be calibrated. then, regarding the selected key parameters, this paper first designed and implemented the collecting method. then filtered and analyzed the data, and acquired the distribution pattern of each key parameter at signalized intersection. Finally, in order to validate the calibration process based on vehicle types through simulation, this paper chose travel time and number of stops as validation parameters. the results showed that there had been a great increase in the accuracy after calibration. the maximum inaccuracy among all evaluation parameters was 14.6%, which indicated that the calibration process based on traffic characteristics distribution at signalized intersections was effective.

关键词： Calibration Roads Acceleration data models Vehicle dynamics Global Positioning System Traffic control

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