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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291-300 订阅

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data-driven Adaptive Optimal Tracking control for Completely Unknown systems

Data-Driven Adaptive Optimal Tracking Control for Completely...

引用

data driven control and learning systems (ddcls)

作者： Dawei Hou Jing Na Guanbin Gao Guang Li Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming School of Engineering and Materials Science Queen Mary University of London London UK

ISBN: (纸本)9781538626191

In this paper, an online data-driven based solution is developed for linear quadratic tracking (LQT) problem of linear systems with completely unknown dynamics. By applying the vectorization operator and Kronecker product, an adaptive identifier is first built to identify the unknown system dynamics, where a new adaptive law with guaranteed convergence is proposed. By using system augmentation method and introducing a discounted factor in the cost function, a compact form of LQT formulation is proposed, where the feedforward and feedback control actions can be obtained simultaneously. Finally, a new policy iteration is introduced to solve the derived augmented algebraic Riccati equation (ARE). Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

关键词： Cost function Optimal control Adaptive systems System dynamics Trajectory Convergence Feedforward systems

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Adaptive Neural Network control For Vehicle Active Suspension System with Unknown Dead-Zones

Adaptive Neural Network Control For Vehicle Active Suspensio...

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data driven control and learning systems (ddcls)

作者： Yan-Qi Zhang Lei Liu Yan-Jun Liu College of Science Liaoning University of Technology Jinzhou

ISBN: (纸本)9781538626191

this paper presents the development of an adaptive neural network (NN) control method for non-linear quarter-vehicle model which has the characteristics of road disturbance, parameter uncertainties and unknown dead-zone. Considering the dead-zone slopes as a model uncertainty, an adaptive NN control scheme is developed depending on back stepping technique. In this paper, uncertain non-linear functions in suspension systems are estimated by NNs. then again, the minimal learning parameters can ensure that the computation and the complexity of system are exceedingly reduced. the stability and the signals boundedness of vehicle suspension system are proved. Finally, a given simulation example shows the feasibility of the designed approach.

关键词： Suspensions (mechanical systems) Adaptive systems Adaptation models Mathematical model Artificial neural networks Roads

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data and Information Fusion Oriented Outline driven Framework for Clause Extraction from Electronic Insurance Contracts 23

Data and Information Fusion Oriented Outline Driven Framewor...

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： Duan, Zhifeng Xu, Bin Wu, Zhengwu Duan, Yucong Ren, Chengxiang China Judicial Big Data Institute Department of Politics and Law Beijing China Hainan University School of Computer Science and Technology Haikou China

ISBN: (纸本)9781665494571

In the justice trail of insurance contract disputes, a judge is required to quickly understand the main points involved in an insurance contract to find related pieces of evidence. the content of an insurance contract is usually standardized and organized to some degree. Researchers and engineers can use techniques including deep learning and rule engines to analyze insurance contracts. Typical tasks include contract outline identification and contract clause extraction. However, judicial applications are facing the challenge of robustness, interpretability, process transparency and continuous improvement as well as accuracy and recall rate. this paper proposed an outline driven framework to extract hierarchical clauses from multimodal electronic contracts by com-bining rules summarized by experts and deep learning methods to continuously discover and improve the rule base. In our approach, conflicts of outlines can be solved globally by combining basic outline rules, outline sequence sorting, an entropy based outline sequence filtering and a global fault tolerance strat-egy. A computer vision method is used to extract complete contract clauses by their spatial position relations. Results from judicial datasets show that the proposed method can quickly and effectively achieve high performance in recall and accuracy compared with purely deep learning algorithms and can be built up in a short period, which shows the practical value in rapid and long-term iterative development. © 2021 ieee.

关键词： Deep learning Filtering Fault tolerant systems Insurance Robustness Iterative algorithms data mining

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Iterative learning based Fault Estimation for Stochastic Nonlinear systems

Iterative Learning based Fault Estimation for Stochastic Non...

引用

data driven control and learning systems (ddcls)

作者： Jiantao Shi Yuhao Yang Jun Sun Ning Wang Nanjing Research Institute of Electronic Technology Nanjing P. R. China

ISBN: (纸本)9781538626191

In this paper, the fault estimation issue is investigated for a type of nonlinear stochastic repetitive systems using the iterative learning (IL) approach. Different from the existing works, a type of systems with initial state errors, stochastic disturbance and measurement noise is considered. In order to estimate the fault, a novel nonlinear iterative learning observer (NILO) is designed by using previous input signals and output estimation errors. A necessary and sufficient condition is obtained to guarantee the uniform ultimate boundedness of fault estimation errors in terms of λ-norm with the given IL strategy. Finally, the given approach is verified by a simulation example.

关键词： Iron Estimation error Measurement uncertainty Noise measurement Observers Stochastic systems

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Accuracy Analysis of Polynomial Model and Auto Regressive Model for data-driven Fault Detection

Accuracy Analysis of Polynomial Model and Auto Regressive Mo...

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data driven control and learning systems (ddcls)

作者： Bowen Sun Zhangming He Shuqing Xu Haiyin Zhou Jiongqi Wang College of Liberal Arts and Sciences National University of Defense Technology Changsha P.R. China Beijing Institute of Control Engineering China Academy of Space Technology Beijing

ISBN: (纸本)9781538626191

the key of data-driven fault detection method lies in the full and effective understanding of the detected data, and the fitting for the detected data is an effective means to realize the parameterization of the data model. In this paper, the polynomial model and the autoregressive model are used to estimate and predict the non-stationary data and the stationary data respectively, so as to achieve the data-driven fault detection. the estimation accuracy of the parameter model is analyzed. the relationship between the prediction accuracy and the prediction duration, the polynomial fitting window, the fitting order are given theoretically. Finally, numerical simulation results are given, which can provide some support for data-driven fault detection to some extent.

关键词： data models Reactive power Time series analysis Analytical models Fault detection Predictive models Fault diagnosis

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Optimal Finite-Time Tracking control for a Class of Unknown Nonlinear System Based on Input-Output data

Optimal Finite-Time Tracking Control for a Class of Unknown ...

引用

data driven control and learning systems (ddcls)

作者： Ruizhuo Song Yulong Xie School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781538626191

this paper appears an optimal tracking control method for completely unknown discrete-time nonlinear affine system. We make iterative adaptive dynamic programming (ADP) to approximately solve the Hamilton-Jacobi-Bellman equation by minimizing the finite-time performance index function. Based on input to output data, using model neural network as an identifier to construct the system input to output mapping, which is utilized to build the augmentation system. then the action and critic neural network are utilized to approximate the virtual control and the corresponding performance index function, respectively. It proves that the estimation errors of the neural network are uniformly ultimately bounded. At last, an example is used to demonstrate the theoretical results and the performance of the proposed approach.

关键词： Dynamic programming Performance analysis Biological neural networks data models Optimal control Nonlinear systems

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MIMO Model Free Adaptive control of Two Degree of Freedom Manipulator

MIMO Model Free Adaptive Control of Two Degree of Freedom Ma...

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data driven control and learning systems (ddcls)

作者： Ziqiang Zen Rongmin Cao Zhongsheng Hou College of Automatione Beijing Information Science and Technology University Beijing The Advanced Control Systems Laboratory Beijing Jiaotong University Beijing China

ISBN: (纸本)9781538626191

Aimed at plane nonlinear two-degree-of-freedom (2-dof) manipulator, which is a nonlinear multi-input and multi-output(MIMO) system, its joint angles are controlled by model-free adaptive control (MFAC) theory to realize trajectory tracking. the nonlinear system model is replaced by the compact form dynamic linearization time-varying model, and the pseudo-Jacobian matrix of the system is estimated on the basis of the input and output data of the manipulator model. the simulation results show that the compact form dynamic linearized model-free adaptive control (CFDL-MFAC) algorithm can effectively ensure the tracking performance of the system output, and the error remains within a certain range.

关键词： Manipulator dynamics Mathematical model Kinematics MIMO communication Adaptation models data models

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Adaptive Natural Policy Gradient in Reinforcement learning

Adaptive Natural Policy Gradient in Reinforcement Learning

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ieee data driven control and learning systems conference

作者： Dazi Li Zengyuan Qiao Tianheng Song Qibing Jin Institute of Automation Beijing University of Chemical Technology

ISBN: (纸本)9781538626191

In recent years, the policy gradient method in intensive learning has attracted wide attention with its good convergence performance. At the same time, regulation of hyper parameters is also a matter of concern. Based on the advantages of Actor-Critic structure (AC), the Natural-Gradient Actor-Critic algorithm (NAC) in the discount model is studied in this article. then the Natural-Gradient Actor-Critic with ADADELTA (A-NAC) algorithm is proposed. the use of ADADELTA is adapted to adjust the learning rate in the actor network, and further improves the convergence speed of the NAC algorithm. Simulation results show that NAC/A-NAC have better learning efficiency and faster convergence rate than regular gradient AC methods.

关键词： Actor-Critic Natural gradient Adadelta Adaptive Value function approximation Reinforcement learning

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Improved Model Free Adaptive control for Winding System

Improved Model Free Adaptive Control for Winding System

引用

data driven control and learning systems (ddcls)

作者： Hongyun Xiong Ye Liao Xiaoyan Chu Information Science and Engineering Central South University Changsha Hunan China

ISBN: (纸本)9781538626191

the winding system is a strong coupling system with parameter variability, uncertainty, structural complexity and external interference. Aiming at the problem that the saturation of the actuator of winding system affects the control effect, a modified partial form dynamic linearization(PFDL) model-free adaptive control(MFAC) method considering the actuator saturation is proposed. the MFAC method based on adaptive observer is used to reduce the complexity of pseudo-Jacobian parameter matrix. At the same time, considering the dynamic constraint of the control quantity, an anti-saturation compensator is designed to dispose the control variable input rate and the limitation of the magnitude. the results show that the improved control algorithm can dispose the actuator' saturation problem, and can realize the fast tracking and stability control of tension and speed.

关键词： Windings Mathematical model Heuristic algorithms Traction motors control systems Observers Adaptation models

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High-Precision Tracking of Piezoelectric Actuator Using Dual-Loop Iterative learning control

High-Precision Tracking of Piezoelectric Actuator Using Dual...

引用

data driven control and learning systems (ddcls)

作者： Yupei Jian Xin Kang Wanqiu Yang Da Min Deqing Huang School of Electrical Engineering Southwest Jiaotong University Chengdu P. R. China

ISBN: (纸本)9781538626191

Suffering from rate-dependent hysteretic nonlinearity, accurate positioning of nanopositioning stage driven by piezoelectric actuators(PEAs) is hard to achieve and effective controllers are urgently needed. In this paper, by assuming that the model of PEA takes a Hammerstein structure, a dual-loop iterative learning control (ILC) scheme is designed to deal with both input hysteresis and linear dynamics of system synchronously. the merit of this controller lies in that, accurate system models are not prerequisite so that the control process is greatly simplified. As a comparison, an extra experiment using single loop ILC is performed to manifest the efficacy of the proposed algorithm. Simulation results show that the dual-loop ILC scheme is superior to the single loop ILC scheme in terms of convergence speed and control accuracy.

关键词： Hysteresis Uncertainty Iterative learning control Piezoelectric actuators Linear systems Trajectory

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