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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11-20 订阅

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Reinforcement learning control for Consensus of the Leader-Follower Multi-Agent systems 7

Reinforcement Learning Control for Consensus of the Leader-F...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Chiang, Ming-Li Liu, An-Sheng Fu, Li-Chen Natl Taiwan Univ Dept Elect Engn Taipei Taiwan NTU Ctr Artificial Intelligence & Adv Robot Taipei Taiwan

ISBN: (纸本)9781538626184

this paper considers the optimal consensus of multi-agent systems using reinforcement learning control. the system is nonlinear and the number of agents can be large. the control objective is to design the controllers for each agent such that all the agents will be consensus to the leader agent. We use the Actor-Critic Network and the Deterministic Policy Gradient method to realize the controller. the policy iteration algorithm is discussed and many simulations are provided to validate the result.

关键词： Optimal control Reinforcement learning Policy iteration Multi-Agent System

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DMPC Applied to the Temperature Regulation System of Building under Packet Dropout Communication 7

DMPC Applied to the Temperature Regulation System of Buildin...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Huang, Qingnan Xie, Liujun Guangxi Univ Sci & Technol Coll Elect & Informat Engn Liuzhou 545006 Peoples R China

ISBN: (纸本)9781538626184

A solution to the interference of control signals and signal loss in the process of signal transmission in the process of decentralized model predictive control (DMPC) is introduced in this paper. Judging whether the data packet is lost at each time sampling signal, then the approximate value of the lost signal at this time is calculated in an alternative way. the results show that: this method is feasible.

关键词： Decentralized model predictive control packet dropout communication Modeling

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Repetitive learning control for a Class of Nonlinear systems 7

Repetitive Learning Control for a Class of Nonlinear Systems

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Sun, Mingxuan Li, He Chen, Qiang Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China

ISBN: (纸本)9781538626184

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： Repetitive learning control convergence time-varying uncertainties nonlinear systems

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Iterative learning Consensus for Discrete-time Multi-Agent systems with Measurement Saturation and Random Noises 7

Iterative Learning Consensus for Discrete-time Multi-Agent S...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Liu, Chen Shen, Dong Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9781538626184

this paper investigates the consensus tracking problem for a class of multi-agent systems with measurement saturation and random noises. A distributed iterative learning control algorithm is proposed by utilizing the input signals and the measured output information from previous iterations. the considered multi-agent systems have a fixed topology of the communication graph and the desired trajectory is only accessible to a subset of agents. With the help of a decreasing gain sequence, it is proved that the input sequence will converge to the desired one in an almost sure sense as the iteration number goes to infinity. Simulation results are given to verify the effectiveness of the proposed algorithm.

关键词： Multi-agent systems iterative learning control output saturation random noises

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Iterative learning control for Continuous-Time systems with Locally Lipschitz Nonlinearity and Input Saturation 7

Iterative Learning Control for Continuous-Time Systems with ...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Zhang, Jingyao Meng, Deyuan Beihang Univ Res Div 7 Beijing 100191 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781538626184

In this paper, a data-based iterative learning control (ILC) is developed to address the output tracking problem of continuous-time locally Lipschitz nonlinear systems subject to input saturation. Under a data-based ILC update law with saturation, an extended data driven framework is established for the ILC convergence in the presence of locally Lipschitz nonlinearity and input saturation. A relative degree condition and the input-to-state stability are given to ensure the boundedness of the state and the convergence of the output tracking error simultaneously. the simulation demonstrates the effectiveness of the results.

关键词： Iterative learning control Locally Lipschitz nonlinearity data-based

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Sampled-data control for T-S Fuzzy systems with Quantized Signals 7

Sampled-data Control for T-S Fuzzy Systems with Quantized Si...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Han, Xiaojing Cheng, Ningwei Ma, Yuechao Yanshan Univ Sch Sci Qinhuangdao 066004 Peoples R China

ISBN: (纸本)9781538626184

this paper deals with the problem of sampled-data control for T-S fuzzy systems with quantized signals. Based on the constructed Lyapunov-Krasovskii functional(LKF), Jensen's inequality and Free weight matrix, some sufficient conditions are obtained in the form of linear matrix inequalities(LMIs). By combining the input delay approach and dynamic quantizer, the sampled-data controller is designed to guarantee that T-S fuzzy systems with quantized signals is asymptotically stable. Finally, a numerical example is presented to verify the feasibility and effectiveness of the proposed methods.

关键词： T-S fuzzy systems Sampled-data controller Quantized signals LMIs

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Iterative learning control for Discrete Singular systems with Randomly Varying Trial Lengths 7

Iterative Learning Control for Discrete Singular Systems wit...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Liu, Jiahuan Tian, Huiping Tian, Senping Li, Xiangyang South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China Guangdong Womens Polytech Coll Guangzhou 511450 Guangdong Peoples R China

ISBN: (纸本)9781538626184

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 discrete singular systems varying trial lengths

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Adaptive Fuzzy Consensus control for Unknown State-Delay Nonlinear Multiagent System 7

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

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Yan, Fei Guo, Baolong Xidian Univ Xian 710100 Shaanxi Peoples R China

ISBN: (纸本)9781538626184

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： Adaptive fuzzy control Consensus control Nonlinear multiagent systems State-delay

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Unsupervised Fault Detection Based on Laplacian Score and TEDA 7

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

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Lou, Chuyue Li, Xiangshun Wuhan Univ Technol Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781538626184

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： Laplacian Score TEDA Unsupervised learning Fault detection

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Iterative learning based Fault Estimation for Stochastic Nonlinear systems 7

Iterative Learning based Fault Estimation for Stochastic Non...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Shi, Jiantao Yang, Yuhao Sun, Jun Wang, Ning Nanjing Res Inst Elect Technol Nanjing 210039 Jiangsu Peoples R China CETC Key Lab Intellisense Technol Nanjing 210039 Jiangsu Peoples R China

ISBN: (纸本)9781538626184

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 lambda-norm with the given IL strategy. Finally, the given approach is verified by a simulation example.

关键词： Iterative learning Fault estimation Nonlinear stochastic systems Initial state errors

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