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检索条件"任意字段=10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021"

共 359 条记录，以下是221-230 订阅

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A Fault Diagnosis Method Based on Wavelet Denoising and 2DCNN under Background Noise 12

A Fault Diagnosis Method Based on Wavelet Denoising and 2DCN...

引用

ieee 12th data driven control and learning systems conference (ddcls)

作者： Liu, Kexin Li, Zhe He, Wenbin Peng, Jia Wang, Xudong Wang, Yaonan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China State Grid Hunan Elect Power Co Ltd Changsha 410029 Peoples R China Hunan Univ Sch Robot Changsha 410082 Peoples R China Natl Engn Res Ctr Robot Visual Sensing Control Te Changsha 410082 Peoples R China

ISBN: (纸本)9798350321050

this paper develops a novel method named wavelet denoising convolutional neural network (WDECNN) for fault diagnosis with background noise. the continuous wavelet transform (CWT) is first applied to transform the measured raw vibration data into time-frequency images which serve as the inputs of WDECNN. then, a light-weight two-dimensional CNN (2DCNN) model is incorporated in WDECNN to simplify the network architecture, while a wavelet denoising module is also applied in it to achieve high accuracy of fault identification in the noisy environment. Particularly, the wavelet denoising module which consists of wavelet decomposition and denoising is parallel to the 2DCNN model, and the denoising results are integrated into pooling layers in the 2DCNN model. thus, the denoised information is added to the 2DCNN model to improve its feature learning ability. Finally, the effectiveness of the developed method is validated on Paderborn bearing dataset, which illustrates its fault diagnosis capability under background noise.

关键词： Fault Diagnosis Background Noise Wavelet Denoising Convolutional Neural Network (CNN) data-driven

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Wind Speed Prediction Based on ARMA and SVR 12

Wind Speed Prediction Based on ARMA and SVR

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ieee 12th data driven control and learning systems conference (ddcls)

作者： Jiao, Xuguo Zhang, Daoyuan Yang, Qinmin Zhang, Zhenyong Liu, Wenfeng Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266520 Peoples R China Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Guizhou Univ Coll Comp Sci & Technol State Key Lab Publ Big Data Guiyang 550025 Peoples R China Qingdao Univ Technol Sch Civil Engn Qingdao 266520 Peoples R China

ISBN: (纸本)9798350321050

Wind power generation technology is one of the research hotspots of renewable energy nowadays. In order to ensure the stable and reliable operation of wind power generation equipment, wind speed prediction is very important. this paper provides a new idea for the wind speed prediction based on Autoregressive Moving Average (ARMA) and Support Vector Regression (SVR). First, to reasonably divide the original data into multiple historical data with strong correlation as features to predict the future wind speed, the ARMA model is employed and its partial autocorrelation coefficient is calculated. By this means, the input features can be optimally selected and the training set of the prediction model can be constructed. Further, SVR model is used to build the nonlinear relationship between the input features and future wind speed. Finally, through simulation, it proves that this method saves more time than try and error method in selecting input features, and through comparison with Backpropagation Neural Network (BPNN), it proves that this method can achieves higher wind speed prediction accuracy.

关键词： Wind Speed Prediction Time Series Autoregressive Moving Average (ARMA) Support Vector Regression (SVR)

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data-driven Fault-Tolerant Distributed Formation control of Multiagent systems with Switching Topologies 13

Data-Driven Fault-Tolerant Distributed Formation Control of ...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhao, Tianxu Zhang, Xian Wang, Xin Xie, Wen-Bo Heilongjiang University Harbin150080 China

ISBN: (纸本)9798350361674

In this paper, we study the problem of data-driven fault-tolerant formation control for linear multiagent systems with actuator faults in a directed communication switching topology. Initially, constructing a distributed observer to generate using the local information as a reference, the convergence of the observer under the switching topology is proved. Subsequently, we leverage reinforcement learning theory to determine the optimal control strategy, even when the parameters of the subsystems within the multi-agent network are not known. In the final step, we apply the acquired control strategy to devise a data-driven fault-tolerant controller, aimed at minimizing fault impacts as they arise. the efficiency of our proposed solution is confirmed through simulation experiments. © 2024 ieee.

关键词： Reinforcement learning

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data-driven Q-learning control for Nonlinear systems Involving Parallel Multi-Step Deduction 13

Data-Driven Q-Learning Control for Nonlinear Systems Involvi...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wang, Jiangyu Wang, Ding Zhao, Mingming Qiao, Junfei Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China

ISBN: (纸本)9798350361674

When facing large amounts of data, it is a challenging task to optimize policies by using all data at once. In this paper, a data-driven Q-learning scheme with parallel multi-step deduction is developed to improve learning efficiency using small batch data for discrete-time nonlinear control. Specifically, a data-driven model is established by making use of all data in advance. then, the proposed algorithm can parallel deduce the small batch data to effectively accelerate the learning process. Furthermore, we can adjust the step size of multi-step deduction to balance the utilization between data and model. the near-optimal policy can be obtained ultimately by using hybrid data from the real system and data-driven model. Finally, a torsional pendulum plant is given to demonstrate the effectiveness of the proposed method. © 2024 ieee.

关键词： Contrastive learning

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Neural Networks Iterative learning Impedance control of Lower Limb Exoskeleton for the Later Stage of Rehabilitation 11

Neural Networks Iterative Learning Impedance Control of Lowe...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Xia, Peng Yang, Yong Jin, Shuping Liu, Xia Dong, Xiucheng School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In the later stage of lower limb rehabilitation training, impedance control is used to realize human-machine force interaction. An expected impedance model-based iterative learning controller is proposed in this paper. Iterative learning control is used to realize the target impedance model, which not only meets the repeatability of rehabilitation training, but also ensures the transient performance of lower limb rehabilitation exoskeleton. In order to solve the model uncertainty, neural networks (NN) is used to estimate the unknown dynamics of the exoskeleton. Simulation results further verify the effectiveness of the proposed control scheme. © 2022 ieee.

关键词： Uncertainty analysis

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Stability of Linear Hyperbolic Distributed Parameter systems Based on Event-Triggered control 11

Stability of Linear Hyperbolic Distributed Parameter Systems...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Ni, Min Dai, Xisheng Zhang, Jiaming Wu, Qiqi Zuo, Huang School of Electrical Electronics and Computer Science Guangxi University of Science and Technology Liuzhou545006 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper investigates the stability for a class of linear hyperbolic distributed parameter systems based on event-triggered control. Lyapunov technique is used to obtain the stable feedback gain and boundary conditions of the system. Sufficient conditions have been established for the exponential stability of the system under linear boundary condition and dissipative boundary condition. In addition, the existence of positive upper bound between two triggering times is proved. A numerical example illustrates the proposed methods. © 2022 ieee.

关键词： Boundary conditions

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Event-Triggered Sliding-Mode Cruise control for Multibody High-Speed Train 11

Event-Triggered Sliding-Mode Cruise Control for Multibody Hi...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Yu, Wei Huang, Deqing Cai, Liangcheng Wu, Yue The School of Electrical Engineering Southwest Jiaotong University Chengdu610031 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the sliding-mode cruise control for multibody high-speed train (HST) is studied with an event-triggered communication scheme in the paper. Firstly, the error equation model of the HST with multibody is established with the consideration of time-varying running resistance. then, the sliding-mode controller (SMC) is designed, and the event-triggered mechanism is constructed to reduce the transmission frequency. Next, by employing the Lyapunov stability theory, the convergence of the error equation of HST is analyzed and the proposed methods are verified by a Japan Shinkansen example. © 2022 ieee.

关键词： Sliding mode control

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Finite-Iteration learning for Multi-Agent systems Using Terminal ILC 13

Finite-Iteration Learning for Multi-Agent Systems Using Term...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Chen, Xiaoxue Zhang, Ruikun Lin, Xue School of Mathematics and Physics Qingdao University of Science and Technology Qingdao266061 China

ISBN: (纸本)9798350361674

the article studied a finite-iteration control problem. Based on the multi-agent systems (MASs), where each agent has a linear structure, the article proposed the controller for finite-iteration tracking problem. Combine with the definition of finite-iteration tracking, the main task is to design a control strategy to realize the finite-iteration according to the predetermined error accuracy requirements and other parameter information. To solve the problem, a finite-iteration control strategy is proposed by using terminal iterative learning control (TILC), which contains information of terminal output and control input of the agents. By using the control algorithm, we can realize the terminal output of agents to be consistent after a finite number of iteration. In the end, this article give a numerical simulation example to illustrate the correctness and effectiveness of the proposed finite-iteration control strategy. © 2024 ieee.

关键词： Multi agent systems

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Position Constraints Adaptive Iterative learning control of Exoskeleton for the Preliminary Stage of Rehabilitation 11

Position Constraints Adaptive Iterative Learning Control of ...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Jin, Shuping Yang, Yong Dong, Xiucheng Liu, Xia Xia, Peng School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, the trajectory tracking control problem of an exoskeleton using in the preliminary stage of rehabilitation is addressed via adaptive iterative learning control. the disturbances are handled by radial basis function neural networks. A Lyapunov-like barrier composite energy function is utilized to ensure that position constraints are satisfied. With the help of the proposed control method, the exoskeleton leg can achieve good tracking performance without violating output constraints. Convergence of system tracking errors is demonstrated by mathematical method and performance of the proposed controller is illustrated by numerical simulations. © 2022 ieee.

关键词： Iterative methods

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Dynamic Obstacle Avoidance Algorithm for Robot Arm Based on Deep Reinforcement learning 11

Dynamic Obstacle Avoidance Algorithm for Robot Arm Based on ...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Cheng, Xiaowei Liu, Shan College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

A dynamic obstacle avoidance planning algorithm based on deep reinforcement learning is proposed for rigid manipulators. After the neural network interacts with the environment and learns, it can give real-time action strategies to guide the manipulator to avoid dynamic obstacles. this paper proposes a new state space description method suitable for manipulators and dynamic environments, and designs the corresponding collision detection method and reward value calculation function for this state description method. the test results in the simulation environment demonstrate the effectiveness of the method. © 2022 ieee.

关键词： Reinforcement learning

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