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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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Substation helmet detection based on improved YOLOX-S algorithm 12

Substation helmet detection based on improved YOLOX-S algori...

引用

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

作者： Tong, Xiaodong Li, Zhaofei Sichuan Univ Sci & Engn Sch Automat & Informat Engn Zigong 643000 Peoples R China Sichuan Univ Sci & Engn Artificial Intelligence Key Lab Sichuan Prov Zigong 643000 Peoples R China

ISBN: (纸本)9798350321050

the improved YOLOX-S algorithm is proposed for the detection of small helmet targets based on an improved YOLOX-S algorithm for the detection of helmets worn by relevant personnel in hazardous scenarios in substations. First, the ECA attention mechanism is introduced into the CSPLayer structure in YOLOX-S to direct the model to pay more attention to channel features of small target information and enhance the model's ability to utilize useful features. Secondly, the addition of the ConvNext Block module after the three feature layers of the backbone feature extraction network to enhance the model's ability to exploit useful features. Finally, the weighted feature fusion mechanism of BiFPN is introduced in the enhanced feature extraction network by changing the original concat to BiFPN_concat, adding learnable weights to each input feature to learn the importance of different input features, distinguishing the importance of different features in the feature fusion process, and better focusing on the target information to be detected. the experimental results show that the mAP of the improved algorithm is 92.65%, which is an average accuracy improvement of 2.55% over the original YOLOX-S algorithm and meets the practical requirements.

关键词： Deep learning YOLOX-S Helmet Detection Substation

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

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

引用

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

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

引用

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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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 Sliding-Mode Decoupling control with Time-Varying Sliding Surface for Nonlinear Discrete-Time Processes 7

Data-Driven Sliding-Mode Decoupling Control with Time-Varyin...

引用

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

作者： Weng, Yongpeng Wang, Ning Li, Shaowu Liu, Xinming Dalian Maritime Univ Coll Marine Elect Engn Dalian 116026 Peoples R China Hubei Univ Nationalities Coll Informat Engn Enshi 445000 Peoples R China Liaoning Tech Univ Coll Elect & Control Engn Huludao 125105 Peoples R China

ISBN: (纸本)9781538626184

In this paper, a novel data-driven time-varying sliding surface (TVSS) for second-order sliding-mode decoupling control (SSDC) law is proposed to improve tracking control performance of nonlinear discrete-time processes. First, using the extended state observer (ESO) and the non-parametric dynamic linearization technique (NDLT), a decoupled data-driven sliding surface (DSS) is firstly proposed to facilitate the SSDC law design. then, inspired by the fuzzy logic control (FLC) approach, a TVSS is proposed to further enhance the SSDC law's performance, where the sliding surface slope is updated by the proposed rule base of FLC. In light of the developed sliding surface, a less overshoot and faster dynamic response are obtained without deteriorate the original discontinuous control term. In addition, the chattering phenomenon is also alleviated under this control scheme. Finally, a numerical example is given to evaluate the proposed approach.

关键词： Time-varying sliding surface Chattering Decoupling Fuzzy logic control Second-order-sliding mode control

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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...

引用

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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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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