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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是101-110 订阅

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Fixed-Time Inter-Axis Synchronization control of Manipulators based on Active Disturbance Rejection Method 13

Fixed-Time Inter-Axis Synchronization Control of Manipulator...

引用

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

作者： Zhao, Hanpu Liu, Yang Seventh Research Division Beijing100191 China

ISBN: (纸本)9798350361674

this paper investigates the fixed-time inter-axis synchronization tracking control for n-degree of freedom (DOF) uncertain manipulators. First, a dynamic model for the manipulator is presented, and a fixed-time disturbance observer is designed to compensate for system uncertainties and external disturbances. then, a non-singular fixed-time active disturbance rejection inter-axis synchronization controller is designed, which not only avoids the singularity problem in traditional terminal sliding mode control, but also realizes the active suppression of disturbances. It is proved that the tracking error of each joint of the uncertain manipulator converges synchronously to zero within a fixed time. Finally, simulations are conducted with a 2-DOF manipulator, and results demonstrate that the method can achieve fixed-time inter-axis synchronization control and possesses robustness against the lumped disturbances. © 2024 ieee.

关键词： Disturbance rejection

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Iterative learning for Nonlinear systems with Variable Pass Lengths: Fault Estimation in the Presence of Actuator Faults 13

Iterative Learning for Nonlinear Systems with Variable Pass ...

引用

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

作者： Gu, Shaodong Chen, Chuang Shi, Jiantao Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China

ISBN: (纸本)9798350361674

this research addresses the faults estimation (FE) of the actuator faults in nonlinear dynamic systems with varying pass lengths. Additionally, the adverse impact of the bounded disturbances and measurement noise on the systems is also considered. A nonlinear iterative learning observer (NILO) is designed to achieve fault estimation. To tackle information loss and redundancy caused by variable pass lengths, a novel enhanced weighted averaging operator for the NILO is proposed. this approach enables fast tracking of fault signals and enhances system robustness. the method ensures the final boundedness of tracking error. Finally, the simulation results are given to verify that the proposed approach is validated by numerical simulation studies. © 2024 ieee.

关键词： Nonlinear analysis

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ANCP: Attention-Based Neural Circuit Policies for Robot Spatiotemporal Reasoning in Crowd Navigation 13

ANCP: Attention-Based Neural Circuit Policies for Robot Spat...

引用

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

作者： Ao, Tianyong Tian, Yongmeng Fu, Le Zhou, Yi School of Artificial Intelligence Henan University Henan Zhengzhou450046 China Henan University International Joint Research Laboratory for Cooperative Vehicular Networks of Henan Henan Zhengzhou450000 China

ISBN: (纸本)9798350361674

this paper explores the complexities of autonomous navigation for mobile robots in densely populated environments, where the accuracy of crowd behavior modeling is crucial. Recent studies have increasingly leveraged deep reinforcement learning and biologically inspired methodologies, which demonstrate enhanced learning capabilities and adaptability in intricate settings. this work proposes a novel attention-based neural circuit policies (ANCP) that merges biological neural mechanisms with attention processes to facilitate spatiotemporal reasoning in robot navigation. We evaluated the algorithm in demanding simulation settings, and the findings indicate that our approach adapts more effectively to crowded scenarios compared with the baseline algorithms. © 2024 ieee.

关键词： Adversarial machine learning

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NN-Based Dynamic Event-Triggered Consensus Tracking for Nonlinear Multi-Agent systems via a Single Parameter learning Method 13

NN-Based Dynamic Event-Triggered Consensus Tracking for Nonl...

引用

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

作者： Xiao, Junwen Liu, Yongchao Institute of Complexity Science School of Automation Qingdao University Qingdao266071 China Shandong Key Laboratory of Industrial Control Technology Qingdao266071 China

ISBN: (纸本)9798350361674

this paper investigates adaptive dynamic event-triggered neural network (NN) consensus tracking for nonlinear multi-agent systems (MASs). A distributed adaptive controller is designed for MASs with unknown nonlinear functions and external disturbances. A single parameter learning method is proposed to simplify the controller design. Moreover, the dynamic event-triggered mechanism is designed to conserve communication resource. the designed control law ensures that all signals of the MASs are uniformly bounded, and the tracking errors of all agents converge to a compact set while avoiding Zeno behavior. An expository simulation example reveals the virtue of the presented method. © 2024 ieee.

关键词： Multi agent systems

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Secure Transmission of State Information for Distributed Model Predictive control in Vehicular Platoons 13

Secure Transmission of State Information for Distributed Mod...

引用

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

作者： Li, Xin Huang, Darong Na, Yuhong Institute of Information Science and Engineering Chongqing Jiaotong University Chongqing400074 China School of Artificial Intelligence Anhui University Hefei230001 China

ISBN: (纸本)9798350361674

In vehicular Ad Hoc networks (VANETs), vehicle platoons share state information through vehicle-to-vehicle (V2V) communication. However, in an open network environment, cyber-attacks may be suffered, threatening the normal traveling and security of the vehicle queuing system. this paper explores encrypted transmission for distributed model predictive control (MPC) in vehicle platoons. Digital envelope technology(DET), AES symmetric encryption and RSA asymmetric encryption are used to ensure secure transmission of information. Authentication and message digest generation through hash algorithm to ensure message integrity. Safely transmitted state information is applied to MPC to maintain vehicle platoon speed consistency and spacing expectations. Finally, the method's feasibility and effectiveness are confirmed through numerical simulation. © 2024 ieee.

关键词： Vehicular ad hoc networks

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Coordinated MFAILC of Multiple High-Speed Trains Subject to False data Injection Attacks 13

Coordinated MFAILC of Multiple High-Speed Trains Subject to ...

引用

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

作者： Wei, Y. Junjie, M. Huang, Deqing School of Electrical Engineering Southwest Jiaotong University Key Laboratory of Railway Industry of Advanced Energy Traction and Comprehensive Energy Conservation Chengdu610031 China Tianfu Yongxing Laboratory Chengdu610231 China Shaanxi Railway and Logistics Industry Group Co. Ltd. Shaanxi 714025 China

ISBN: (纸本)9798350361674

this study presents a coordinated model free adaptive iterative learning controller (MFAILC) for multiple high-speed trains (MHSTs) subjected to random false data injection attacks (FDIA). Firstly, the nonlinear train system is converted as a linear one that solely relies on input and output (I/O) information of the trains. Subsequently, by establishing an attack model using random variables, the coordinated MFAILC is developed. Furthermore, the convergence analysis is conducted based on mathematical expectation, followed by numerical simulation to verify the validity of the control scheme. © 2024 ieee.

关键词： Railroads

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data-driven Multi-Objective Optimization of Power Consumption and Quality for Finishing Rolling Process 13

Data-Driven Multi-Objective Optimization of Power Consumptio...

引用

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

作者： Chen, Xiaolu Yang, Ying College of Engineering Peking University State Key Laboratory for Turbulence & Complex Systems Department of Mechanics and Engineering Science Beijing100871 China

ISBN: (纸本)9798350361674

this paper investigates the operational optimization in the context of the steel hot rolling production and finishing facilities. Optimizing the operational variables of the finishing rolling process can improve the rolling quality and reduce the unit power consumption. However, there are too many operational variables in the process and their interactions are strongly coupled, making it difficult to develop an effective model for in-depth study. therefore, this paper adopts a data-driven approach, using neural network algorithms to construct the data prediction models between performance and set values respectively. Multi-objective optimization of the set values is performed by combining the actual mechanism and equipment constraints. In the obtained Pareto optimal solutions, the conflicting choices are further balanced among multiple objectives to obtain the most suitable operating parameters. Experimentally verified using production line production data, the method proposed in this paper significantly improves the performance indexes. © 2024 ieee.

关键词： Multiobjective optimization

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Dense-Descriptor Transporter Networks for Multidimensional Deformable Object Manipulation 13

Dense-Descriptor Transporter Networks for Multidimensional D...

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

作者： Cao, Yang Gong, Daoxiong Yu, Jianjun Beijing University of Technology Faculty of Information Technology Beijing100124 China

ISBN: (纸本)9798350361674

With the continuous development of robotics, there are emerging new trends in robotic manipulation of deformable objects, calling for improved adaptability and flexibility to better cope with complex environments and task requirements. Due to the high-dimensional state space and complex dynamics of deformable objects during deformation, it is challenging for neural networks to directly extract object features such as shape, texture and color from images. In this paper, we propose an imitation learning approach that utilizes dense descriptor modules to improve its perception of different deformable objects and deformations. the learned descriptors can extract the features of deformable objects from RGB-D images and map them to the descriptor space, making the representation of objects more compact and robust. Furthermore, our proposed approach demonstrates better competitiveness in simulation environments featuring fabrics, ropes, or bags when compared to the baseline methods. © 2024 ieee.

关键词： Contrastive learning

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Fault Estimation for Nonlinear systems with Disturbance Based on Reinforcement Iterative learning Strategy 13

Fault Estimation for Nonlinear Systems with Disturbance Base...

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

作者： Chen, Zhengquan Huang, Ruirui Zhang, Zhiheng Hou, Yandong Henan University School of Computer and Information Engineering Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Artificial Intelligence Henan University Zhengzhou450000 China

ISBN: (纸本)9798350361674

this paper addresses the problem of low fault estimation accuracy in Lipschitz nonlinear systems with external disturbances, presenting an innovative strategy based on a Reinforcement Iterative learning (RIL) framework. Initially, we conducted precise mathematical modeling, comprehensively considering the impact of Lipschitz non-linearity and external disturbances. Subsequently, an iterative learning fault observer was designed, and the optimization problem for its parameters was reformulated into a reinforcement learning task, clearly defining the states, actions, and reward functions. Employing the Advantage Actor-Critic (A2C) reinforcement learning method, we optimized the gain parameters of the iterative learning fault estimator to approximate non-linearity. Simultaneously, with the increase in iteration count, the neural network weights were continually optimized, achieving accurate fault estimation. Experimental results demonstrate a significant improvement in fault estimation using the proposed method. © 2024 ieee.

关键词： Adversarial machine learning

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Small Leak Detection in Pipelines Using Deep learning and Statistical control Chart 13

Small Leak Detection in Pipelines Using Deep Learning and St...

引用

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

作者： Liang, Yu-Chen Cheng, Yi-Hsiang Hung, Zhen-Yu Yao, Yuan National Tsing Hua University Department of Chemical Engineering Hsinchu300044 Taiwan Material and Chemical Research Laboratories Industrial Technology Research Institute Hsinchu310401 Taiwan

ISBN: (纸本)9798350361674

Since the late 20th century, the demand for petrochemical materials and products in industry and daily life has increased, leading to the widespread use of pipelines for transporting these products. Recognizing the importance of pipeline safety, many countries have emphasized safety management through the enactment and amendment of laws, requiring business owners to oversee the design, construction, operation, and maintenance of pipelines. Additionally, business owners must ensure the integrity of pipeline systems, undertake autonomous management, perform regular risk assessments, and implement a monitoring and diagnostic system for industrial pipelines. this study seeks to develop an algorithm that outperforms current leak detection systems, enhancing the safety of industrial pipelines. © 2024 ieee.

关键词： Pipelines

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