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检索条件"任意字段=6th International Conference on Informatics in Control, Automation and Robotics"

共 2793 条记录，以下是521-530 订阅

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E2F2: End-to-End Feature-Free Network for Lithium-Ion Battery State of Health Prediction

E2F2: End-to-End Feature-Free Network for Lithium-Ion Batter...

引用

international conference on Electronic Engineering and informatics (EEI)

作者： Ziyue Huang Lujuan Dang Lei Xing National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

With the development of new energy technologies, such as solar and wind power, lithium-ion batteries are being widely used for energy storage and distribution. the State-of-Health (SOH) is very important for the safety and reliability of battery applications. However, existing SOH prediction methods usually require health feature extraction for model training, which limits the coherence and reliability of SOH prediction. To deal with this issue, we propose an End-to-End Feature-Free network (E2F2). the proposed model is an end-to-end neural network model that combines convolutional and Gated Recurrent Unit (GRU) architectures to process raw battery data, effectively capturing both spatial and temporal dependencies without relying on pre-defined features, thus simplifying modeling and enhancing adaptability. Experimental results on a real dataset illustrate the high accuracy and reliability of the proposed method.

关键词： Lithium-ion batteries Training Adaptation models Accuracy Neural networks Logic gates Wind power generation Feature extraction Data models Safety

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Estimation of RoA for Nonlinear Systems Based on Gaussian Processes

Estimation of RoA for Nonlinear Systems Based on Gaussian Pr...

引用

international conference on Electronic Engineering and informatics (EEI)

作者： Yucheng Chen Yupeng Zhu Hongming Zhu Quanqi Zhang Chengwei Wu Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

this paper employs Gaussian Processes (GPs) to estimate uncertainty dynamics inherent in nonlinear systems. A control policy ensuring the system satisfies initial stability constraints is derived using linear quadratic regulator (LQR). By modeling the Lyapunov function based on uncertain dynamics as GPs, the stability of the system dynamics in the Lyapunov sense is ensured. To discretize the Lyapunov function with Lipschitz continuity, a finite number of discrete points are chosen to represent the properties of the function. the region of attraction (RoA) based on Lyapunov function is widely utilized as a safe constraint in the field of control. Additionally, the maximum uncertainty dynamics are chosen as the boundary for exploration to expand the evaluation of the RoA. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed approach.

关键词： Uncertainty Regulators System dynamics Estimation Gaussian processes Stability analysis Nonlinear dynamical systems informatics thermal stability Lyapunov methods

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Design of Simulation and Evaluation System for Safety Operation in High Energy Radiation Scene Based on SysML

Design of Simulation and Evaluation System for Safety Operat...

引用

international conference on Electronic Engineering and informatics (EEI)

作者： Huai Wu Baoran An Weijian Huang Xuedong Zhang Sifeng Zeng Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

With the emergence and wide application of new energy sources and technologies, various high-energy radiation devices have become indispensable core facilities and play an important role. At present, there are not only a large number of high-energy radiation devices in service, but also many have been or will enter the decommissioning stage. Aiming at the uncertainty and fuzziness of the traditional safety evaluation methods in the maintenance and decommissioning of high-energy radiation equipment, this paper designs the algorithms of task allocation, path planning and safety ergonomics evaluation for high-energy radiation equipment. Based on the system modeling language, the rationality of the system control flow is verified, and a simulation evaluation system for high-energy radiation equipment is developed to realize the comprehensive evaluation of safety operation schemes.

关键词： Uncertainty Ergonomics Process control control systems Systems Modeling Language Path planning Safety Maintenance Resource management informatics

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Form-finding of Tensegrity Structures via Monte-Carlo Force Density Random Search

Form-finding of Tensegrity Structures via Monte-Carlo Force ...

引用

IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Zhengyin Du Mingxiang Zheng Min Lin Zhifei Ji College of Marine Equipment and Mechanical Engineering Jimei University Xiamen China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

In this paper, a novel random search algorithm on the basis of the Monte-Carlo method is proposed to solve the form-finding problem of tensegrity structures. the optimization model of tensegrity structures has been developed under the conditions that semi-definite force density matrix should satisfy the rank deficiency constraints. Given the structural topology and the types of the members of tensegrity structures, by searching the force density vectors, the node coordinates are obtained. It follows that equilibrium configurations are derived. Afterwards, numerical simulations have been conducted. the results indicate that the proposed random search algorithm can solve the form-finding problems of tensegrity structures effectively.

关键词： Monte Carlo methods Accuracy Optimization models Force Refining Search problems Vectors Robustness Topology robotics and automation

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Deep Koopman Operator Based Model Predictive control for Nonlinear robotics Systems 6

Deep Koopman Operator Based Model Predictive Control for Non...

引用

6th IEEE international conference on Advanced robotics and Mechatronics (ICARM)

作者： Wang, Xuefeng Kang, Yu Cao, Yang Univ Sci & Technol China Dept Automat Hefei Peoples R China Univ Sci & Technol China State Key Lab Fire Sci Hefei 230027 Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei 230027 Peoples R China

ISBN: (纸本)9780738133645

Modeling and control of nonlinear robotic systems have been challenging tasks. If a linear approximate embedding space for nonlinear dynamical robotic systems can be constructed, well-established techniques in the field of linear systems are expected to be used to deal with this problem. the Koopman theory suggests that a data-driven approach can be used to construct a suitable set of observation functions to map the nonlinear system into an equivalent linear model in the embedding space. We use deep neural networks to construct more adaptive sets of observation functions, treat the control inputs as generalized states, learn the input-Koopman operator of the controlled nonlinear robotic system, and construct the embedded linear state model DKoopman-predictor. the learned linear state model is then used to design the model prediction controller (DKoopman-MPC) to control the original nonlinear robotic system. the proposed approach is easy to implement and is data-driven without the need for a priori knowledge of model dynamics. Our experiments on mobile robot modeling and control show that the proposed method has higher model fidelity than existing local linearization methods, achieving 79.27% error reduction in the prediction task and has good convergence properties in the control task.

关键词： Adaptation models Simulation Neural networks Predictive models Aerospace electronics Nonlinear dynamical systems Mobile robots

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Verification of Delayed Detectability for Unambiguous Weighted Automata

Verification of Delayed Detectability for Unambiguous Weight...

引用

IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Guoqiang Xu Shaowen Miao Aiwen Lai Weiyao Lan Department of Automation Xiamen University Xiamen China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

Detectability verification is an important problem that is tightly related to state estimation in the domain of discrete-event systems. Delayed detectability, referred to as (k 1 , k 2 )-detectability, is a property that necessitates the ability to determine the states of the system at the instant when the k 1 th event occurs, following an observation of k 1 + k 2 events. this differs from the conventional detectability problem due to the introduction of information delay. this paper delves into the examination of delayed detectability for unambiguous weighted automata. We employ a verification algorithm, strongly inspired by the work in [1], to verify this property. the fundamental concept involves verifying, after observing k 1 events, whether each pair of current and subsequent state estimates can be ascertained after k 2 -step delays. Furthermore, we illustrate the pivotal steps of this algorithm using straightforward examples to validate the (k 1 , k 2 )-detectability of the examined unambiguous weighted automaton.

关键词： Sufficient conditions Mechatronics Automata Delays Discrete-event systems State estimation robotics and automation

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An Acupoint Location Method Based on Lightweight Efficient Channel Attention Network

An Acupoint Location Method Based on Lightweight Efficient C...

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international conference on Electronic Engineering and informatics (EEI)

作者： Chengbin Wang Zhaoshui He Xu Wang Bo Yang Jing Guo Hao Liang Zhijie Lin Liqiang Zhang School of Automation Guangdong University of Technology GuangZhou China School of Electronics and Communication Guangdong Mechanical and Electrical Polytechnic Guangzhou China Beijing Research Institute of Automation for Machinery Industry Beijing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

In Traditional Chinese Medicine (TCM), acupuncture localization plays a crucial role in the adjunctive prevention and treatment of various diseases. Traditional localization methods rely on anatomical knowledge and experience, but they are limited in accuracy and labor-intensive. Recently, deep learning techniques have emerged in the medical field due to advancements in computer networks, offering new avenues for acupoint localization. Inspired by the success of deep learning in medical imaging, we propose a Lightweight Efficient Channel Attention (LECA) network for acupoint localization. Specifically, MobileNetV2, a representative lightweight network, is adopted as the backbone of the LECA network to construct an efficient acupoint localization network with high accuracy. Additionally, Efficient Channel Attention (ECA) is integrated to enhance feature extraction capabilities. LECA network effectively improves the accuracy rate and reduces the number of parameters, and the average accuracy on the test image dataset reaches 87.7%, which is better than other existing deep learning methods.

关键词： Location awareness Deep learning Measurement Accuracy Prevention and mitigation Feature extraction Computer networks informatics Biomedical imaging Diseases

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Synchronization of Delayed Neural Networks via Self-Triggered Impulsive control Strategy

Synchronization of Delayed Neural Networks via Self-Triggere...

引用

international conference on Electronic Engineering and informatics (EEI)

作者： Fei Chang Chuandong Li Yinghua Zhou College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

this paper addresses the issue of asymptotic synchronization in delayed neural networks (DNNs) by designing a rational self-triggered impulsive control (STIC) strategy. By applying a Razumikhin-like comparison lemma, the presence of time delays in the system is considered, and a self-triggered mechanism (STM) is formulated to determine the impulsive interval based on the previous impulsive moment. Utilizing the developed STIC strategy, a set of sufficient conditions for achieving asymptotic synchronization of DNNs is established, with a proof ensuring the exclusion of the Zeno phenomenon. Compared to the event-triggered impulsive control (ETIC) strategy, this STIC strategy offers an advantage by predicting the next impulse occurrence based on the previous impulsive instant and available information, eliminating the need for continuous system state monitoring to verify triggering conditions. this strategy reduces communication costs and enhances resource utilization.

关键词： Sufficient conditions Costs Delay effects Neural networks Continuous time systems Trajectory Synchronization Resource management informatics Monitoring

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Robust Extended Kalman Filter for Graphical Nonlinear Systems with Hybrid Cyber-Attacks

Robust Extended Kalman Filter for Graphical Nonlinear System...

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international conference on Electronic Engineering and informatics (EEI)

作者： Simeng Guo Wenling Li School of Automation Science and Electrical Engineering Beihang University (BUAA) Beijing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

this paper studies the extended Kalman filtering for graphical nonlinear systems with hybrid cyber-attacks. the considered system states and measurement outputs are generated based on graph structures, and the measurement signals are subject to denial-of-service attacks and deception attacks. In order to improve the filtering performance of graphical nonlinear systems subjected to hybrid cyber-attacks, we design an extended Kalman filter with a diagonal gain matrix in the graph frequency domain by means of the graph Fourier transform. the diagonal form of the gain matrix allows the states of each node to be updated independently, thus reducing the iterative accumulation error. Further, we introduce higher-order terms in the first-order Taylor expansion of the nonlinear function to compensate for the linearization error. Finally, we obtain the diagonal Kalman gain matrix by solving two Riccati-like equations. A simulation example on a power system validates the superior performance of the proposed filter against hybrid cyber-attacks.

关键词： Filtering Frequency-domain analysis Taylor series Mathematical models Hybrid power systems Kalman filters Iterative methods Nonlinear systems informatics Cyberattack

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Distributed Data-Driven Iterative Learning control for Consensus Tracking of MASs against Deception Attacks

Distributed Data-Driven Iterative Learning Control for Conse...

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international conference on Electronic Engineering and informatics (EEI)

作者： Runchang Zou Xuefang Li Deqing Huang School of Intelligent Systems Engineering shenzhen campus of Sun Yat-Sen University Shenzhen China School of Electrical Engineering Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

In the paper, we study the problem of distributed model-free adaptive iterative learning control (DMFAILC) for multiagent systems (MAS) under deception attacks. Firstly, we introduce an improved dynamic linearization technology in iteration domain to establish an equivalent data model of MASs. then, an attacks compensation strategy is developed to mitigate the impact of deception attacks. Subsequently, a DMFAILC scheme is developed based on input and output of the system model. Moreover, the convergence of the consensus tracking error is proved using the contraction mapping method. Finally, a simulation result is provided to verify the effectiveness of the developed DMFAILC scheme.

关键词： Adaptation models Network topology Simulation Data models Topology Nonlinear dynamical systems informatics Iterative learning control Convergence Multi-agent systems

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