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arXiv 2024年

作者： Ma, Xinyue Wang, Chenxing The School of Automation The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

3D single-pixel imaging (SPI) is a promising imaging technique that can be flexibly applied to various wavebands. The main challenge in 3D SPI is that the calibration usually requires a large number of standard points as references, which are tricky to capture using single-pixel detectors. Conventional solutions involve sophisticated device deployment and cumbersome operations, resulting in hundreds of images needed for calibration. In our work, we construct a Calibration Field (CaliF) to efficiently generate the standard points from one single image. A high accuracy of the CaliF is guaranteed by the technique of deep learning and digital twin. We perform experiments with our new method to verify its validity and accuracy. We believe our work holds great potential in 3D SPI systems or even general imaging systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Calibration

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3D Point Cloud-Based Lithium Battery Surface Defects Detection Using Region Growing Proposal Approach 8th

3D Point Cloud-Based Lithium Battery Surface Defects Detecti...

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8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Rehman, Zia Ur Wang, Xin Alsumeri, Abdulrahman Abdo Ali Khan, Malak Abid Ali Ma, Hongbin State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819975891

Detecting the lithium battery surface defects is a difficult task due to the illumination reflection from the surface. To overcome the issue related to labeling and training big data by using 2D techniques, a 3D point cloud-based technique has been proposed in this paper. The 3D point cloud-based defect detection of lithium batteries used feature-based techniques to downscale the point clouds to reduce the computational cost, extracting the normals of the points and calculating their differences to detect the defects of the battery which assure the quality of the product. This paper offers a novel strategy using 3D point clouds to get beyond the labeling and training challenges involved with conventional 2D approaches. This 3D point cloud-based approach for lithium battery fault identification makes use of feature-based methods to improve the point cloud data and lessen the computing burden. In our work, the experiments show that the feature-based technique precisely detects the affected surface of the battery. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithium batteries

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Anti-disturbance speed regulation control of PMSM at low speed by extended state observer and kalman filter

Anti-disturbance speed regulation control of PMSM at low spe...

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2023 Jiangsu Annual Conference on Automation, JACA 2023

作者： Xu, Lei Mao, Linxuan Li, Shihua School of Electronic Science and Engineering Southeast University Nanjing210096 China School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9781839539848

This paper presents an enhanced friction compensation anti-disturbance control strategy for improving the speed regulation control performance of a permanent magnet synchronous motor operating in low-speed mode. This is achieved via friction modelling and lumped disturbance compensation by seamlessly integrating extended state observer (ESO) and Kalman filter algorithms. Considering that nonlinear friction torque can not be ignored at low speeds, a piece-wise linearized friction model is first established based on speed estimation of Kalman filter and ESO. Then feed-forward compensation of friction torque and lumped disturbance estimation is conducted to reduce speed fluctuation during load changes at low speeds. Comparative simulation studies in the presence of measurement noises, external disturbances and nonlinear friction torque are conducted to validate the proposed algorithm with improved performance. © The Institution of Engineering & Technology 2023.

关键词： Kalman filters

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Research on Image Segmentation of Power Line Based on Encoder-Decoder Network 16th

Research on Image Segmentation of Power Line Based on Encode...

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16th Annual Conference of China Electrotechnical Society, CES 2021

作者： Sun, Haotian Fang, Zhijian Wei, Zhiguo Xie, Fei School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

ISBN: (纸本)9789811915277

Power line network is the main carrier of the power system, and the smooth patrol inspection plays a crucial role in the fault prevention of the power line network. With the emergence of the patrol inspection UAV, how to detect the target of the power line through the picture and carry out accurate positioning has become a major research focus of the intelligent power system. A method for detecting and locating power lines based on an encoding lightweight decoder network (EDN-light) is proposed in this paper. It provides a foundation for the research and development of intelligent inspection of power line network, such as detection and identification, fault location, and guarantee of operation safety, and puts forward a new solution to the fault location diagnosis of power line using image. By EDN-light network, it focuses on detecting high level target and eliminating irrelevant information such as background, the convolutional layer of the encoder is used for extracting features, and the deconvolutional layer of the decoder for reconstructing images. The EDN-light network can improve the detection accuracy, and generate high-quality segmentation object suggestions. Use Turkish Electricity Transmission Corporation's aviation power line network dataset and self-made dataset to train the model to improve the generalization ability of the model. Experiments have found that due to the fully convolutional network design, it can carry out multi-scale input, For power line image segmentation and detection, the accuracy is better, and the evaluation indicators of the proposed models have increased. And due to the design of the lightweight decoder, Without retraining the network, the learned model can be extended to unknown linear category targets without repeated training. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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Data-Driven Multi-step Nonlinear Model Predictive control for Industrial Heavy Load Hydraulic Robot

arXiv

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arXiv 2024年

作者： Ma, Dexian Zhou, Bo Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education Nanjing210096 China

Automating complex industrial robots requires precise nonlinear control and efficient energy management. This paper introduces a data-driven nonlinear model predictive control (NMPC) framework to optimize control under multiple objectives. To enhance the prediction accuracy of the dynamic model, we design a single-shot multi-step prediction (SSMP) model based on long short-term memory (LSTM) and multilayer perceptrons (MLP), which can directly obtain the predictive horizon without iterative repetition and reduce computational pressure. Moreover, we combine offline and online models to address disturbances stemming from environmental interactions, similar to the superposition of the robot’s free and forced responses. The online model learns the system’s variations from the prediction mismatches of the offline model and updates its weights in real time. The proposed hybrid predictive model simplifies the relationship between inputs and outputs into matrix multiplication, which can quickly obtain the derivative. Therefore, the solution for the control signal sequence employs a gradient descent method with an adaptive learning rate, allowing the NMPC cost function to be formulated as a convex function incorporating critical states. The learning rate is dynamically adjusted based on state errors to counteract the inherent prediction inaccuracies of neural networks. The controller outputs the average value of the control signal sequence instead of the first value. Simulations and experiments on a 22-ton hydraulic excavator have validated the effectiveness of our method, showing that the proposed NMPC approach can be widely applied to industrial systems, including nonlinear control and energy management. Copyright © 2024, The Authors. All rights reserved.

关键词： Excavators

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Robust Delay-Dependent Stability for Uncertain Linear systems with Time-Varying Delay 49

Robust Delay-Dependent Stability for Uncertain Linear System...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chen, Dong-Shuai He, Yong Shangguan, Xing-Chen School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9798350331820

This paper investigates the problem of stability analysis for the uncertain linear systems with time-varying delay. Firstly, an uncertain linear system model considering time-varying delay is established. Then based on the Lyapunov-Krasovskii functional (LKF) method, a novel robust delay-dependent stability criterion is proposed, which is benefited by a new augmented LKF with more effective time-delay information and the use of a tighter integral inequality to estimate functional derivative. The stability criterion obtained is less conservative. At last, a numerical example shows the superiority and effectiveness that the method used in this paper. © 2023 IEEE.

关键词： Time delay

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Improving Collision-Free Success Rate For Object Goal Visual Navigation Via Two-Stage Training With Collision Prediction

arXiv

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arXiv 2025年

作者： Lian, Shiwei Zhang, Feitian Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China

The object goal visual navigation is the task of navigating to a specific target object using egocentric visual observations. Recent end-to-end navigation models based on deep reinforcement learning have achieved remarkable performance in finding and reaching target objects. However, the collision problem of these models during navigation remains unresolved, since the collision is typically neglected when evaluating the success. Although incorporating a negative reward for collision during training appears straightforward, it results in a more conservative policy, thereby limiting the agent’s ability to reach targets. In addition, many of these models utilize only RGB observations, further increasing the difficulty of collision avoidance without depth information. To address these limitations, a new concept—collision-free success is introduced to evaluate the ability of navigation models to find a collision-free path towards the target object. A two-stage training method with collision prediction is proposed to improve the collision-free success rate of the existing navigation models using RGB observations. In the first training stage, the collision prediction module supervises the agent’s collision states during exploration to learn to predict the possible collision. In the second stage, leveraging the trained collision prediction, the agent learns to navigate to the target without collision. The experimental results in the AI2-THOR environment demonstrate that the proposed method greatly improves the collision-free success rate of different navigation models and outperforms other comparable collision-avoidance methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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A Data-Driven Modeling and Motion control of Heavy-Load Hydraulic Manipulators via Reversible Transformation

arXiv

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arXiv 2024年

作者： Ma, Dexian Liu, Yirong Liu, Wenbo Zhou, Bo Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education Nanjing210096 China

This work proposes a data-driven modeling and the corresponding hybrid motion control framework for unmanned and automated operation of industrial heavy-load hydraulic manipulator. Rather than the direct use of a neural network black box, we construct a reversible nonlinear model by using multilayer perceptron to approximate dynamics in the physical integrator chain system after reversible transformations. The reversible nonlinear model is trained offline using supervised learning techniques, and the data are obtained from simulations or experiments. Entire hybrid motion control framework consists of the model inversion controller that compensates for the nonlinear dynamics and proportional-derivative controller that enhances the robustness. The stability is proved with Lyapunov theory. Co-simulation and Experiments show the effectiveness of proposed modeling and hybrid control framework. With a commercial 39-ton class hydraulic excavator for motion control tasks, the root mean square error of trajectory tracking error decreases by at least 50% compared to traditional control methods. In addition, by analyzing the system model, the proposed framework can be rapidly applied to different control plants. Copyright © 2024, The Authors. All rights reserved.

关键词： Industrial manipulators

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Study on Self-Organizing Aggregation Method for Massive Flexible Resources and Its Simulation 4

Study on Self-Organizing Aggregation Method for Massive Flex...

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4th International Conference on Advanced Electrical and Energy systems, AEES 2023

作者： Yin, Changwei Ji, Wenlu Ye, Ting Mao, Tingfeng Zhang, Kaifeng Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

ISBN: (纸本)9798350342659

Distributed flexible resources are developing in the trend of sea quantization. In this situation, the existing resource aggregation methods are facing bottlenecks, and it is difficult to fully exploit the regulation potential of massive flexible resources. In order to achieve efficient and flexible aggregation in the scenario of massive resources, this paper proposes a self-organizing aggregation method for flexible resources based on the social network and simulates the aggregation process under the proposed method based on the multi-agent simulation platform NetLogo. Firstly, this paper outlines the idea of the self-organizing aggregation method for flexible resources, which projects flexible resources from the physical space to the social network space, and realizes flexible aggregation through the interactive attraction between resources in the social network. Secondly, this paper establishes a simulation model for flexible resource self-organizing aggregation based on the NetLogo platform. The model defines four types of agents involved in the self-organizing aggregation process and provides detailed specifications for their attributes and interaction rules. Finally, simulation experiments are designed to verify the rationality of the self-organizing aggregation method and simulation model proposed in this paper. The simulation results show that, following the idea of self-organizing aggregation, massive flexible resources can autonomously and flexibly aggregate through the interaction attraction in the social network due to the incentive of task gain, demonstrating the effectiveness and rationality of the self-organizing aggregation method and the simulation model proposed in this paper. © 2023 IEEE.

关键词： Multi agent systems

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Distributed Resilient State Estimation for Time-Varying Fractional-Order Discrete complex Networks with Encoding-Decoding Scheme 42

Distributed Resilient State Estimation for Time-Varying Frac...

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42nd Chinese control Conference, CCC 2023

作者： Chen, Yu Hu, Jun Li, Jiaxing Zhou, Aozhan Yu, Hui Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China Harbin University of Science and Technology Heilongjiang Prov. Key Lab. of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China School of Automation Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9789887581543

In this paper, the distributed state estimation method with resilient attenuation feature is proposed for time-varying fractional-order complex networks under encoding-decoding mechanism. The encoding-decoding-induced dynamic errors for distinct nodes are characterized by the truncated Gaussian distributions. In order to compensate the effects induced by encoding-decoding scheme, the variances of encoding-decoding-induced dynamic errors are considered in process of designing the resilient distributed estimation algorithm. In particular, the upper bounds of updated estimation error covariances are derived firstly. Subsequently, the upper bounds are minimized by constructing the gain matrices at each sampling instant. Besides, a sufficient condition is provided to guarantee the boundedness of estimation error dynamics in the mean-square sense. Finally, the validity of distributed resilient state estimation scheme is demonstrated by a simulation example. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： complex networks

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