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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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data-Driven Prediction of Plunge Milling Force Using Stochastic Differential Equations

Data-Driven Prediction of Plunge Milling Force Using Stochas...

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Industrial Cyber-Physical Systems (ICPS)

作者： Feng Hua Yasen Wang Ziheng Zhou Zhanpeng Yang Zhihui Wang Cheng Cheng School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Institute of Industrial Data Analysis HUST-Wuxi Research Institute Wuxi China

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

Plunge milling constitutes a critical procedure in industrial machinery fabrication, with its applications extending to vital sectors such as aerospace, automotive, and maritime industries. As the demand for precision in plunge milling operations grows, precise modelling of cutting forces is essential for the systematic optimization of CNC milling processes and parameters, including but not limited to feed rate, cutting depth, and others. Current cutting force modelling methodologies that utilize spindle drive current and vibration data often assume constant noise levels. Nevertheless, in many mechanical manufacturing systems, process noise is dependent on system states and inputs, suggesting that the dynamics of cutting force are influenced by more complex multiplicative noise. In response to this complexity, this paper introduces an approach that employs stochastic differential equations to more accurately characterize the dynamics of cutting force. Specifically, we propose an innovative Bayesian method for parameter inference within the stochastic framework. When benchmarked against deep learning models using real measured data, our method demonstrates better accuracy and robustness.

关键词： Force Noise Stochastic processes Milling Predictive models Aerodynamics Mathematical models

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Surface roughness prediction in boring of TC4-DT with multi-sensor data and machining parameters

Surface roughness prediction in boring of TC4-DT with multi-...

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International Conference on Prognostics and Health Management, PHM

作者： Longjianjie Zhang Xiaoyu Liu Ziheng Zhou Zhihui Wang Feng Hua Cheng Cheng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Institute of Industrial Data Analysis HUST-Wuxi Research Institute Wuxi China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350374476

ISBN: (纸本)9798350374483

Surface roughness in boring process is a critical indicator of product quality. With the aim of maximizing quality, there has been a widespread adoption of computer-controlled machine tools that incorporate realtime prediction of surface roughness. However, existing predictive models often rely on truncated multi-sensor data due to computational limits, potentially overlooking essential sensor inputs crucial for accurate prediction. Moreover, while machining parameters like spindle speed, feed rate and cutting depth directly influence surface roughness, their combined use with multi-sensor data in prediction models is rarely explored in existing literature. To bridge these gaps, this paper introduces patch time series CNN-LSTM feature mixed network (PatchCL-MixNet), a novel deep learning approach for predicting surface roughness in boring by integrating multi-sensor data with machining parameters. The method involves segmenting extensive multi-sensor time series data into smaller patches, thereby reducing computational demands while retaining significant temporal information. A specialized encoder-decoder structure, comprising a CNN-LSTM encoder for temporal pattern identification across patches and a channel-mix decoder for feature fusion from both sensor data and machining parameters, is developed. Experimental validation conducted through rigorous boring experiments on TC4-DT titanium alloy demonstrates that our PatchCL-MixNet method outperforms existing models, achieving a 21% improvement in mean absolute percentage error. Furthermore, an ablation study confirms the model’s effectiveness in leveraging both machining parameters and multi-sensor data for enhanced surface roughness prediction.

关键词： Titanium alloys Accuracy Time series analysis Predictive models Surface roughness Product design Rough surfaces

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Emergency Scheduling of Aerial Vehicles via Graph Neural Neighborhood Search

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2025年

作者： Guo, Tong Mei, Yi Du, Wenbo Lv, Yisheng Li, Yumeng Song, Tao Beihang University State Key Laboratory of CNS/ATM School of Electronic and Information Engineering Beijing100191 China Victoria University of Wellington Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Wellington New Zealand Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Jinan250353 China University of the Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Beijing Institute of Technology School of Aerospace Engineering Beijing China

The thriving advances in autonomous vehicles and aviation have enabled the efficient implementation of aerial last-mile delivery services to meet the pressing demand for urgent relief supply distribution. Variable Neighborhood Search (VNS) is a promising technique for aerial emergency scheduling. However, the existing VNS methods usually exhaustively explore all considered neighborhoods with a prefixed order, leading to an inefficient search process and slow convergence speed. To address this issue, this paper proposes a novel graph neural neighborhood search algorithm, which includes an online reinforcement learning (RL) agent that guides the search process by selecting the most appropriate low-level local search operators based on the search state. We develop a dual-graph neural representation learning method to extract comprehensive and informative feature representations from the search state. Besides, we propose a reward-shaping policy learning method to address the decaying reward issue along the search process. Extensive experiments conducted across various benchmark instances demonstrate that the proposed algorithm significantly outperforms the state-of-the-art approaches. Further investigations validate the effectiveness of the newly designed knowledge guidance scheme and the learned feature representations. © 2020 IEEE.

关键词： Reinforcement learning

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Fast Estimation of Relative Transformation Based on Fusion of Odometry and UWB Ranging data

arXiv

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arXiv 2024年

作者： Fu, Yuan Zhang, Zheng Zeng, Guangyang Liu, Chun Wu, Junfeng Ren, Xiaoqiang School of Mechatronic Engineering and Automation Shanghai University China Shanghai Artificial Intelligence Laboratory China Institute of Artificial Intelligence Shanghai University China School of Data Science The Chinese University of HongKong Shenzhen China Key Laboratory of Marine Intelligent Unmanned Swarm Technology and System Ministry of Education China

MSC Codes 93J08In this paper, we investigate the problem of estimating the 4-DOF (three-dimensional position and orientation) robot-robot relative frame transformation using odometers and distance measurements between robots. Firstly, we apply a two-step estimation method based on maximum likelihood estimation. Specifically, a good initial value is obtained through unconstrained least squares and projection, followed by a more accurate estimate achieved through one-step Gauss-Newton iteration. Additionally, the optimal installation positions of Ultra-Wideband (UWB) are provided, and the minimum operating time under different quantities of UWB devices is determined. Simulation demonstrates that the two-step approach offers faster computation with guaranteed accuracy while effectively addressing the relative transformation estimation problem within limited space constraints. Furthermore, this method can be applied to real-time relative transformation estimation when a specific number of UWB devices are installed. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Effective Identification of Lower-Level Optimal Solutions via Discriminator of Conditional Generative Adversarial Network

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Wang, Weizhong Liu, Hai-Lin Tan, Kay Chen School of Automation Guangdong University of Technology China Center for Mathematics and Interdisciplinary Sciences and the School of Mathematics and Statistics Guangdong University of Technology China Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Hong Kong

Bilevel multi-objective optimization problem (BLMOP) can be seen as a special constrained multi-objective optimization problem (CMOP), where the optimality constraint of the lower-level (LL) problem cannot be easily and quickly checked, but must be verified by solving the LL problem. If there was a relatively simple alternative formulation for effectively identifying LL optimal solutions, the abovementioned special constraint could become as simple as the usual constraints and the nested optimization structure of BLMOP would be altered, which can greatly improve efficiency and effectively find LL optimal solutions with excellent upper-level (UL) performance. In this paper, we improve the training mechanism for conditional generative adversarial network (cGAN) by introducing multiple generators to construct a reasonable reference distribution, so as to prevent the discriminator from performance degradation. With a discriminator identifying LL optimal solutions effectively, the nested optimization structure of BLMOP is altered by adding the objective that maximizing the discriminator output score into the UL objectives and removing the LL optimality constraint, realizing synchronous optimization of UL and LL vectors. The cooperation of generators and discriminator greatly reduces the computational overhead for solving BLMOPs. The proposed algorithm has achieved the best or competitive results in comparison with 6 state-of-the-art algorithms and a nested method on benchmark problems and a real-world problem, whose effectiveness has been demonstrated. IEEE

关键词： Generative adversarial networks

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Privacy Preserving Discretized Spiking Neural Network with TFHE

Privacy Preserving Discretized Spiking Neural Network with T...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Li, Pengbo Gao, Ting Huang, Huifang Cheng, Jiani Gao, Shuhong Zeng, Zhigang Duan, Jinqiao Huazhong University of Science and Technology Center for Mathematical Sciences Wuhan China Huazhong University of Science and Technology School of Mathematics and Statistics Wuhan China Clemson University School of Mathematical and Statistical Sciences Clemson United States Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Great Bay University The Dongguan Key Laboratory for Data Science and Intelligent Medicine Depatment of Mathematics and Department of Physics Dongguan China

ISBN: (纸本)9798350316889

artificial intelligence not only offers significant convenience but also raises privacy and security concerns, which pose challenges for Spiking Neural Networks (SNNs). Fully Homomorphic Encryption (FHE) is a crucial privacy-preserving computation technology, and the discrete nature of SNNs naturally aligns with FHE's requirements. In this paper, we construct a framework to evaluate the homomorphic computation of SNN named FHE-DiSNN that enables SNN to achieve good prediction performance on encrypted data. First, benefitting from the discrete nature of spike signals, our proposed model avoids the errors introduced by discretizing activation functions. Second, by applying bootstrapping, we design new privacy-preserving functions FHE-Fire and FHE-Reset, through which noise can be refreshed, allowing us to evaluate SNN for an arbitrary number of operations. Furthermore, we improve the computational efficiency of FHE-DiSNN while maintaining a high level of accuracy. Finally, we evaluate our model on the MNIST dataset. The experiments show that FHE-DiSNN with 30 neurons in the hidden layer achieves a minimum prediction accuracy of 94.4%. Under optimal parameters, it achieves a 95.1% accuracy, with only a 0.6% decrease compared to the original SNN (95.7%). These results demonstrate the superiority of SNN over second-generation neural networks for homomorphic evaluation. © 2023 IEEE.

关键词： Computational efficiency

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Model-based Reinforcement Learning for Sim-to-Real Transfer in Robotics using HTM neural networks

Model-based Reinforcement Learning for Sim-to-Real Transfer ...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： M. R. Diprasetya A. N. Pullani A. Schwung D. Schwung Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany Department of Artificial Intelligence and Data Science Hochschule Düsseldorf University of Applied Sciences Düsseldorf Germany

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In this work we propose a novel approach based on model-based Reinforcement Learning (RL) for the sim-to-real transfer of industrial robots. Specifically, we propose to employ a recently developed kinematics-informed, modular neural network serving as a learnable environment model within the world model framework. Using the kinematics-informed model, training of the world model is made more efficient resulting in faster training. Furthermore, the approach allows to train industrial robots on specific tasks solely within the simulation of the system thereby saving time and energy-consumption. Using simulations ensures safe and controlled training implementation and allows for parallelization to increase training speed. We conduct various experiments which underline the effectiveness of the proposed method. We show that training the RL algorithm solely within the simulation, results in a hundred percent task completion rate in both simulation and real world experiments.

关键词： Training Service robots Simulation Reinforcement learning artificial neural networks Industrial robots Information technology

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Automatic Fruit Detection Based on Robotic Vision

Automatic Fruit Detection Based on Robotic Vision

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Big data & artificial intelligence & Software Engineering (ICBASE), International Conference on

作者： Yiran Chen Rui Yao Haoran Liu Bingqi Liu Mingzhe Liu School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Norla Institute of Technical Physics Chengdu China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

Automatic fruit detection has greatly reduced labor costs and crop damage rates, contributing to the progress of agricultural modernization. It involves real-time assessment of the surrounding environment and recognition of target fruit categories. However, fruit detection faces challenges due to the similarities in shape, color, and other features among different types of fruits, as well as the impact of environmental factors such as inadequate lighting, occlusion, and background variations on recognition accuracy. This project focuses on the simulation of the robotic fruit harvesting process, which includes environmental image acquisition, image processing, and robot control. The robot utilizes its integrated camera to capture real-time images of the surrounding environment. These images are then processed on a computer, employing color recognition based on traditional algorithms. The images are segmented based on the identified color regions and subjected to object detection using YOLO v5. By discerning the presence of the target objects, the results are transmitted to the robot, which responds accordingly. The real-time performance is enhanced by filtering and narrowing down the target regions, reducing the time required for object recognition.

关键词： YOLO Image recognition Accuracy Image color analysis Target recognition Lighting Laser feedback Real-time systems Computational efficiency Laser beams

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Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs

arXiv

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arXiv 2024年

作者： Lin, Xinyu Zhang, Tianyu Hou, Chengbin Wang, Jinbao Xue, Jianye Lv, Hairong School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China Department of Automation Tsinghua University Beijing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Node Importance Estimation (NIE) is a task that quantifies the importance of node in a graph. Recent research has investigated to exploit various information from Knowledge Graphs (KGs) to estimate node importance scores. However, the semantic information in KGs could be insufficient, missing, and inaccurate, which would limit the performance of existing NIE models. To address these issues, we leverage Large Language Models (LLMs) for semantic augmentation thanks to the LLMs’ extra knowledge and ability of integrating knowledge from both LLMs and KGs. To this end, we propose the LLMs Empowered Node Importance Estimation (LENIE) method to enhance the semantic information in KGs for better supporting NIE tasks. To our best knowledge, this is the first work incorporating LLMs into NIE. Specifically, LENIE employs a novel clustering-based triplet sampling strategy to extract diverse knowledge of a node sampled from the given KG. After that, LENIE adopts the node-specific adaptive prompts to integrate the sampled triplets and the original node descriptions, which are then fed into LLMs for generating richer and more precise augmented node descriptions. These augmented descriptions finally initialize node embeddings for boosting the downstream NIE model performance. Extensive experiments demonstrate LENIE’s effectiveness in addressing semantic deficiencies in KGs, enabling more informative semantic augmentation and enhancing existing NIE models to achieve the state-of-the-art performance. The source code of LENIE is freely available at https://***/XinyuLin-FZ/LENIE. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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