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Reduction of data amount in data-driven design of linear quadratic regulators

control Theory and Technology 2024年第4期22卷 532-542页

作者： Shinsaku Izumi Xin Xin School of Systems Engineering Kochi University of TechnologyKamiKochi782-8502Japan School of Automation Southeast Universityand Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of EducationNanjing210096JiangsuChina

This paper discusses the data-driven design of linear quadratic regulators,i.e.,to obtain the regulators directly from experimental data without using the models of *** particular,we aim to improve an existing design method by reducing the amount of the required experimental *** the data amount leads to the cost reduction of experiments and computation for the data-driven *** present a simplified version of the existing method,where parameters yielding the gain of the regulator are estimated from only part of the data required in the existing *** then show that the data amount required in the presented method is less than half of that in the existing method under certain *** addition,assuming the presence of measurement noise,we analyze the relations between the expectations and variances of the estimated parameters and the *** a result,it is shown that using a larger amount of the experimental data might mitigate the effects of the noise on the estimated *** results are verified by numerical examples.

关键词： Data-driven design Linear quadratic regulators Linear systems Riccati equation Stochastic properties

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Interval analysis for neural networks with application to fault detection

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Science China(Information Sciences) 2024年第11期67卷 222-237页

作者： Zhenhua WANG Youdao MA Song ZHU Thach Ngoc DINH Yi SHEN Department of Control Science and Engineering Harbin Institute of Technology National Key Laboratory of Complex System Control and Intelligent Agent Cooperation School of Mathematics China University of Mining and Technology The CEDRIC-Lab Conservatoire National des Arts et Métiers (CNAM)

This paper investigates an interval analysis method for neural networks and applies it to fault detection for systems with unknown but bounded measurement noise. First, a novel interval analysis method is presented, which can compute the bounds of the output of a feedforward neural network subject to a bounded input. By applying the proposed interval analysis method to a network trained with fault-free system data, adaptive thresholds for fault detection are computed. Finally, one can acquire fault detection results via a fault detection strategy. The proposed method can achieve tight bounds of the network output and employ simple operations, which leads to accurate fault detection results and a low computational burden.A numerical simulation and an experiment on an AC servo motor are given to illustrate the effectiveness and superiority of the proposed method.

关键词： interval analysis feedforward neural network fault detection adaptive thresholds

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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RSD-Diff: Boundary-Frequency Feature-Enhanced Rail Surface Defect Inspection with Diffusion-Based Transformer Decoder 10

RSD-Diff: Boundary-Frequency Feature-Enhanced Rail Surface D...

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10th International Conference on systems and Informatics, ICSAI 2024

作者： Wang, Dezhou Su, Shixiang Lu, Xiaobo School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9798331510138

Rail surface defect inspection is an essential task for the railway system. However, due to the similarity of the background and defect foreground pixels, uneven textures, irregular shapes and multiple scales of the existing defects, the inspection accuracy is still required to be further improved. In this paper, we first expand our dataset by GAN-based method for the scarcity of the defect samples. Then, we propose a diffusion-based model to perform the classification and segmentation task. The diffusion model predicts the mask from the Gaussian noise broken ground truth by successive designed transformer decoder. Meanwhile, to better distinguish the pixels in the boundary or other illegible regions, a boundary-frequency feature enhancement module (BFM) is proposed to make the model pay more attention to the defect-related features. Experiments on the rail surface defect dataset revealed that the proposed diffusion-based transformer decoder and BFM can work well together and efficiently improve the rail surface defect segmentation accuracy. © 2024 IEEE.

关键词： Decoding

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Guided Transformative GAN: Joint Face Image Super-Resolution and Frontalization 10

Guided Transformative GAN: Joint Face Image Super-Resolution...

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10th International Conference on systems and Informatics, ICSAI 2024

作者： Mu, Minghao Wang, Wei Lu, Xiaobo School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9798331510138

Joint face super-resolution and frontalization of non-frontal low-resolution faces is of significant importance for many face analysis applications. However, at the same time, it is a challenging task. In this paper, a novel Guided Transformative Generative Adversarial Network (GT-GAN) is proposed to address the challenge of generating frontal high-resolution face images from non-frontal low-resolution face images. Existing methods usually treat super-resolution and frontalization as two separate tasks, leading to unsatisfactory processing results. GTG AN innovatively achieves both goals within a unified frame-work. Specifically, we propose Profile Attention Guidance (PAG) to generate landmark heatmaps that provide a priori pose information to the generative network and direct its attention to critical facial regions. In addition, an Attention Fusion Transformation Module (AFTM) is proposed to significantly improve the hallucination performance of the network by utilizing the attention mechanism and STNs, combined with sequential feature enhancement strategies. The experimental results show that GT-GAN exhibits superior performance in the joint face super-resolution and frontalization task on the public face dataset Multi-PIE, which provides new ideas and references for this challenging topic. © 2024 IEEE.

关键词： Generative adversarial networks

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A Model-Based Unmatched Disturbance Rejection control Approach for Speed Regulation of a Converter-Driven DC Motor Using Output-Feedback

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 365-376页

作者： Lu Zhang Jun Yang Shihua Li School of Automation Southeast UniversityKey Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education IEEE Department of Aeronautical and Automotive Engineering Loughborough University

The speed regulation problem with only speed measurement is investigated in this paper for a permanent magnet direct current(DC)motor driven by a buck *** lumping all unknown matched/unmatched disturbances and uncertainties together,the traditional active disturbance rejection control(ADRC)approach provides an intuitive solution for the problem under ***,for such a higher-order disturbed system,the increase of poles for the extended state observer(ESO)therein will lead to drastically growth of observer gains,which causes severe noise *** paper aims to propose a new model-based disturbance rejection controller for the converter-driven DC motor system using *** of estimating lumped disturbances directly,a new observer is constructed to estimate the desired steady state of control signal as well as errors between the real states and their desired steady-state ***,a controller with only speed measurement is proposed by utilizing the *** performance of the proposed method is tested through experiments on *** is further shown via numerical calculations and experimental results that the poles of the observer within the proposed control approach can be largely increased without significantly increasing magnitude of the observer gains.

关键词： Bandwidth widening converter-driven direct current(DC)motor disturbance observer model-based disturbance rejection speed regulation

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Thickness measurement of Single-Mode Fiber Coating Layer Based on Forward Brillouin Scattering

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IEEE Sensors Journal 2025年第11期25卷 19332-19340页

作者： Zhang, Yongqi Wang, Tao Liu, Yijia Liu, Li Zhang, Mingjiang Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education College of Physics and Optoelectronic Engineering Taiyuan030024 China Taiyuan University of Technology Shanxi Key Laboratory of Precision Measurement Physics College of Physics and Optoelectronic Engineering Taiyuan030024 China

The coating layer is essential for protecting and applying optical fibers. Nowadays, the thickness measurement of the coating layer relies on point measurement schemes such as the side-looking light distribution method and mechanical method. However, there are some problems with the above methods, such as the trade-off between resolution and field of view, the cutting of the measurement position and the long measurement time. To overcome these problems, we propose a new method based on forward Brillouin scattering, which measures the coating layer thickness of single-mode fibers. The physical properties of the transverse acoustic field involved in forward Brillouin scattering make it possible to measure the thickness of the fiber coating. The corresponding relationship between the transverse acoustic mode frequency and the diameter of the coating layer can be calculated. The coating layer thickness can be measured with high precision by measuring the frequency spectrum of forward Brillouin scattering corresponding to multiple transverse acoustic modes at a given temperature. In the verification experiment, three types of single-mode fibers coated with polyimide are used as the sensing fiber to measure the thickness of the coating layer at multiple temperatures. The experimental results show that the error in the coating layer thickness obtained is less than 0.05 μm. To the best of our knowledge, this is the first demonstration of non-destructive, high-precision monitoring of fiber coating thickness. © 2001-2012 IEEE.

关键词： Acoustic field measurement

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HarmonyNav: Robot Navigation in Human-Robot Coexistence Environment Considering Group and Pedestrian Intentions

HarmonyNav: Robot Navigation in Human-Robot Coexistence Envi...

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2024 Jiangsu Annual Conference on Automation, JACA 2024

作者： Wang, Yutao Fang, Fang Liu, Chenyang Fan, Tao Li, Yi The Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education Nanjing210096 China

ISBN: (纸本)9781837242689

Service robots must navigate efficiently and harmoniously in human-robot coexistence environments. Previous studies often treated pedestrians as independent entities, ignoring human intentions and leading to group intersections and performance issues. We introduce HarmonyNav, a crowd navigation algorithm that enhances human-robot harmony. Our method uses Human-Robot Interaction sub-networks to account for indirect group impacts on pedestrians and predicts pedestrian trajectories to infer intentions. Employing proximal policy optimization (PPO) in reinforcement learning, we develop a harmonious navigation policy. In simulation experiments, our results show improved navigation performance, reducing group intersections and individual intrusions. © The Institution of engineering & Technology 2024.

关键词： Reinforcement learning

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Collision-free and Effecient Robotic Grasping based on Hierarchical Reinforcement Learning

Collision-free and Effecient Robotic Grasping based on Hiera...

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2024 Jiangsu Annual Conference on Automation, JACA 2024

作者： Li, Yi Fang, Fang Liu, Chenyang Wang, Yutao Fan, Tao School of Automation Southeast University China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9781837242689

In the realm of robotics, grasping within unstructured environments has consistently been a challenging task. Direct grasping often results in failures due to collisions, and some pre-grasping maneuvers can eliminate the tightly stacked state between objects, thereby enhancing the success rate of grasping. In this paper, we opt for pushing as a pre-grasping maneuver. We propose a deep Q-network with a fused convolutional feature enhancement module to improve action value estimation, enhancing the robot’s pushing and grasping abilities. Additionally, a hierarchical reinforcement learning framework optimizes action sequences for better synergy between pushing and grasping. In simulated experiments, the model achieved a high grasp success rate with just a few hours of training, outperforming other methods in the literature. © The Institution of engineering & Technology 2024.

关键词： Adversarial machine learning

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Weak signal detection method based on novel composite multistable stochastic resonance

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Chinese Physics B 2023年第5期32卷 178-187页

作者：焦尚彬高蕊薛琼婕史佳强 Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048China School of Electronic and Electrical Engineering Baoji University of Arts and SciencesBaoji 721016China

The weak signal detection method based on stochastic resonance is usually used to extract and identify the weak characteristic signal submerged in strong noise by using the noise energy transfer *** propose a novel composite multistable stochastic-resonance(NCMSR)model combining the Gaussian potential model and an improved bistable *** with the traditional multistable stochastic resonance method,all the parameters in the novel model have no symmetry,the output signal-to-noise ratio can be optimized and the output amplitude can be improved by adjusting the system *** model retains the advantages of continuity and constraint of the Gaussian potential model and the advantages of the improved bistable model without output saturation,the NCMSR model has a higher utilization of *** the output signal-to-noise ratio as the index,weak periodic signal is detected based on the NCMSR model in Gaussian noise andαnoise environment respectively,and the detection effect is *** application of NCMSR to the actual detection of bearing fault signals can realize the fault detection of bearing inner race and outer *** outstanding advantages of this method in weak signal detection are verified,which provides a theoretical basis for industrial practical applications.

关键词： weak signal detection composite multistable stochastic resonance bearing fault detection

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