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42nd Chinese control Conference, CCC 2023

作者： Zhang, Jinwei Su, Jinya Yi, Dewei Yang, Jun Ministry of Education The School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

ISBN: (纸本)9789887581543

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Computational efficiency

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Quantum Robust control for Time-Varying Noises Based on Adversarial Learning

Quantum Robust Control for Time-Varying Noises Based on Adve...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haotian Ji Sen Kuang Daoyi Dong Chunlin Chen Department of Automation University of Science and Techonology of China Hefei China School of Engineering Australian National University Canberra Australia Department of Control and Systems Engineering School of Management and Engineering Nanjing University Nanjing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Time-varying noises are one of the reasons that make it difficult for quantum systems to complete control tasks. How to quantify the influence of time-varying noises on control results and how to design a control law that can resist time-varying noises are two important problems. In this paper, the adversarial learning is introduced into quantum control and the loss function under the worst-case noise is used as a way to quantify the impact of time-varying noises on control performance. We utilize the Gradient Ascent Pulse engineering (GRAPE) technique to search the worst-case noise and meanwhile offer a strategy to improve the robustness of the control law. Simulation experiments on a two-qubit system and a four-qubit system show that the found noises indeed can act as worst-case noises. Furthermore, the optimized control laws demonstrate good robustness to time-varying noises in state preparation tasks.

关键词： Robust control Quantum system Noise Pipelines control systems Robustness Adversarial machine learning Superconducting device noise Trajectory Time-varying systems

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Adaptive Decentralized Asymptotic Tracking control for Large-Scale Nonlinear Systems With Unknown Strong Interconnections

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 173-186页

作者： Ben Niu Jidong Liu Ding Wang Xudong Zhao Huanqing Wang College of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Artificial Intelligence Nankai UniversityTianjin 300071China Faculty of Information Technology Beijing University of TechnologyBeijing 100124 State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Control Science and Engineering Dalian University of TechnologyDalian 116023China Department of Mathematical Science and Automatization Institute Bohai UniversityJinzhou 121000China

An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections,unknown time-varying parameters,and ***,by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time,all extra signals in the framework of decentralized control are filtered out,thereby removing all additional assumptions imposed on the interconnec-tions,such as upper bounding functions and matching ***,by introducing two integral bounded functions,asymptotic tracking control is ***,the nonlinear filters with the compensation terms are introduced to circumvent the issue of“explosion of complexity”.It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero *** the end,a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

关键词： Adaptive decentralized control asymptotic tracking control large-scale nonlinear systems unknown strong interconnections

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Fault diagnosis of control moment gyroscope based on a new CNN scheme using attention-enhanced convolutional block

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Science China(Technological Sciences) 2022年第11期65卷 2605-2616页

作者： ZHAO HaoTian LIU Ming SUN YiYong CHEN Zhang DUAN GuangRen CAO XiBin Research Center of Satellite Technology School of AstronauticsHarbin Institute of TechnologyHarbin 150001China School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Department of Automation TsinghuaUniversityBeijing 100084China Control Theory and Guidance Technology School of AstronauticsHarbin Institute of TechnologyHarbin150001China

control moment gyroscope(CMG)is a typical attitude control system component for satellites and mobile robots,and the online fault diagnosis of CMG is crucial because it determines the stability and accuracy of the attitude control *** paper develops a data-driven CMG fault diagnosis scheme based on a new CNN *** this design,seven types of fault signals are converted into spectrum datasets through short-time Fourier transformation(STFT),and a new CNN network scheme called AECB-CNN is proposed based on attention-enhanced convolutional blocks(AECB).AECB-CNN can achieve high training accuracy for the CMG fault diagnosis datasets under different sliding window ***,simulation results indicate that the proposed fault diagnosis method can achieve an accuracy of nearly 95%in 1.28 s and 100%in 2.56 s,respectively.

关键词： fault diagnosis control moment gyroscope CNN scheme attention-enhanced convolutional block

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Enhancing Pain Level Assessment in Post-Surgery Patients Using Artificial Intelligence Algorithms

Enhancing Pain Level Assessment in Post-Surgery Patients Usi...

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European control Conference (ECC)

作者： G. Ben Othman E. Yumuk D. Copot A. R. Ynineb H. Farbakhsh I. R. Birs C. I. Muresan R. De Keyser I. Chihi C. M. Ionescu M. Neckebroek Department of Electromechanics Systems and Metal Engineering Research Group on Dynamical Systems and Control Ghent University Gent Belgium Department of Control and Automation Engineering Istanbul Technical University Maslak Istanbul Turkey Department of Automation Technical University of Cluj-Napoca Cluj-Napoca Romania Department Of Engineering Faculty of Science Technology and Medicine University of Luxembourg Kirchberg Luxembourg Department of Anesthesia Ghent University Hospital Ghent Belgium

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

control performance decreases significantly in the presence of uncertainty in variable availability, measurement noise, or instrumentation failure. In cluttered environments such as the Post Anesthesia Care Unit (PACU), clinical measures are often influenced by noise and artifacts. An important component in post-operative treatment is the assessment and management of pain levels. However, reliable information is critical for clinically relevant results and improved patient outcomes. From a control engineering point of view, variables are often estimated and interpolated to allow a suitable flow of feedback information to control loops for the optimization of drug dosages. In this context, Artificial Intelligence (AI) stands as a promising tool to augment pain level assessment. This study introduces and compares two AI-based approaches for predicting continuous Numerical Rating Scales (NRS) based on heart rate (HR) data. The first approach uses polynomial regression, lasso regression, and ridge regression, while the second employs an LSTM model. Notably, the AI prediction model, independent of traditional interpolation techniques, outperforms the approach relying on interpolation. The proposed AI-based method holds promise for continuous estimation and can serve as an estimator for model-based control. This proof of concept study underscores the potential of AI tools to enhance pain level assessment.

关键词： Heart rate Interpolation Uncertainty Pain Noise Numerical models Noise measurement

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Evaluating the Influence of Object Detection Models and Dataset Selection on Traffic Accident Prediction Performance

Evaluating the Influence of Object Detection Models and Data...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Chen, Wen-Hui Huang, Yu-Hung Lin, Yu-Chen Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan

ISBN: (纸本)9798331521165

Traffic accidents pose significant social and economic challenges, making effective accident prediction systems essential. Traditional machine learning methods often struggle to capture the complex, nonlinear patterns in visual accident data. Recent advancements suggest that deep learning offers a promising solution to this challenge. These vision-based systems rely on object detection as a foundational step, identifying key entities in traffic scenes captured by cameras to improve prediction accuracy. Despite its importance, the impact of object detection accuracy on overall accident prediction effectiveness remains underexamined. This study investigates this relationship through cross-dataset analysis. Experimental results show that object detection accuracy directly impacts accident prediction performance, with training dataset choice playing a critical role. Enhancing model generalization is essential to stabilizing accident prediction systems. © 2025 IEEE.

关键词： Highway accidents

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A Robust Path Planning of Messenger UAV for Air-Ground Coordination Under Road Network Constraints

A Robust Path Planning of Messenger UAV for Air-Ground Coord...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Wang, Yuyang Xin, Bin Ding, Yulong Yin, Zhen He, Bin School of Automation Beijing Institute of Technology Beijing100081 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Tongji University Department of Control Science and Engineering Shanghai China

ISBN: (纸本)9781665481090

This paper addresses the path planning problem for unmanned aerial vehicle (UAV), where a UAV serves as a messenger to periodically traverse over road-constrained ground vehicles (GVs) to relay information. The GVs may not move on the road network with the velocities agreed with the UAV due to their movement uncertainties. The UAV path planning problem can be formulated as the Dynamic Dubins Traveling Salesman Problem with Uncertain Neighborhood (DDTSPUN). To address the uncertainty in GVs motion, we define robust neighborhoods and corridors and prove that the UAV visiting these areas can revisit GVs reliably, thus transforming DDTSPUN into a Revisit-time-constrained Dubins Traveling Salesman Problem with Neighborhood (RDTSPN). To address the RDTSPN, we propose an online heuristic path planning algorithm. This algorithm incorporates a gradient-based method for visiting robust neighborhoods and a sampling-based search strategy along robust corridors to obtain the Dubins paths of UAV that achieve the robust visit for each GV. Finally, computational experiments demonstrate its advantage over other benchmark algorithms in obtaining the shortest path satisfying UAV curvature constraints, and ensuring robust access to all GVs. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Quantification of valve stiction in control loops using adaptive differential evolution algorithm

Quantification of valve stiction in control loops using adap...

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2024 China automation Congress, CAC 2024

作者： Yang, Dong Lang, Xun Wu, Jiande Yu, Wanke Liu, Songhua Xie, Lei Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Automation China University of Geosciences Wuhan China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

Accurately and promptly quantifying stiction is crucial for valve maintenance in industrial settings and ensuring the stability of control loop performance. However, current Hammerstein-based stiction quantification methods overlook valve dynamics, potentially leading to inaccurate quantification. Therefore, this paper proposes a novel stiction quantification method based on an adaptive differential evolution algorithm. In addition to considering the impact of valve dynamics on stiction, the proposed method demonstrates a certain level of robustness against the interference of unknown noise. Finally, the effectiveness of the proposed method is demonstrated through a series of simulations and industrial case studies. © 2024 IEEE.

关键词： Adaptive algorithms

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Influence of the Kalman Filtering Initial Setting on SINS Initial Alignment and an Improved Method for the Alignment of Large Misalignment Angles 30

Influence of the Kalman Filtering Initial Setting on SINS In...

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30th Saint Petersburg International Conference on Integrated Navigation Systems, ICINS 2023

作者： Ning, Xiaoge Huang, Jixun Li, Jianxun Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Department of Automation Shanghai China Beijing Aerospace Times Optical-Electronic Co. Ltd Beijing China Beijing Institute of Aerospace Control Devices Beijing China

ISBN: (纸本)9785919950950

This paper analyzes the instability of the filtering system of the initial alignment under a quasi-static base and points out the great influence of Kalman filtering initial value setting on the filtering results. This is instructive for many conditions under initial alignment and integrated navigation. Meanwhile, an improved method for the alignment of large misalignment angles is proposed by replacing the body-frame bias state with the geographic-frame bias state. Simulation experiments show that it can improve the horizontal alignment accuracy by 0.1°and vertical alignment accuracy by 1.6°. © 2023 Concern CSRI Elektropribor, JSC.

关键词： Alignment

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Experimental Design of Lower-limb Movement Recognition Based on Support Vector Machine 41

Experimental Design of Lower-limb Movement Recognition Based...

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第41届中国控制会议

作者： Wangyang Ge Juan Zhao Feng Wang Chi Xu Zhaohui Yang Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Rehabilitation Union HospitalTongji Medical CollegeHuazhong Univ.Science & Technology School of Engineering Tokyo University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

As a rehabilitation aid,the lower extremity rehabilitation robot usually works together with the patient during the rehabilitation training process,and achieves therapeutic effects by assisting the patient to complete movements within a certain trajectory for *** realize the active training of the rehabilitation robot,the rehabilitation robot needs to be able to recognize the patient's movement intention and cooperate with *** signals contain rich neuromuscular movement information and are often used to extract patients' movement *** this paper,using the principle of human lower extremity force generation,we designed an experiment for lower extremity action recognition of sEMG *** extracting the composition of feature vectors through time-domain feature values,we designed an SVM classifier to achieve the classification of five common lower extremity actions and obtained an average accuracy rate of 90.66% through the experiment,which verified the effectiveness of the experimental design.

关键词： sEMG signal SVM movement recognition lower-limb

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