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engineering 2024年第4期35卷 74-85页

作者： Yanjie Chen Yangning Wu Limin Lan Hang Zhong Zhiqiang Miao Hui Zhang Yaonan Wang School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Computer Science Aberystwyth UniversityAberystwyth SY233DBUK School of Robotics Hunan UniversityChangsha 410082China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle Visual servoing Velocity observer Target tracking

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Koopman Operator Learning control of the Piezoelectric Actuator 42

Koopman Operator Learning Control of the Piezoelectric Actua...

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42nd Chinese control Conference, CCC 2023

作者： Huang, Xiang Zhang, Hai-Tao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

The piezoelectric actuator is one kind of device that can drive nanoscale motion. However, the nonlinear hysteresis effect induced by its natural material greatly degrades its positioning accuracy. To handle this challenging issue, this work develops a Koopman model predict control (Koopman- MPC) framework for the piezoelectric actuator. Specifically, the Koopman operator theory is adapted for modeling the piezoelectric actuator dynamics. A simple yet powerful linear model spanned in a high-dimensional space is thus constructed to characterize the hysteresis dynamics. Subsequently, upon the established Koopman model, an MPC scheme is put forward for tracking control of piezoelectric actuators. Therein, by sustained optimizing a cost function containing future outputs and control increments, the control input is obtained. Moreover, extensive tracking simulations are carried out on a simulated piezoelectric actuator for verifying the feasibility and effectiveness of the Koopman- Mpc scheme. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Model predictive control

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Aggregation of Magnetic Particles in a Triangular Oscillating Magnetic Field

Aggregation of Magnetic Particles in a Triangular Oscillatin...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Zhao, Peiran Yan, Liang Gao, Xiaoshan Bu, Suwan Gerada, Chris School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Ningbo Institute of Technology Beihang University Ningbo315800 China Power Electronics Machines and Control Group University of Nottingham NottinghamNG7 2RD United Kingdom

ISBN: (纸本)9789811966125

The existing vascular microrobots are mainly individual robots because of the limitations of size. Aiming at the existing problems of vascular swarm robot, this paper focuses on the aggregation of magnetic particles. Through theoretical analysis, the formula of magnetic interaction force between particles is derived. Then the mechanism is verified and a ribbon-like swarm is generated by the numerical simulation. The simulation results show that the main factors affect the aggregation are the amplitude ratio γ, the frequency f and the constant component B0. γ and f are positively and negatively correlated with the aspect ratio of swarm respectively. The constant component affects the direction of swarm. In conclusion, the magnetic interaction force is the fundamental reason for the aggregation of magnetic particles and the particles can aggregate into swarm in a triangular oscillating field. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Agglomeration

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Real-Time and Variable-Length Prediction for Drogue Motion: A Deep Learning Method

Real-Time and Variable-Length Prediction for Drogue Motion: ...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Liu, Yiheng Wang, Honglun Fan, Jiaxuan Su, Zikang School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Shenyuan Honors College of Beihang University Beijing100191 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9789811581540

During the autonomous aerial refueling (AAR) docking phase, the drogue is disturbed by multiple perturbations and will swing quickly, which increases the docking difficult for the receiver. If the drogue motion can be predicted precisely in real-time, the preview information of the drogue motion could be used in the receiver control system and the docking success rate would be improved. Most studies use the complicated hose-drogue assembly model to predict the drogue motion but the states of the hose cannot be sensed and the real-time requirement is also unsatisfied. Therefore, a deep learning based real-time and variable-length prediction method for the drogue is proposed in this paper. A gated recurrent unit model is used to extract the feature from the time series of the drogue motion. And the prediction step size is also used as an input of the model to realize the variable-length prediction. Finally, the effectiveness and correctness of the proposed drogue motion prediction method is demonstrated by simulations. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 264-280页

作者： Liu, Ruonan Zhang, Quanhu Han, Te Yang, Boyuan Zhang, Weidong Yin, Shen Zhou, Donghua Shanghai Jiao Tong University Department of Automation Shanghai200240 China Tianjin University College of Intelligence and Computing Tianjin300350 China Beijing Institute of Technology Center for Energy and Environmental Policy Research Beijing100081 China Beijing Institute of Technology School of Management Beijing100081 China Beijing Laboratory for System Engineering of Carbon Neutrality Beijing100081 China Nanjing University Center for Advanced Control and Smart Operations Suzhou215163 China Hainan University School of Information and Communication Engineering Haikou570228 China Shanghai Jiaotong University Department of Automation Shanghai200240 China Norwegian University of Science and Technology Trondheim7491 Norway

Industrial Cyber-Physical Systems (ICPS) integrating disciplines such as computer science, communication technology, and engineering, have become a crucial component of modern manufacturing and industry. However, ICPS faces numerous challenges during long-term operation, including equipment faults, performance degradation, and security threats, etc. To achieve efficient maintenance and management, prognostics and health management (PHM) has been widely applied in the critical tasks of ICPS such as fault prediction, health monitoring, and maintenance decision-making. The emergence of large-scale foundation models (LFMs) like BERT and GPT marks a significant advancement in artificial intelligence (AI) technology, demonstrating substantial application potential in multiple fields. The accumulation of AI technology, rapid development of LFMs, and the abundance of industrial data and industrial process knowledge provide the foundational conditions for the construction and advancement of industrial LFMs. However, there is currently a lack of consensus on applying LFMs of PHM in ICPS, necessitating a systematic review and roadmap to clarify future development directions. To bridge this gap, this survey provides a comprehensive survey and understanding of the recent advances in LFMs of PHM in ICPS. It provides valuable references for decision makers and researchers in the industry, and helps to further improve the reliability, availability and safety of ICPS. © 2023 IEEE.

关键词： Cyber Physical System

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Lexicographic Multi-objective Order Picking Optimization for Robotic Mobile Fulfillment Systems

Lexicographic Multi-objective Order Picking Optimization for...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Hanying Zhao, Ziyan Liang, Jiaqi Li, Xingyang Liu, Shixin College of Information Science and Engineering Northeastern University Shenyang110819 China Foshan Graduate School of Innovation Northeastern University Foshan China Polytechnique Montréal Département de Mathématiques et Génie Industriel MontréalQCH3T 1J4 Canada Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Shenyang110819 China School of Control Engineering/School Northeastern University at Qinhuangdao Hebei Qinhuangdao066004 China

ISBN: (纸本)9781665410205

In light of advancements in artificial intelligence, the Internet of Things, and mechatronics, robots are increasingly integrated into e-commerce warehouses to enable smart order picking solutions and foster intelligent automation. A robot-assisted order picking process revolutionizes the traditional labor-intensive person-to-goods order picking technology, leading to a goods-to-person (G2P) smart warehouse. Within it, robots transport pods to predefined picking stations, where human pickers retrieve the requested goods from these pods to fulfill customer orders. The allocation of pods to robots and the scheduling of picking operations are key optimization issues in G2P order picking systems. Although they play a key role in improving operational efficiency, existing research has paid limited attention to their joint optimization. This study considers a lexicographic multi-objective optimization problem to shorten the order picking cycles under the premise of optimizing the picking efficiency evaluated by makespan. We build a mixed integer program for the newly proposed problem and develop a matheuristic algorithm by integrating a commodity-order model into a metaheuristic algorithm to solve it. Experimental results show that the proposed method can significantly shorten the total order picking cycles while keeping the minimum makespan. It outperforms a recent state-of-the-art algorithm. This work emphasizes the importance of joint optimization within G2P smart warehouses and reveals the high potential of the proposed method to be used in practice. © 2024 IEEE.

关键词： Integer programming

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A Prototypical Networks-based Multi-task Model for Few-shot Fault Diagnosis 11

A Prototypical Networks-based Multi-task Model for Few-shot ...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Huo, Zhihao Yang, Xiaoyu Yang, Tao Fan, Huijin Su, Housheng Zheng, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology China-Belt and Road Joint Laboratory On Measurement and Control Technology Wuhan430074 China School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Due to the powerful classification performance, Prototypical Networks is widely used in few-shot fault diagnosis. However, insufficient feature extraction ability and negligence of inter-class distance in the Prototypical Networks may lead to the decrease of diagnosis accuracy. To solve the above problems, a Prototypical Networks-based multi-task model (PNBMTM) is proposed in this paper. Firstly, a convolutional autoencoder is built based on the feature extraction network of the Prototypical Networks, in which the optimization of the reconstruction loss is taken as a new task to obtain more efficient features. Secondly, a channel attention module (CAM) is designed to be applied to each layer of the encoder, which focuses on important features and suppresses useless features. Thirdly, relative distance loss is introduced while considering the effect of intra-class distance and inter-class distance to make inter-class distance larger and intra-class distance smaller, which helps to improve the quality of the features. And the optimization of relative distance loss is consequently taken as a new task to constitute a multi-task fault diagnosis model. Finally, a mechanical fault simulation experiment platform is built to verify the validity of the proposed model. The experimental results show that the proposed model has superior performance compared with other models. © 2022 IEEE.

关键词： Failure analysis

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Decentralized Open-Loop Strategies of Linear Quadratic Mean Field Games: From Finite to Infinite Population

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IFAC-PapersOnLine 2023年第2期56卷 894-898页

作者： Yong Liang Bing-Chang Wang Huanshui Zhang School of Control Science and Engineering Shandong University Jinan P.R. China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao P.R. China

This paper studies the decentralized open-loop asymptotic Nash equilibria for linear quadratic mean field games following the direct approach. In the first step of the direct approach, the necessary and sufficient condition for the existence of centralized open-loop Nash equilibria is obtained by using the variational analysis. The Nash equilibria are characterized by an adapted solution to a system of forward-backward stochastic differential equations. A feedback representation of the open-loop Nash equilibrium is obtained by using the solution to a system of Riccati equations. In the second step of the direct approach, the decentralized asymptotic Nash equilibria are designed by considering the limit of the centralized Nash equilibria with a standard Riccati equation and a non-symmetric Riccati equation. The results show that the decentralized open-loop asymptotic Nash equilibria coincide with the feedback solutions which are designed by the fixed-point approach, provided both exist.

关键词： mean field games Nash equilibria linear quadratic differential games decentralized strategies asymptotic strategies

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Adaptive Critic Attitude Learning control for Hypersonic Morphing Vehicles without Backstepping

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Li, Shihong Shao, Xingling Wang, Hongyu Liu, Jun Zhang, Qingzhen School of Instrument and Electronics North University of China Taiyuan030051 China State Key Laboratory of Instrumentation Science & Dynamic Measurement Taiyuan030051 China School of Electrical and Control Engineering North University of China Taiyuan030051 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

This paper proposes an adaptive critic attitude learning control for hypersonic morphing vehicles (HMVs) under large uncertainties and deformation. Firstly, to remove the recursive design complexity caused by backstepping, an attitude-morphing coupled model in Brunovsky form is created by introducing a coordinate transformation. Secondly, a disturbance compensation controller is designed without using backstepping, wherein a resource-saving yet efficient unknown system dynamics estimator (USDE) is introduced to estimate the lumped uncertainties by a simple filtering operation. Then, a near optimal regulator capable of online learning is developed under a critic-only adaptive © 2022 IEEE.

关键词： Backstepping

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Secure Data Reconstruction: A Direct Data-Driven Approach

arXiv

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arXiv 2025年

作者： Yan, Jiaqi Markovsky, Ivan Lygeros, John School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Barcelona Spain Barcelona Spain Automatic Control Laboratory ETH Zurich Switzerland

This paper addresses the problem of secure data reconstruction for unknown systems, where data collected from the system are susceptible to malicious manipulation. We aim to recover the real trajectory without prior knowledge of the system model. To achieve this, a behavioral language is used to represent the system, describing it using input/output trajectories instead of state-space models. We consider two attack scenarios. In the first scenario, up to k entries of the collected data are malicious. On the other hand, the second scenario assumes that at most k channels from sensors or actuators can be compromised, implying that any data collected from these channels might be falsified. For both scenarios, we formulate the trajectory recovery problem as an optimization problem and introduce sufficient conditions to ensure successful recovery of the true data. Since finding exact solutions to these problems can be computationally inefficient, we further approximate them using an 1-norm and group Least Absolute Shrinkage and Selection Operator (LASSO). We demonstrate that under certain conditions, these approximation problems also find the true trajectory while maintaining low computation complexity. Finally, we extend the proposed algorithms to noisy data. By reconstructing the secure trajectory, this work serves as a safeguard mechanism for subsequent data-driven control methods. © 2025, CC BY.

关键词： Spatio-temporal data

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