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Data-Driven Tracking Control of a Cushion Robot with Safe Autonomous Motion Considering Human-Machine Interaction Environment

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Journal of Dynamic systems, Measurement and Control 2025年第4期147卷 041003页

作者： Sun, Ping Shan, Rui Chang, Hongbin Wang, Shuoyu School of Artificial Intelligence Shenyang University of Technology Shenyang110870 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Intelligent Mechanical Systems Engineering Kochi University of Technology Kochi7828502 Japan

This study proposes a data-driven safety controller with velocity constraints for a cushion robot. We constructed a mathematical description of the human-machine interaction environment by decomposing the generalized input force and coefficient matrix in the dynamic model. We established a new equivalent data model, which considered various human-machine interaction environments using a pseudo-Jacobian matrix. A stochastic configuration network (SCN) estimation method for variations in the human-machine interaction environment was proposed, with hidden layer nodes added at random. We designed a safe autonomous navigation path and proposed a data-driven control method that limited the actual velocity while stabilizing the tracking error system. In addition, the desired motion velocity of the robot was designed. This approach has the advantage of ensuring safety at a specified velocity. We also demonstrated and validated the effectiveness of the proposed data-driven algorithm using simulation-based comparative analysis and an experimental study. Copyright © 2024 by ASME.

关键词： Stochastic systems

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Autonomous sortie scheduling for carrier aircraft fleet under towing mode

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Defence technology(防务技术) 2025年第1期43卷 1-12页

作者： Zhilong Deng Xuanbo Liu Yuqi Dou Xichao Su Haixu Li Lei Wang Xinwei Wang Department of Engineering Mechanics State Key Laboratory of Structural AnalysisOptimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian116024China School of Mathematical Sciences Dalian University of TechnologyDalian 116024China Naval Aviation University Yantai 264001China China State Shipbuilding Corporation Systems Engineering Research Institute Beijing 100094China Department of Automation School of Information Science and TechnologyTsinghua UniversityBeijing 100084China

Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft *** primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch *** this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource ***,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as *** analyzing the constraints in sortie scheduling,a mixed-integer planning model is *** particular,the constraint on preparing spot occupancy is improved to further enhance the sortie *** basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance *** efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is *** solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing *** animation of the simulation results is available at ***/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.

关键词： Carrier aircraft Autonomous sortie scheduling Resource allocation Collision-avoidance Hybrid flow-shop scheduling problem

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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Lu, Benxue Gao, Kaizhou Duan, Peiyong Slowik, Adam School of Computer Science Liaocheng University Liaocheng252000 China Macau Institute of Systems Engineering Macau University of Science and Technology Macau999078 China School of Information and Automation Engineering Qilu University of Technology Jinan250353 China Koszalin University of Technology Department of Electronics and Computer Science Koszalin75-453 Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots (Bi-HFSP_CS). The objectives are to minimize the makespan and total energy consumption. First, the Bi-HFSP_CS is formalized, followed by the establishment of a mathematical model. Second, enhanced version of the artificial bee colony (ABC) algorithms is proposed for tackling the Bi-HFSP_CS. Then, fourteen local search operators are employed to search for better solutions. Two different O-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration process. Finally, the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm, its three variants, and three effective algorithms in resolving 95 instances of 35 different problems. The experimental results and analysis showcase that the enhanced ABC algorithm combined with O-learning (QABC1) demonstrates as the top performer for solving concerned problems. This study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength, offering beneficial perspectives for exploration and research in relevant domains. © 2021 TUP.

关键词： Energy utilization

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A comparison study between Modbus and IEC 61850 MMS Protocols 33

A comparison study between Modbus and IEC 61850 MMS Protocol...

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33rd Southern African Universities Power Engineering Conference, SAUPEC 2025

作者： Johnson, Rayner Krishnamurthy, Senthil Mataifa, Haltor Esmail, Mohammed Cape Peninsula University of Technology Center for Substation Automation and Energy Management Systems Department of Electrical Electronic and Computer Engineering Cape Town7530 South Africa

ISBN: (纸本)9798331535162

The growing adoption of renewable energy primary plants has been seen globally. South Africa has seen a significant development in utility-scale photovoltaic (PV) farms in recent years due to the alleviated regulations stipulated in the latest Electricity Regulation Amendment Act. Connecting the PV plants to the power grid has also undergone a technical evolution by incorporating modern embedded computers known as Intelligent Electronic Devices (IEDs). The International Electrotechnical Committee (IEC) 61850 standard defines a suite of protocols used in modern power systems. This study compares the IEC 61850 Manufacturing Messaging Specification (MMS) with the well-known MODBUS protocol. The comparison is achieved by simulating a solar farm's Power Plant Controller (PPC). © 2025 IEEE.

关键词： Solar power plants

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Distributed Resilient Control of Cyber-Physical Multi-Agent systems Under Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 241-250页

作者： Xu, Yong Zhang, Wenyu Zhang, Yifang Wu, Zheng-Guang Beijing Institute of Technology School of Automation Beijing100081 China The University of Hong Kong Department of Mechanical Engineering Hong Kong Hong Kong Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China

This paper delves into the distributed resilient state estimation-based secure control in multi-agent systems under false-data injection attacks. Firstly, we propose a novel adaptive distributed output observer approach that is based on resilient outputs. The key feature of our proposed observer is its sole dependence on the coefficients of the minimal polynomial of the leader's system matrix, rather than on individual entries of the leader's system. Subsequently, to identify the target attacker, we assign both trust and confidence values to each agent and its neighbors, thereby enabling attack detection and localization. Furthermore, we propose a distributed finite-time resilient control strategy, grounded in trust and confidence, to safeguard against attackers while ensuring secure output estimation and maintaining a bounded upper limit for finite-time convergence. Additionally, we introduce a piecewise function-based approximation method to achieve a less conservative upper bound for the convergence time, compared to traditional methods. Finally, we provide a numerical example to demonstrate the effectiveness of our proposed theoretical analysis. © 2023 IEEE.

关键词： Cyber attacks

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Neural Data Fusion Enhanced PD Control for Precision Drone Landing in Synthetic Environments

Neural Data Fusion Enhanced PD Control for Precision Drone L...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Altinses, Diyar Torres, David Orlando Salazar Lier, Stefan Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and learning systems Soest Germany South Westphalia University of Applied Sciences Department of Logistics and Supply Chain Management Meschede Germany

ISBN: (纸本)9798331533892

Unmanned aerial vehicles are increasingly used in applications like surveillance, mapping, and delivery, where precise and safe landings are crucial, especially on small, designated platforms in multimodal systems. However, achieving consistent landing accuracy is challenging due to sensor inaccuracies, environmental disturbances, and dynamic conditions. This paper introduces a synthetic drone simulation environment to test landing scenarios and control strategies. We propose a control framework combining a classical PD controller with a neural network-based data fusion model that corrects sensor noise, particularly in GPS and UWB data, to improve landing precision. Simulation results demonstrate that our approach enhances landing accuracy and robustness compared to traditional methods. © 2025 IEEE.

关键词： Drones

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A Novel Study on Modelling and Adaptive Optimal Control of a Tubular Reactor Based on Gaussian Processes 19th

A Novel Study on Modelling and Adaptive Optimal Control of a...

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19th International Conference on Artificial Intelligence: Methodology, systems, and Applications, AIMSA 2024

作者： Grancharova, Alexandra Xie, Junhong Kocijan, Juš Department of Industrial Automation University of Chemical Technology and Metallurgy Sofia Sofia Bulgaria Department of Systems and Control Jozef Stefan Institute Ljubljana Slovenia University of Nova Gorica Rožna Dolina Slovenia

ISBN: (纸本)9783031815416

Number of engineering systems can be characterized as complex since they have a dynamic and nonlinear behaviour incorporating a stochastic uncertainty. On the other hand, as a machine learning method, Gaussian processes (GP) provide a practical, probabilistic approach to learning in kernel machines. This makes them very suitable for obtaining probabilistic, nonparametric black-box models of stochastic nonlinear dynamic systems. In this paper, a novel study on the modeling and adaptive optimal control of a tubular reactor is made by using Gaussian processes. Such reactor is a typical example of a nonlinear distributed parameters system, whose first-principles dynamic models consists of partial differential equations. The purpose is to obtain a nonlinear autoregressive models with exogenous input (NARX) of the output concentration and temperature of the reactor by applying a GP modeling approach. The identified surrogate models are then used to design an adaptive model predictive controller to achieve optimal performance of the reactor despite of the stochastic changes in the feed temperature. The performance of the adaptive MPC based on GP models is studied by simulation experiments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Predictive control systems

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Stability and bifurcation analysis of delayed tri-trophic food chain model in poisoned environment with fear effect and additional food

Stability and bifurcation analysis of delayed tri-trophic fo...

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作者： Ramasamy, Sivasamy Banjerdpongchai, David Park, PooGyeon Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Republic of

One of the most important goals of theoretical ecologists is to find a strategy for controlling the chaos in ecological models to maintain healthy ecosystems. We investigate the influence of fear and the supply of additional food to predators in a delayed tri-trophic food chain model, where interference among the species is determined by Holling type-II functional response. The proposed model incorporates the fact that the growth rates of prey and middle predators are decreased due to the fear of middle and special predators, respectively. The additional food is supplied to both predators by either nature or external agencies. Furthermore, the poisoned prey in the predator's environment is taken into account. In the proposed model, we use two time delays: one for the growth term of the middle predator and another for the gestation delay of the special predator. We determine the conditions for the existence of ecologically feasible equilibrium points and their local and global stability. In addition, we establish the conditions for the existence of Hopf bifurcation around interior equilibrium to seek periodic behavior in nondelayed and delayed models. Numerical investigations are performed to justify the proposed theoretical findings through phase portraits, time series of solutions, and bifurcation diagrams. We observe that the chaotic dynamics of a tri-trophic food chain model can be controlled by the proper choice of the fear effect and by supplying additional food parameters. As a result, our findings provide more ecological insights into the dynamics of the delayed tri-trophic food chain model with the fear effect and additional food supply. © 2023 John Wiley & Sons, Ltd.

关键词： Hopf bifurcation

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