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Output Tracking Control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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A Pilot Usability Study of a Humanoid Avatar to Assist Therapists of ASD Children 15th

A Pilot Usability Study of a Humanoid Avatar to Assist Th...

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15th International Conference on Social robotics, ICSR 2023

作者： Fournier, Carole Michelon, Cécile Tanguy, Arnaud Audoyer, Paul Granit, Véronique Baghdadli, Amaria Kheddar, Abderrahmane CNRS -University of Montpellier Laboratory of Computer Science Robotics and Microelectronics of Montpellier Montpellier France Centre Ressources Autisme Languedoc-Roussillon and Center of Excellence for Autism and Neurodevelopmental Disorders CHU Montpellier Montpellier France Faculty of Medicine University of Montpellier Montpellier France CNRS-AIST Joint Robotics Laboratory IRL3218 Tsukuba Japan

ISBN: (纸本)9789819987146

In this article, we report on a pilot study consisting of an evaluation of the usability satisfaction and effectiveness of a preliminary telerobotic system to assist therapists of children with ASD. Unlike existing pre-programmed robotic systems, our solution beamed therapists in a humanoid robot (Pepper) to reproduce in real-time the therapist’s gestures, speech and visual feedback aiming to embody the therapist in a humanoid robot avatar and be able to perform activities during an ESDM intervention. Evaluations of our system, used by eleven therapists in internal tests during mock session without children, are reported and suggest that future use in real therapy sessions with ASD children can begin. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anthropomorphic robots

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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Cost-Effective Federated Learning-Based Approach for SINR Prediction in Cellular-Connected UAVs

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Journal of Communications and Information Networks 2024年第4期9卷 374-389页

作者： Ibrahem Mouhamad Dushantha Nalin K.Jayakody Dejan Vukobratovic School of Computer Science and Robotics National Research Tomsk Polytechnic UniversityTomsk 634050Russia COPELABS Lus´ofona UniversityLisbon 1749-024Portugal Faculty of Technical Sciences University of Novi SadNovi Sad 21000Serbia

This study introduces a novel approach to empower cellular-connected unmanned aerial vehicles(UAVs)in predicting signal *** proposed prediction model leverages data collected by the UAVs,addressing privacy concerns and ensuring effectiveness,while taking into account the constraints of UAVs.A unique three-step approach is proposed,which integrates a detailed physical ray-tracing(RT)method,deep learning,and federated learning(FL)for continuous learning and field adaptation.A dual input feature fusion convolutional neural network(DIFF-CNN)model is proposed,which is pretrained on RT data and fine-tuned using data collected by the UAVs via *** proposed model demonstrates superior performance and robustness to data sparsity compared to traditional machine learning ***,the model achieves a root mean squared error of 0.837 dB and an R-squared of 97.7% for signal-to-interference-plus-noise ratio(SINR)prediction after the fine-tuning step in the fixed-altitude scenario,but performance drops with uniform altitude distribution,highlighting the impact of flying height on *** research indicates that the proposed approach can enhance performance while reducing training rounds by 35% to 90%,thus mitigating FL *** research could explore efficiency gains by using different pretrained models tailored to specific flying heights.

关键词： federated learning unmanned aerial vehicle convolutional neural network ray-tracing signal-tointerference-plus-noise ratio

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MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time Systems

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ACM Transactions on Design Automation of Electronic Systems 2025年第3期30卷 1-29页

作者： Ahmadi-Pour, Sallar Saha, Sangeet Mcdonald-Maier, Klaus Drechsler, Rolf Institute of Computer Science University of Bremen Faculty 3 Mathematics and Computer Science Bremen Bremen Germany Robotics and Embedded Systems Research Group University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Cyber-Physical Systems DFKI Rheinland-Pfalz Kaiserslautern Germany

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today's Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this article, we consider the challenge of scheduling an RT application modeled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTG on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6% and area usage reductions of 46.3% are possible compared to full Software (SW) and HW execution, respectively. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Integer programming

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RL-Based Control of UAS Subject to Significant Disturbance

RL-Based Control of UAS Subject to Significant Disturbance

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Kousheek Chakraborty Thijs Hof Ayham Alharbat Abeje Mersha Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede The Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance.

关键词： Training Attitude control Simulation Force Reinforcement learning Autonomous aerial vehicles Stability analysis Maintenance Dynamical systems Logistics

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A Sentiment Analysis Study of Twitter Users' Reactions to the COVID-19 Vaccine 14

A Sentiment Analysis Study of Twitter Users' Reactions to th...

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14th International Conference on Information and Communication Systems, ICICS 2023

作者： Alkhushayni, Suboh Alomari, Zakaria Al-Zaleq, Du'a Yarmouk University Faculty of Information Technology and Computer Science Dept. Information Systems Irbid Jordan New York Institute of Technology Dept. Computer Science VancouverBC Canada Al-Ahliyya Amman University Dept. Robotics and Artificial Intelligence Engineering Amman Jordan

ISBN: (纸本)9798350307870

This study investigates public attitudes towards the COVID-19 vaccine through Twitter data analysis. Using the Twitter API, tweets were collected, preprocessed, and labeled. Features were extracted using the Bag of Words representation, and sentiment analysis was conducted using Text Blob and Vader. Machine learning and deep learning models were trained and tested, revealing that deep learning models achieved the highest accuracy and F1 score. The research underscores the efficacy of machine learning and deep learning in analyzing COVID-19 vaccine-related tweets, shedding light on factors influencing vaccine resistance. These insights are crucial for pharmaceutical companies and public health officials, enabling them to address barriers to vaccine acceptance and enhance the societal benefits of widespread vaccination, contributing to the pandemic's resolution. © 2023 IEEE.

关键词： COVID-19

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RL-based Control of UAS Subject to Significant Disturbance

arXiv

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arXiv 2025年

作者： Chakraborty, Kousheek Hof, Thijs Alharbat, Ayham Mersha, Abeje Smart Mechatronics and Robotics Research Group Saxion University of Applied Science Enschede Netherlands Robotics and Mechatronics Research Group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede Netherlands

关键词： Reinforcement learning

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Onboard Perception-Assisted High Fidelity Simulation Framework for Autonomous Planetary Soft-Landing

Onboard Perception-Assisted High Fidelity Simulation Framewo...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Alibekov, Ulugbek Banerjee, Avijit Satpute, Sumeet Gajanan Nikolakopoulos, George ERASMUS MUNDUS JOINT MASTER in Intelligent Field Robotic Systems Faculty of Informatics Eötvös Loránd University Budapest Hungary Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents an onboard perception-assisted high-fidelity simulation framework for autonomous planetary soft-landing, enabling visual information processing tightly integrated with advanced onboard guidance system in a realistic simulation environments. Utilizing the open source Unity game engine, a physics-based simulation toolbox integrated with the Robotic Operating System (ROS2), we effectively emulate the motion of spacecraft approaching planetary terrain. The simulation environment features a realistic 3D terrain model and a lander spacecraft equipped with onboard camera to effectively capture detailed local terrain images. The simulation engine accurately captures the spacecraft’s 6-degrees of freedom motion, seamlessly integrating space craft orientation with the associated camera feed while incorporating various illumination conditions using a light source representing the Sun. To demonstrate the efficacy of the framework, we implement a recently developed attitude-constrained minimum jerk guidance algorithm to emulate spacecraft motion. Concurrently, the visual feed from the onboard RGB camera is processed through a gradient extraction-based perception system, providing the visual odometer. This system identifies provably safe landing sites, effectively avoiding potential risks of landing on uneven terrain. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Planetary landers

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Detection of Gender and Age Category from Speech

Detection of Gender and Age Category from Speech

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2023 World Symposium on Digital Intelligence for Systems and Machines, DISA 2023

作者： Haluška, Renát Popovič, Michal Pleva, Matus Frohman, Matthew Department of Electronics and Multimedia Communications Faculty of Electrical Engineering and Informatics Technical University Kosice Kosice Slovakia Perception and Robotics Group Department of computer science University of Maryland College ParkMD United States

ISBN: (纸本)9798350343533

The main goal of this paper was to find out how the gender and age group acoustical models behave on audio data that is in no way related to the data corpora used to train and evaluate the models. These models could be used for early predator detection in minors’ audio chats. The datasets used for training were mainly composed of English speakers. One part was made up of speakers whose native language is English and the rest use English as a secondary language. © 2023 IEEE.

关键词： Electronics industry

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