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23rd International Conference on control, Automation and Systems, ICCAS 2023

作者： Kim, Min Seong Lee, Jeon Hyeok Kim, Taek Lim Park, Tae-Hyoung Chungbuk National University Department of Control and Robot Engineering Cheongju28644 Korea Republic of Chungbuk National University Department of Intelligent Systems and Robotics Cheongju28644 Korea Republic of

ISBN: (纸本)9788993215274

Local Trajectory Planning plays a pivotal role in reducing lap times during a race. It must take into account not only obstacles but also the hardware limitations of the vehicle and the performance of the controller. Moreover, in a high-speed racing competition like F1, surrounding vehicles exhibit highly dynamic movements, necessitating motion prediction for safety. However, existing studies have demonstrated good performance in environments with static objects or when avoiding racing vehicles in straight-line sections, but accidents occur in environments with large numbers of vehicles and high curvature. To address these issues, this paper proposes an algorithm that ensures stability even in complex environments by considering the uncertainty of motion prediction for multiple vehicles. By considering the uncertainty of vehicle motion, we design the cost and generate an optimized path based on sampling. The CarMaker simulator is utilized to verify the proposed algorithm. © 2023 ICROS.

关键词： Motion planning

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Neural Network Approaches for Magnetization Surface Prediction in Switched Reluctance Motors: Classical, Radial Basis Function, and Physics-Informed Models 3

Neural Network Approaches for Magnetization Surface Predicti...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Demidova, Galina Bogdanov, Andrey Iaremenko, Andrei Xirong, Zhu Chen, Hao Anuchin, Alecksey Moscow Power Engineering Institute Electric Drive Department Moscow Russia Itmo University Faculty of Control System and Robotics Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China

ISBN: (纸本)9798331516321

This paper investigates the prediction of magnetization surfaces in Switched Reluctance Motors using three distinct methods: classical Neural Networks, Radial Basis Function networks, and Physics-Informed Neural Networks. The study utilizes experimental data from a 7.5 kW SRM to assess the performance of these approaches, with all methods implemented in Python. Among the methods analyzed, PINN demonstrates the highest accuracy in predicting the magnetization surfaces, effectively capturing the underlying physical behavior of the motor. The computational costs of each method are evaluated, providing insights into training times, inference speeds, and floating-point operations per sample. The findings reveal that while NN offers a reasonable balance between performance and efficiency, the RBF method excels in training speed but shows limitations in capturing the physical dynamics of the system. In contrast, the PINN not only delivers superior predictive performance but also emphasizes the significance of incorporating physical principles into machine learning frameworks. The results highlight the applicability of these methods across various types of SRMs, suggesting their potential for enhancing control strategies in electrical and power engineering applications. © 2024 IEEE.

关键词： Radial basis function networks

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Multiagent Consensus over Time-Invariant and Time-Varying Signed Digraphs via Eventual Positivity

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IEEE Transactions on Automatic control 2023年第9期68卷 5429-5444页

作者： Fontan, Angela Wang, Lingfei Hong, Yiguang Shi, Guodong Altafini, Claudio Linköping University Division of Automatic Control Department of Electrical Engineering LinköpingSE-58183 Sweden KTH Royal Institute of Technology Division of Decision and Control Systems StockholmSE-10044 Sweden Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Tongji University Department of Control Science and Engineering Shanghai201804 China University of Sydney Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering SydneyNSW2008 Australia

Laplacian dynamics on signed digraphs have a richer behavior than those on nonnegative digraphs. In particular, for the so-called 'repelling' signed Laplacians, the marginal stability property (needed to achieve consensus) is not guaranteed a priori and, even when it holds, it does not automatically lead to consensus, as these signed Laplacians may lose rank even in strongly connected digraphs. Furthermore, in the time-varying case, instability can occur even when switching in a family of systems each of which corresponds to a marginally stable signed Laplacian with the correct corank. In this article, we present novel conditions for achieving consensus on signed digraphs based on the property of eventual positivity, a Perron-Frobenius (PF) type of property for signed matrices. The conditions we develop cover both time-invariant and time-varying cases. A particularly simple sufficient condition, valid in both cases, is that the Laplacians are normal matrices. Such condition can be relaxed in several ways. For instance, in the time-invariant case it is enough that the Laplacian has this PF property on the right side, but not on the left side (i.e., on the transpose). For the time-varying case, convergence to consensus can be guaranteed by the existence of a common Lyapunov function for all the signed Laplacians. All conditions can be easily extended to bipartite consensus. © 1963-2012 IEEE.

关键词： Lyapunov functions

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Improving Road Safety With Enhanced Vehicle Detection for Night-time Traffic in Mirpur 6

Improving Road Safety With Enhanced Vehicle Detection for Ni...

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6th International Conference on robotics and Automation in Industry, ICRAI 2024

作者： Younis, Usama Riaz, Faisal Raza, Umar Atali, Gokhan Rabnawaz, Hamid HEC Control Automotive and Robotics Lab Islamabad Pakistan Manchester Metropolitan University Department of Engineering Manchester United Kingdom Mechatronics Engineering Sakarya University of Applied Sciences Sakarya54050 Turkey Selftech Pvt Ltd Pakistan

ISBN: (纸本)9798350355703

This paper presents a study on improving night-time vehicle detection in the Mirpur region through a modified neural network model. Given the challenges of low visibility at night, the research focused on optimizing a deep learning algorithm to enhance detection accuracy. A dataset of 10,000 images covering various vehicle types was collected and annotated to train and test the model. The study found a 7% increase in detection performance compared to the standard model, highlighting the potential for tailored algorithms in specific environmental conditions. The findings suggest significant implications for road safety and autonomous driving systems, particularly in regions with similar night-time driving challenges. The research contributes to the broader field of computer vision by showing the effectiveness of model optimization in real-world conditions. © 2024 IEEE.

关键词： Vehicle safety

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Design and Analysis of Extensible Cable-Driven Continuum Robot with Variable Stiffness

Design and Analysis of Extensible Cable-Driven Continuum Rob...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Seleem, Ibrahim A. Naeem, Mohamed A. Ishii, Hiroyuki Waseda University Department of Modern Mechanical Engineering Japan Menoufia University Faculty of Electronic Engineering Department of Industrial Electronics and Control Engineering Egypt Harbin Institute of Technology School of Mechanical and Electrical Engineering Department of Mechatronics Engineering China South Valley University Faculty of Engineering Department of Mechatronics and Robotics Engineering Egypt

ISBN: (纸本)9798350325706

Extensible Soft Robots (ESRs) have increasingly attracted attention, especially in tight and complex environments, owing to their dexterity, and wide reachable workspaces relative to their volume. The existing actuation methods of ESR still suffer from challenges including large dimensions, expensive, complex modeling and control, and limited payload capacity. In this paper, we introduce a novel design for a lightweight, extensible and variable stiffness soft robot-based cable-driven actuation without backbone. The design composes of one section with three segments based on semi-octagon honeycomb structure, wherein the middle segment acts like a soft spring to provide extension feature. Additionally, the top and bottom segments have identical structure with two honeycomb patterns embedded within each other to improve the stiffness of the design. A design analysis is conducted to optimize the proposed structure with respect to stress and displacement. Additionally, a shape estimation approach is utilized to get accurate inference of the prototype's shape based on data acquired from a low-cost Inertial Measurement Unit (IMU) and Motion Capture System (MCS), and constant curvature assumption. A series of experiments including workspace reach-ability, repeatability, shape approximation and payload analysis are carried out to validate the proposed design. The results show that the prototype exhibits good compression/extension ratio up to 42% and 50% relative to its normal length, respectively. Moreover, its payload capability reaches to 565 grams. © 2023 IEEE.

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Preliminary Analysis of Physiological Signals in Relation to Early Indication of Paralysis Disorder Using Neural Network Approach

Preliminary Analysis of Physiological Signals in Relation to...

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Silchar Subsection Conference (SILCON), IEEE

作者： Vijay Kumar Sachin Gupta Department of Robotics and Control Engineering School of Electronics and Control Engieening Lovely Professional University Phagwara India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

As per latest research in the main medical sector, paralysis sight as is sight as observed as one of the main foremost causes of disability in human subjects trailing only behind chronic illnesses such as cancer and heart disease. In this sight as study, we examined health-related signals to uncover potential indicators of paralysis related disability. In collaboration with medical experts, we identified eight crucial parameters that could assist in determining whether an individual shows symptom of paralysis. Our research involved assembling an observational database comprising over 40,000 human subjects, spanning the main period from January 2018 to December 2023. We employed a three-layer Recurrent Neural Network to conduct categorical analyses involving training, validation, and testing phases. Our results suggest that ECG, blood pressure, heart rate and PPG signals are essential in evaluating an individual's risk of paralysis sight as disorders. The main model achieved an impressive accuracy rate of 94.3%, outperforming other models observed during the main study. These observed findings underscore the main potential of advanced machine learning techniques for facilitating the main early detection of paralysis sight as symptoms. By providing timely notifications to healthcare providers and human subjects at risk, our methodology can significantly enhance patient care and outcomes. This sight as research not only enriches the main scientific understanding of paralysis sight as but also offers practical solutions for improving human health through timely intervention strategies. Ultimately aiding in the main prevention of severe consequences associated with this sight as debilitating condition.

关键词： Training Recurrent neural networks Prevention and mitigation Machine learning Electrocardiography Muscles Physiology Paralysis Potassium Testing

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Unscented Kalman Filtering for Localization using Range or Bearing Data

Unscented Kalman Filtering for Localization using Range or B...

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UKACC International Conference on control (control)

作者： Richard T. O'Brien Michael D. M. Kutzer Robotics and Control Engineering Department United States Naval Academy Annapolis MD

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

A search and rescue localization algorithm is proposed using an unscented Kalman filter (UKF). The UKF provides superior performance to other filtering options such as the extended Kalman filter or the particle filter and is computationally less expensive than neural network localization methods. Each search agent obtains asynchronous measurements of its range and/or bearing relative to the victim. It is assumed that the victim's pose (position and orientation) and speed estimates and the associated covariance matrices are available to all agents simultaneously. Each agent maintains its own UKF to update these estimates and its pose estimate when a new measurement is received. Simulation results demonstrate that the agents can track the victim's location and orientation with minimal steady-state error despite significant measurement noise and large initiate estimation error.

关键词： Location awareness Estimation error Filtering Noise Measurement uncertainty Steady-state Kalman filters

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Task Load Based Persistent Coverage control for Large-Scale Domains Using Multi-Agent Systems with Anisotropic Sensors

Task Load Based Persistent Coverage Control for Large-Scale ...

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IEEE Southeastcon

作者： Kyle Odoy Erick J. Rodríguez-Seda Department of Weapons Robotics and Control Engineering United States Naval Academy Annapolis MD

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Multi-agent coverage control refers to the task of coordinating autonomous robots to effectively survey and gather information across a task-domain. This paper introduces a novel approach that addresses agents equipped with anisotropic sensors and constrained by nonholonomic movement, aiming to ensure persistent coverage and maintain awareness over a large-scale domain. The approach employs redefined Voronoi partitions that leverage each agent's local coverage domain based on its relative task load. The task load herein refers to the level of awareness that each agent has about the domain and the size of its area of dominance, all indirectly encoded in the Voronoi's mass. To account for the vehicles' nonholonomic nature and velocity constraints, the proposed approach is extended using the minimum curvature-based distance when determining the agent's area of dominance. A set of simulations involving teams of three and five agents showcases the effectiveness of the proposed control approach.

关键词： Surveys Anisotropic control systems Sensor systems Sensors Multi-agent systems Load modeling Autonomous robots

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Experimental Investigation of a Semi-Autonomous Search and Rescue System

Experimental Investigation of a Semi-Autonomous Search and R...

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UKACC International Conference on control (control)

作者： Richard T. O'Brien Eli Walz Katherine R. Hammonds Megan Rumbaugh Robotics and Control Engineering Department United States Naval Academy Annapolis MD

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

A previously-proposed semi-autonomous search and rescue system is investigated experimentally. A stationary vision system identifies and tracks a victim using an object detection neural network. The vision system then guides an autonomous unmanned ground vehicle to the victim's location using a ROS 2 publisher-subscriber communication protocol. A series of experiments are conducted with off-the-shelf components to investigate the impact of several design parameters. The resulting experimental data demonstrates that a fast and accurate response can be achieved. Furthermore, guidelines are established for parameter selection in future experiments.

关键词： Protocols Robot vision systems Neural networks Object detection Cameras Land vehicles Delays

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Model Predictive Torque control of Six-Phase Switched Reluctance Motors Based on Improved Voltage Vector Strategy

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7650-7661页

作者： Yang, Yifei Sun, Xiaodong Dianov, Anton Demidova, Galina Prakht, Vladimir Wang, Yong Han, Shouyi Suzhou Vocational University School of Mechanical and Electrical Engineering Suzhou215104 China Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China ITMO University Faculty of Control Systems and Robotics Petersburg197101 Russia Ural Federal University Department of Electrical Engineering Ekaterinburg620062 Russia Jiangsu University School of Electrical and Information Engineering Zhenjiang212013 China

To rapidly and accurately establish the model of switched reluctance motors (SRMs) and enhance torque control performance, this paper proposes a model predictive torque control (MPTC) strategy based on the optimized voltage vector. First, a fourth-order Fourier series is used to calculate the flux and torque models, and a complete nonlinear model of the SRM is constructed using the Kriging model. Second, the torque characteristics are used to divide the sectors, thereby reducing the number of candidate voltage vectors (CVVs) and effectively decreasing the computational burden of predictive control. Finally, an adaptive adjustment algorithm for the sector boundary angle is proposed, where the position of the sectors is determined based on variations in speed and load. The proposed method reduces torque ripple and enhances dynamic response capability. The effectiveness of this approach is validated through experiments on a 12/10 pole six-phase SRM prototype. © 2015 IEEE.

关键词： Vectors

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