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29th International Conference on information, Communication and Automation Technologies, ICAT 2023

作者： Teczely, Zoltan Kiss, Balint Budapest University of Technology and Economics Department of Control Engineering and Information Technology Budapest Hungary

ISBN: (纸本)9798350399837

This paper presents an LPV model-based controller synthesis case study on a 2-DOF robotic manipulator. A polytopic LPV formulation was chosen with two scheduling variables. An H ∞ control strategy was used for the reference tracking objective which was implemented through LMI optimization and was compared to a nominal H∞ controller and feedback linearization extended with PD reference tracking criterion. The solution was tested on a real-life robotic arm located at BME Robotics Laboratory. Numerical evaluation was based on a joint measure of performance and robustness. © 2023 IEEE.

关键词： Feedback linearization

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PD control and the unwinding effect in quaternion parameterized attitude control 27

PD control and the unwinding effect in quaternion parameteri...

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27th IEEE International Conference on Intelligent engineering Systems, INES 2023

作者： Kimathi, Stephen Lantos, Bela Budapest University of Technology and Economics Department of Control Engineering and Information Technology Budapest Hungary

ISBN: (纸本)9798350328516

The unit quaternion is popular in the representation and control of rigid body attitude systems. However, it is known that two equilibrium points in the unit quaternion attitude formulation represent a single equilibrium point in a rotation matrix in SO(3). This often leads to a non-efficient control effort as the quaternion transients can settle to either of the equilibrium point regardless of the path taken or, even undergo a homoclinic orbit between the two equilibrium points. This behaviour is referred to as the unwinding effect. This paper presents solutions to cure the unwinding effect in unit quaternion parameterized attitude control systems through the use of a switching parameter or a saturation function. © 2023 IEEE.

关键词： Attitude control

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A Collaborative Graph-based SLAM Framework Using a Computationally Effective Measurement Algebra

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2023年第4期67卷 403-412页

作者： Péter, Gábor Kiss, Bálint Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

Simultaneous localization and mapping (SLAM) is an essential task for autonomous rover navigation in an unknown environment, especially if no absolute location information is available. This paper presents a computationally lightweight framework to enable agents with limited processing power to carry out the SLAM cooperatively and without absolute onboard localization sensors in a 2D environment. The proposed solution is built on a graph-based map representation, where nodes (resp. edges) represent landmarks (resp. odometry-based relative measurements), a measurement algebra with embedded uncertainty, and a compact database format that could be stored on a server in a centralized manner. The operations required by the agents to insert a new landmark in the graph, update landmark positions and combine measurements as a loop is closed in the graph are detailed. The resulting framework was tested in a laboratory environment and on a public dataset with encouraging results;hence our method can be used for cost-effective indoor mobile agents with limited computational resources and onboard sensors to achieve a mapping while keeping track of the agent’s position. The method can also be easily generalized for a 3D scenario. © 2023 Budapest University of technology and Economics. All rights reserved.

关键词： Mobile agents

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Adaptive Impedance control of Upper Limb Exoskeleton Robot Based on Human Motion Data

Adaptive Impedance Control of Upper Limb Exoskeleton Robot B...

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2024 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2024

作者： Zhai, Zhifei Liu, Keping Liu, Guang Yu, Changlin Sun, Zhongbo Changchun University of Technology Department of Control Science and Engineering Changchun China Jilin Engineering Normal University Department of Electrical and Information Engineering Changchun China

ISBN: (纸本)9798331504755

The upper limb exoskeleton robot provides an effective treatment method for patients with upper limb functional impairments. A humanoid impedance learning controller (HILC) was designed based on upper limb motion data from humans, which enhances the effectiveness of repetitive rehabilitation training based on human motion mechanisms. Based on human kinematic structures and healthy upper limb motion trajectories, a bionic robot dynamic model was developed, which served as the foundation for designing a humanoid robot control system. To address inaccuracies in the robot's dynamic model, differential adaptive estimation is utilized to compensate for model uncertainties, thereby enhancing accuracy. Additionally, the integration of impedance learning control (ILC) and feedforward control (FFC) within the system reduced the time required to reach a stable state, decreased tracking errors compared to traditional controllers, and improved system stability. Finally, experimental simulations based on ideal parameterized upper limb motion data were conducted to validate the controller's performance. The results demonstrated that the control system could accurately track the ideal parameterized upper limb motion trajectories, with the shoulder and elbow joint trajectories of the exoskeleton robot closely resembling those of a healthy human. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Sensor Selection for Mechatronic Systems Based on Closed-Loop control Simulations 1

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25th International Symposium on Measurement and control in Robotics, ISMCR 2023

作者： Gincsainé Szádeczky-Kardoss, Emese Varga-Berta, Tamás Kiss, Bálint Fazekas, Csaba Department of Control Engineering and Information Technology Budapest University of Technology and Economics Budapest Hungary Thyssenkrupp Components Technology Hungary Budapest Hungary

ISBN: (数字)9783031510854

ISBN: (纸本)9783031510847

The goal of the paper is to present a tool, which can be used for the selection of sensors in a closed-loop control of a mechatronic system. Several sensor parameters (such as noise power or delay) are simulated and their effects on the whole system’s performance are analyzed. For the simulations, Matlab-Simulink environment is applied. The simulated data are then used to estimate the transfer function of the whole closed-loop system with a controller. The services of the System Identification toolbox are appropriate for this task. If the specifications are given as the required damping and bandwidth of the system, the tool estimates the sensor parameters, which can ensure the desired performance. Some simulation results demonstrate the workflow of our tool, which was implemented using the App Designer of Matlab. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Simulink

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Robust Aerodynamic Parameter Estimation of Unmanned Aircraft Based on Two-step Identification

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2023年第4期67卷 394-402页

作者： Kimathi, Stephen Lantos, Bela Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Magyar tudósok krt. 2. BudapestH-1117 Hungary

This paper presents the estimation of stability and control derivatives of an unmanned aircraft. The aerodynamics are described using regressors composed of velocity, angular rates, flow angles and control surface deflections. The flight data is generated from numerical simulation of postulated equations of motion describing the aerodynamics model. Least squares based on the equation error method is used to estimate the parameters representing the different force and moment aerodynamic coefficients. Statistical analysis is done on the estimates to determine the accuracy and adequacy of the estimates to describe the aerodynamic model. A dynamic simulation based on the identified aerodynamic model is used to improve the parameter estimates through regression of the errors between the flight data and the model response. The aircraft under consideration is a scaled Yak-54 fixed wing unmanned aerial vehicle. © 2023 Budapest University of technology and Economics. All rights reserved.

关键词： Fixed wings

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A Model-free Combined Energy and Thermal Management Strategy for HEVs Based on Reinforcement-Learning Under Low-Temperature

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Li, Kai Chen, Hong Wu, Yuhu Zhao, Jing Ding, Shihong Gao, Jinwu Changchun China Changchun China School of Control Science and Engineering Dalian University of Technology Dalian China Department of Electromechanical Engineering University of Macau Macau China School of Electrical and Information Engineering Jiangsu University Zhenjiang China

To improve vehicle adaptability to low-temperature environments, this paper proposes a combined energy and thermal management strategy (C-ETM) based on twin delayed deep deterministic policy gradient (TD3) algorithm for hybrid electric vehicles (HEVs). First, a vehicle energy management system (EMS) model and a engine-battery-cabin coupled thermal management system (CTMS) model are developed. By analyzing the coupling relationship between the CTMS and the EMS, a multiobjective optimization problem is constructed to minimize fuel consumption and battery aging damage and ensure SOC stability. Facing the challenges of solving optimization problems caused by the high-order complex nonlinearity of thermal-electrical coupling systems, the optimization problems are transformed into a Markov decision process (MDP). A reinforcement learning framework based on the TD3 algorithm is designed to achieve a real-time solution to the problem from a new perspective, overcoming the reliance on the system models and accurate future traffic information. The proposed strategy has efficient performance in terms of fuel economy, battery life, ensuring SOC stability, and adaptability. The total optimization cost reaches 91.42% level of the dynamic programming (DP) strategy, which is 30.3% lower than the model predictive control (MPC) strategy. The online computing burden is only 0.19% of the MPC strategy, which has strong potential for real-time applications. IEEE

关键词： Waste heat

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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HKB-YOLO: transmission line fire detection method based on hierarchical feature fusion

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International Journal of Security and Networks 2024年第4期19卷 188-198页

作者： Ying, Zhilei Meng, Yanan Chen, Ruoxi Lou, Jianlou School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin 132022 China Department of Automation Jilin Institute of Chemical Technology Jilin 132022 China School of Computer Engineering Northeast Electric Power University Jilin 132012 China

Timely transmission line fire inspections are vital for power system safety. Although deep learning models are widely used for flame detection, struggle with small target recognition due to background interference and are vulnerable to input data perturbations, posing security risks. This study introduces a hierarchical feature fusion method based on the you only look once Version 8 (YOLOV8) framework. It employs high-performance GPU network version 2 (HGNetV2) to enhance small-target feature extraction while reducing computational complexity. A spatial pyramid pooling-fast module with large separable kernel attention is designed to highlight key features and suppress background noise. The proposed bidirectional slim neck structure (BiSlimneck) reduces feature loss. Adversarial training enhances the model’s robustness. Experimental results show a 1.4% accuracy improvement, with a reduction in parameters and complexity by 18.3% and 21.6%, respectively. After adversarial training, the accuracy of the network in the face of attacks increased by 10% compared to the pre-training. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Premixed flames

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