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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Kadu, C.B. Bhosale, V.P. Vikhe, P.S. Mandhare, V.V. Pravara Rural Engineering College Department of Instrumentation & Control Engineering Loni India Pravara Rural Engineering College Department of Computer Engineering Loni India

ISBN: (纸本)9798350355611

In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity caused by the continuously varying cross section. An IMC-based PID controller is employed to improve efficiency of controlling the level of spherical tanks which exhibit nonlinear dynamics. For processes up to second-order with dead time and first-order numerator dynamics, a single tuning parameter will be offered. The process model is determined employing mathematical modeling and standard step response-based system identification. The laboratory model of an interacting and non-interacting nonlinear system is introduced, which is capable of performing more complex tasks. To verify the effectiveness of the suggested IMC based PID controller, results of real-time experiments and a simulation study on spherical tank level control are incorporated. © 2025 IEEE.

关键词： Three term control systems

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Assembling Solar Panels by Dual Robot Arms Towards Full Autonomous Lunar Base Construction

Assembling Solar Panels by Dual Robot Arms Towards Full Auto...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Nunziante, Luca Uno, Kentaro Diaz, Gustavo H. Santra, Shreya De Luca, Alessandro Yoshida, Kazuya Sapienza University of Rome Department of Computer Control and Management Engineering Italy Department of Aerospace Engineering Sendai980-8579 Japan

ISBN: (纸本)9798331531614

Since the successful Apollo program, humanity is once again aiming to return to the Moon for scientific discovery, resource mining, and inhabitation. Upcoming decades focus on building a lunar outpost, with robotic systems playing a crucial role to safely and efficiently establish essential infrastructure such as solar power generating towers. Similar to the construction of the International Space Station (ISS), shipping necessary components via modules and assembling them in situ should be a practical scenario. In this context, this paper focuses on the integration of vision, control, and hardware systems within an autonomous sequence for a dual-arm robot system. We explore a perception and control pipeline specifically designed for assembling solar panel modules, one of the benchmark tasks. Ad hoc hardware was designed and tested in real-world experiments. A mock-up of modular solar panels and active-passive connectors are employed, with the control of this grappling fixture integrated into the proposed pipeline. The successful implementation of our method demonstrates that the two robot manipulators can effectively connect arbitrarily placed panels, highlighting the seamless integration of vision, control, and hardware systems in complex space applications. © 2025 IEEE.

关键词： Space applications

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STGNN: a new self-trained generalized neural networks task space control for robot manipulators

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Neural Computing and Applications 2025年 1-26页

作者： Elmogy, Ahmed Elawady, Wael El-Ghaish, Hany Computer Engineering Department Prince Sattam Ibn Abdelaziz University Alkharj16273 Saudi Arabia Computers and Automatic Control Department Faculty of Engineering Tanta University Tanta31527 Egypt

The inherent uncertainties in the dynamic and kinematic parameters of robot manipulators pose significant challenges for their control in task space. This paper introduces an innovative adaptive sliding mode control strategy, leveraging self-trained generalized neural networks to enhance the precision and robustness of robot manipulator control. The proposed framework called STGNN integrates deep neural networks for the real-time estimation and adjustment of dynamics’ and kinematics’ parameters. Key contributions of this work include the development of semi-supervised learning models for adaptive dynamics and kinematics estimation. By training the generalized neural network on different trajectories, the same models can be utilized to operate on a variety of tasks, significantly improving the robustness, scalability, and adaptability of the control system. The STGNN approach allows the models to leverage both labeled and unlabeled data, incorporating a self-training mechanism that enhances their ability to generalize and adapt to diverse operational scenarios. This capability is particularly beneficial for real-time applications, where the ability to learn and adapt on the fly is crucial. Simulation results demonstrate the superior performance of the STGNN framework in controlling robot manipulators in operational space, showcasing its potential for real-world applications. The proposed method significantly advances the adaptability, accuracy, and efficiency of robot control systems, offering notable improvements over conventional approaches. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self adjusting control systems

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Intelligent obstacle avoidance algorithm for safe urban monitoring with autonomous mobile drones

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Journal of Electronic Science and Technology 2024年第4期22卷 19-36页

作者： Didar Yedilkhan Abzal E.Kyzyrkanov Zarina A.Kutpanova Shadi Aljawarneh Sabyrzhan K.Atanov Department of Computer Engineering Astana IT UniversityAstana010000Kazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Automation and Control Systems L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Software Engineering Jordan University of Science and TechnologyIrbid22110Jordan

The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone *** deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition *** this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring *** primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as *** algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone ***,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the ***,the algorithm identifies observers within the swarm based on the current *** further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy *** approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular *** integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle *** proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive *** results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.

关键词： Drone swarms Fuzzy logic Intelligent solution Smart city Urban monitoring

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Design an optimal FOPD controller for electric vehicle speed tracking control 7

Design an optimal FOPD controller for electric vehicle speed...

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7th International Conference on Vocational Education and Electrical engineering, ICVEE 2024

作者： Abood, Layla Hattim Abood, May H. Action, Dalal Hammood University of Technology Department of Control and Systems Engineering Baghdad Iraq Al-Iraqia University Department of Computer Engineering Baghdad Iraq Mtu Edu Department of Computer Engineering Baghdad Iraq

ISBN: (纸本)9798331505103

Electric Vehicles (EVs) become very important issue and gained attention due to many reasons like its economic price, saving environment and more reliable. In this study, controlling speed for EV is utilized by tracking the desired speed using Fractional Order Proportional Derivative (FOPD) controller with a smart tuning algorithm named Sunflower Optimization (SFO) algorithm to satisfy robust and efficient controlled speed. The numerical analysis is obtained using Matlab program in two steps, controlling the system to reach the desired value in best and stable response then apply two undesired signals (disturbance signals) to test system robustness and finally compare the suggested controller with different conventional controllers(Normal PD controller-Optimal PD controller). © 2024 IEEE.

关键词： MATLAB

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Demonstrating the Potential of Visualization in Education with the Manim Python Library: Examples from Algorithms and Data Structures 47

Demonstrating the Potential of Visualization in Education wi...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Marković, Milorad Kaštelan, Ivan University Of Novi Sad Department Of Computing And Control Engineering Sub-department Of Computer Engineering And Communications Novi Sad Serbia

ISBN: (纸本)9798350382495

Visualization is a powerful tool for learning and teaching complex concepts, especially in the field of computer science. However, creating effective and engaging visualizations can be challenging and time-consuming for educators and students. This paper demonstrates how the Manim Python library, a free and open-source project for creating mathematical animations, can be used to create visualizations that illustrate algorithms and data structures in an undergraduate course. The paper showcases several examples of visualizations created with the Manim library, and explains how they help the students to understand the logic, behavior, and performance of the algorithms and data structures. The paper also discusses the benefits and limitations of the Manim library, and suggests some directions for future research and development. © 2024 IEEE.

关键词： Python

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Neuro-Based Higher Order Sliding Mode control for Perturbed Nonlinear Systems

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Intelligent Automation & Soft Computing 2023年第4期36卷 385-400页

作者： Ahmed M.Elmogy Wael M.Elawady Computer Engineering Department Prince Sattam Bin Abdelaziz UniversityAlkharj24567KSA Department of Computers and Control Engineering Tanta UniversityTanta13457Egypt

One of the great concerns when tackling nonlinear systems is how to design a robust controller that is able to deal with *** researchers have been working on developing such type of *** of the most efﬁ-cient techniques employed to develop such controllers is sliding mode control(SMC).However,the low order SMC suffers from chattering problem which harm the actuators of the control system and thus unsuitable to be used in many practical *** this paper,the drawbacks of low order traditional sliding mode control(FOTSMC)are resolved by presenting a novel adaptive radial basis function neural network–based generalized rth order sliding mode control strategy for nth order uncertain nonlinear *** proposed solution adopts neural networks for their excellent capability in function approximation and thus used to approximate the nonlinearities and uncertainties for systems under *** approximation errors are completely considered in the developed *** proposed approach can be used with any order of sliding mode and thus can be generally used with various types of *** global sta-bility of the proposed control approach is proved through Lyapunov stability *** proposed approach is validated and assessed through simulations on the nonlinear inverted pendulum system with severe modeling *** simulations results show that the proposed approach provide superior perfor-mance compared with other approaches in the literature.

关键词： SMC nonlinear systems PID Lyapunov stability radial basis function neural networks

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A Data Fusion Service-Oriented Infrastructure for Production Line Monitoring 25

A Data Fusion Service-Oriented Infrastructure for Production...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Gaiardelli, Sebastiano Dall'ora, Nicola Ponzio, Francesco Fraccaroli, Enrico Fummi, Franco Di Cataldo, Santa Vinco, Sara University Of Verona Department Of Engineering For Innovation Medicine Italy Politecnico Di Torino Department Of Control And Computer Engineering Italy

ISBN: (纸本)9798350340266

The Industry 4.0 paradigm has deeply changed classical manufacturing by introducing data-based analytics and decision-support strategies. At the state of the art, data used for manufacturing monitoring is mostly originated by sensors, that undergo a fusion step to align different data sources. However, this data is only relative to the monitored process, and it does not include the corresponding operating conditions and parameters, that are known by the Manufacturing Execution System (MES). Such information is currently either not included or labeled by hand, thus incurring in errors and limiting the amount of available labeled data. To overcome this issue and go beyond the sole data fusion of sensor data, this paper proposes an infrastructure that automatically label time series generated by sensors with information extracted from the MES, to achieve enhanced monitoring of the production process. The relevance of the proposed solution and the possibilities opened by its application are stressed with the application to a robotic arm. © 2024 IEEE.

关键词： Anomaly detection

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Routing with ART: Adaptive Routing for P4 Switches With In-Network Decision Trees

Routing with ART: Adaptive Routing for P4 Switches With In-N...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Angi, Antonino Sacco, Alessio Esposito, Flavio Marchetto, Guido Politecnico di Torino Department of Control and Computer Engineering Italy Saint Louis University Computer Science Department United States

ISBN: (纸本)9798350351255

Recent advances in Machine Learning (ML) brought several advantages also within computer network management. For programmable data planes, however, it is more challenging to benefit from these advantages, given their limited resource capabilities colliding with the complexity of ML models. In this paper, we propose ART, an attempt to simplify ML-based solutions for routing, so that they can "fit", i.e., be executed, on P4 switches. To provide such model simplification, ART relies on efficient knowledge distillation techniques, converting, in particular, Deep Reinforcement Learning (DRL) models into a simpler Decision Tree (DT). Our evaluation results validate the accuracy of the extracted model and the application of the model logic directly into switches with little impact, paving the way for a more reactive data plane programmability via machine learning integration. © 2024 IEEE.

关键词： Adversarial machine learning

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