作者:
Liu, XinWen, ShuhuanLiu, HuapingRichard Yu, F.Yanshan University
Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Key Laboratory of Intelligent Rehabilitation and Neuroregulation in Hebei Province Department of Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Tsinghua University
Department of Computer Science and Technology Beijing100084 China Shenzhen University
College of Computer Science and Software Engineering Shenzhen518060 China Carleton University
School of Information Technology Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada
Traditional visual-inertial Simultaneous Localization and Mapping (SLAM) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. ...
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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 enha...
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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.
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 tracki...
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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 pe...
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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.
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 f...
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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 origi...
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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 technique...
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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.
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 ...
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This paper explores recent innovation in the field of robotic teleoperation, presenting a state-of-the-art system for a robotic arm, configurable as an exoskeleton or prosthetic limb. Based on noninvasive neural heads...
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Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging *** several scheduling algorithms have been proposed in recent years,they are mainly designed to handle batch task...
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Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging *** several scheduling algorithms have been proposed in recent years,they are mainly designed to handle batch tasks and not well-suited for real-time *** address this issue,researchers have started exploring the use of Deep Reinforcement Learning(DRL).However,the existing models are limited in handling independent tasks and cannot process workflows,which are prevalent in cloud computing and consist of related *** this paper,we propose SA-DQN,a scheduling approach specifically designed for real-time cloud *** approach seamlessly integrates the Simulated Annealing(SA)algorithm and Deep Q-Network(DQN)*** SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server,serving as a crucial feature of the task for the neural network to *** provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.
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