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检索条件"任意字段=International Conference on Complex Systems: Control and Modeling Problems"

共 12202 条记录，以下是431-440 订阅

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Linearization of Quadruple Tank System using Taylor Series method and control by BFOA and PSO algorithm 2

Linearization of Quadruple Tank System using Taylor Series m...

引用

2nd IEEE international conference on Measurement, Instrumentation, control and Automation, ICMICA 2023

作者： Kumar, C Praveen Ayyar, K. Parameswaran, B. Srm Valliammai Engineering College Dept. of Electronics and Instrumentation Engineering Chennai India

ISBN: (纸本)9798350355048

The Quadruple four tank system is one of the real time complex multi-variable tank systems. It involves various process variables like pressure inside of the tank, level of the tanks, flow rate of the process fluid which flows into the tank system. All these variables are to be sensed and controlled. Difficulty lies in the sensing and controlling of these variables as they are inter linked with one another. Flow rate of the process fluid influences or determines the level of the fluid present inside the tank. The system includes two or more input variables with two or more output variables that can be represented using Mathematical modeling. Here in this paper, the Quadruple System is mathematically modelled, and the linearization is achieved using Taylor Series method to obtain the transfer function model of the system. Further, a controller based on Bacteria Foraging Optimization Algorithm and Particle Swarm Optimization technique is used to tune the controller. The TD (time domain) and TI (time integral) performance criteria are used to assess the overall performance of the proposed controllers using MATLAB Simulink. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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modeling Processes for controlling the Behavior of Non-Extensive systems

Modeling Processes for Controlling the Behavior of Non-Exten...

引用

problems of Logistics, Management and Operation in the East-West Transport Corridor (PLMO), international conference on

作者： Bahram Ismailov Department of Instrumental Engineering Azerbaijan State Oil and Industry University Baku Azerbaijan

ISBN: (数字)9798350353099

ISBN: (纸本)9798350353105

The article proposes a non-standard approach to the analysis, evaluation and control of the behavior of complex coupled chaotic systems with fractional dynamics interacting in an Open System. The results of studies using analytical and numerical methods for modeling and simulating the processes of system interactions at the meso-level in a non-monotonic space are presented. Using the example of intelligently connected systems, their connections are analyzed and collective behavior is modeled. The purpose of modeling is to find a satisfactory coherent state of interacting systems. The axiomatic of the Wireless Sensor Networks (WSN) is given in terms of a swarm system in combination with coupled non-linear hyper chaotic structures of fractional order. The evaluation of the results of interactions is given in terms of the Tsallis entropy, the visualization of the results of interactions is given in the form of recurrence plots.

关键词： Wireless sensor networks Analytical models Visualization Heuristic algorithms Process control Open systems Search problems Entropy Numerical models Transient analysis

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Optimization of complex systems in Photonics by Multi-agent Robotic control 21st

Optimization of Complex Systems in Photonics by Multi-age...

引用

21st international conference on Practical Applications of Agents and Multi-Agent systems, PAAMS 2023

作者： Pouvreau, Quentin Georgé, Jean-Pierre Bernon, Carole Maignan, Sébastien IRIT Université de Toulouse CNRS Toulouse INP UT3 Toulouse France ISP System Vic-en-Bigorre France

ISBN: (纸本)9783031376153

The optimization of complex industrial systems represents a class of difficult problems, due to their embodiment in the physical world, and whose search spaces are disrupted, non-linear and potentially vast. Their parametrization relies on the combination of many variables, each change generally impacting the whole system. Mathematical approaches are limited by the fact that the models are too coarse or non-existent, and by the imprecision of the measurements and the machining of components of such systems. The action cost of the system calls into question population-based heuristics and swarm intelligence where each individual must be tested. We propose a multi-agent approach allowing a global/black-box modeling of the system in terms of input variables and objectives, as well as an agnostic and continuous adaptive optimization, based on sensor feedbacks from the running system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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A Stochastic Games based Resource Allocation Method for Cloud based Safe Computing Platform 26

A Stochastic Games based Resource Allocation Method for Clou...

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IEEE 26th international conference on Intelligent Transportation systems (ITSC)

作者： Zhang, Zhilin Lv, Fei Ying, Zhipeng Wang, Xi Zhao, Hongli Tang, Tao Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China China Acad Railway Sci Signals & Commun Res Inst Beijing 100081 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China

ISBN: (纸本)9798350399462

Safe computing platform is a safe architecture for the wayside equipment of CBTC, which keeps train operation control safely and efficiently in urban rail transit system. With the expansion of the subway line and the increase of passenger flow, the cloud computing technology is introduced into transit system to cope with the increase of maintenance cost and accident risk probability caused by the increase of physical equipments of CBTC. However, the architecture of safe computing platform must be considered when transplant the physical wayside equipment to cloud servers, which is hard to allocate the resources for the wayside equipment softwares to realize a good load-balance value while meeting the safety requirements defined by the safe computing platform. As one of the standard analytical tools, game theory has been applied in various fields to analyze resource allocation and decision-making problems. And stochastic games is good at modeling multi-agent decision-making problems, which is suitable for the problem we face. In this paper, a stochastic games is utilized to formulate the resource allocation of the cloud servers among the safe computing platform softwares and a SARSA based resources allocation algorithm with safety requirements is proposed to solve it. We compare SRAS with other similar algorithms in some simulation scenarios. The simulation results show that the SRAS method can get a better convergence performance and load-balance value of the cloud servers.

关键词： Cloud computing Safe computing platform Stochastic games Resources allocation

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SENSORLESS control STRATEGY OF STEPPER MOTOR BASED ON SUPER-TWISTING SLIDING MODE OBSERVER

SENSORLESS CONTROL STRATEGY OF STEPPER MOTOR BASED ON SUPER-...

引用

2024 CSAA/IET international conference on Aircraft Utility systems, AUS 2024

作者： Zhang, Yuchen Lu, Peng Che, Jionghui Hu, Mingliang Zhao, Jun Aeronautics Computing Technique Research Institute Xi’an China

The open-loop control of the stepper motor is simple, but it has low positioning accuracy, large torque pulsation, and low energy efficiency. While the closed-loop magnetic field oriented control has high positioning accuracy, small torque pulsation, and high energy efficiency, but has problems such as complex structure and large amount of calculation. This paper proposes a hybrid control strategy using subdivision control and sensorless control based on sliding mode observer (SMO) for low speed and high speed respectively. Super-twisting SMO is designed observe back electromotive force (back-EMF) and sensorless speed closed-loop control is implemented. Compared with the traditional first-order SMO, the super-twisting SMO improves the estimation accuracy and stability of the rotor position and speed, weakens the system chattering, and improves the system robustness and dynamic response capability. Finally, an experimental platform on the basis of a 2-phase hybrid stepper motor was built to verify the effectiveness and reliability of the control strategy. © The Institution of Engineering & Technology 2024.

关键词： Stepping motors

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Designing an Advanced control System for ABB IRB 140 Robotic Manipulator: Integrating Machine Learning and Computer Vision for Enhanced Object Manipulation 5

Designing an Advanced Control System for ABB IRB 140 Robotic...

引用

5th international conference on control systems, Mathematical modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Kondratyev, Sergey Pikalov, Vladimir Belokopytov, Ruslan Muravyev, Artem Boikov, Andrey Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia

ISBN: (纸本)9798350309126

This research paper presents an approach to developing a control system for the six-axis industrial robotic manipulator ABB IRB 140, aimed at enhancing object manipulation tasks. The focus is on the integration of computer vision and machine learning for efficient object detection and recognition. The study includes the analysis and comparison of various machine learning algorithms, including the application of convolutional neural networks and the YOLOv3 algorithm for real-time object detection using a depth camera. The work also covers the development of a robotic gripper with a robust control system capable of compensating for the saturation effect. To verify the proposed control system, the robotic complex is modeled in the Simulink environment. The research results confirm the possibility of successful implementation of the developed components into the existing robotic complex and its telecontrol system, ensuring the enhancement of efficiency and reliability in the object manipulation process. © 2023 IEEE.

关键词： Object detection

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Scalable Task-Driven Robotic Swarm control via Collision Avoidance and Learning Mean-Field control

Scalable Task-Driven Robotic Swarm Control via Collision Avo...

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IEEE international conference on Robotics and Automation (ICRA)

作者： Cui, Kai Li, Mengguang Fabian, Christian Koeppl, Heinz Tech Univ Darmstadt Dept Elect Engn & Informat Technol D-64287 Darmstadt Germany

ISBN: (纸本)9798350323658

In recent years, reinforcement learning and its multi-agent analogue have achieved great success in solving various complex control problems. However, multi-agent reinforcement learning remains challenging both in its theoretical analysis and empirical design of algorithms, especially for large swarms of embodied robotic agents where a definitive toolchain remains part of active research. We use emerging state-of-the-art mean-field control techniques in order to convert many-agent swarm control into more classical single-agent control of distributions. This allows profiting from advances in single-agent reinforcement learning at the cost of assuming weak interaction between agents. However, the mean-field model is violated by the nature of real systems with embodied, physically colliding agents. Thus, we combine collision avoidance and learning of mean-field control into a unified framework for tractably designing intelligent robotic swarm behavior. On the theoretical side, we provide novel approximation guarantees for general mean-field control both in continuous spaces and with collision avoidance. On the practical side, we show that our approach outperforms multi-agent reinforcement learning and allows for decentralized open-loop application while avoiding collisions, both in simulation and real UAV swarms. Overall, we propose a framework for the design of swarm behavior that is both mathematically well-founded and practically useful, enabling the solution of otherwise intractable swarm problems.

关键词： Reinforcement learning

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Enhancing Echo Processing Through the Integration of Support Vector Machine and Weber’s Law Descriptors 19

Enhancing Echo Processing Through the Integration of Support...

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19th international conference on Software Technologies, ICSOFT 2024

作者： Hedir, Mehdia Demim, Fethi Messaoui, Ali Zakaria Messaoui, Aimen Abdelhak Belaidi, Hadjira Rouigueb, Abdenebi Nemra, Abdelkrim Algeria Laboratory of Guidance and Navigation Ecole Militaire Polytechnique Bordj El Bahri Algiers Algeria Laboratory of Complex Systems Control and Simulators Ecole Militaire Polytechnique Bordj El Bahri Algiers Algeria Signals and Systems Laboratory Institute of Electrical and Electronic Engineering University M’Hamed Bougara of Boumerdes Algeria Laboratory of Artificial Intelligence and Virtual Reality Ecole Militaire Polytechnique Bordj El Bahri Algiers Algeria

ISBN: (纸本)9789897587061

Removing ground echoes from weather radar images is a topic of great importance due to their significant impact on the accuracy of processed data. To address this challenge, we aim to develop methods that effectively eliminate ground echoes while preserving the precipitation, which is a crucial meteorological parameter. To accomplish this, we propose to test Local Descriptors based on Weber’s law (WLD), as well as descriptors that combine Weber’s law with Local Binary Pattern (WLBP), using Support Vector Machine (SVM) classifiers to automate the recognition of both types of echoes. The proposed methods are rigorously tested at the sites of Setif and Bordeaux to evaluate their effectiveness in accurately identifying the ground echoes and precipitation. The results of our experiments demonstrate that the proposed techniques are highly effective in eliminating ground echoes while preserving the precipitation, and can be considered satisfactory for practical applications in meteorological data processing. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Support vector machines

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The Design of Multi-section Bionic Robot and the Development of control System

The Design of Multi-section Bionic Robot and the Development...

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2023 international conference on Artificial Intelligence and Automation control, AIAC 2023

作者： Yang, Yaxi Zhang, Naiwen Chen, Richeng Ke, Mingguan Aerospace Internet of Things Technology Co. Ltd Beijing100094 China

ISBN: (纸本)9798350383805

Traditional reconnaissance and detection platforms are constrained by the comprehensive conditions, such as the obstacle crossing ability, the concealment and the body size, and their reconnaissance capabilities are limited, and there are problems such as single adaptability to the terrain environment and poor concealment, which cannot fully meet the needs of military reconnaissance, cave detection, and post-disaster rescue. Therefore, a multi-section bionic robot is proposed, its overall mechanical structure and control system are designed, and experimental tests are carried out under grass, gravel, sand, steps and other terrains, and the results show that the multi-section bionic robot can adapt to a variety of complex terrains and geomorphology and improve reconnaissance capabilities. © 2023 IEEE.

关键词： control systems

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A Reinforcement Learning-based Approach for Active Optimization of High Proportional Distributed Power Supply Access to Rural Distribution Networks 2

A Reinforcement Learning-based Approach for Active Optimizat...

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2022 2nd international conference on Intelligent Power and systems, ICIPS 2022

作者： Li, Rui Yan, Tao Zhao, Yang Zhang, Jinming Zhang, Lu China Electric Power Research Institute Beijing100192 China China Agricultural University Beijing100083 China

In the process of actively implementing the rural revitalization strategy and the "double carbon"goal, distributed power sources are connected to the rural distribution network on a large scale, resulting in a gradual transition to a new type of active distribution network, which has a greater impact on the distribution network in terms of voltage, network loss, and protection, thus posing greater demands on the distribution network in terms of planning and control, power quality management, and operation. This significantly impacts the voltage, network loss, and protection of the distribution network, thus putting higher requirements on the planning and control, power quality management, operation, and maintenance of distribution networks. Since the traditional OPF algorithm has great defects in solving large-scale decentralized, time-series, and stochastic nonlinear problems, in order to realize real-time control of distribution networks under more complex network conditions, this paper proposes an optimization algorithm based on reinforcement learning and applies it to real-time control of active distribution networks, taking the optimal network loss as the objective function and considering network voltage, line load factor, and distributed power supply. As a de-modeling artificial intelligence method, reinforcement learning has great advantages in solving large problems with complex mathematical models. The method's control effect and convergence time can be improved by introducing imitation learning and migration learning, expanding the knowledge matrix, and introducing risk assessment mechanisms. Based on MATLAB R2017a simulation platform for the IEEE 33-node improvement model, the simulation results show that the reinforcement learning method has a better control effect in solving the real-time control problem of active distribution network, significantly alleviates the voltage crossing limit, effectively reduces the network loss, and has good real-time char

关键词： Real time control

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