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

共 12248 条记录，以下是1291-1300 订阅

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On affine quasi-LPV System Identification with unknown state-scheduling using (deep) Recurrent Neural Networks 26

On affine quasi-LPV System Identification with unknown state...

引用

26th international conference on System Theory, control and Computing (ICSTCC)

作者： Rehmer, Alexander Kroll, Andreas Univ Kassel Kassel Dept Measurement & Control Kassel Germany

ISBN: (数字)9781665467469

ISBN: (纸本)9781665467469

The main contribution of this paper is a model structure for the identification of affine quasi linear parameter-varying (qLPV) models in state-space (SS) form. The special case where the state is unknown and part of the scheduling vector is considered. In this case the model is always recurrent in the states which demands for an internal dynamics approach, e.g. a Recurrent Neural Network (RNN). The proposed approach is based on a structured RNN developed by Lachhab et al. [1], but extends the original model via so-called gates, neural network structures which are responsible for the recent success of RNNs on various areas of application in Machine Learning, such as the Long short-term memory (LSTM) network and the Gated Recurrent Unit (GRU). The use of gates has multiple advantages: The complexity of the models scheduling map and hence its approximation capabilities are increased, while preserving its affine quasi-LPV structure and the applicability of the results on global asymptotic stability (GAS) by Lachhab et al. [1] at the same time. The performance of the proposed approach is demonstrated by comparison with two other RNN-based approaches on two nonlinear system identification benchmark problems.

关键词： System Identification and modeling Machine Learning Nonlinear systems LPV

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Load Frequency control in Four-Area Interconnected Power System Using Fuzzy PI controller with Penetration of Renewable Energies 2

Load Frequency Control in Four-Area Interconnected Power Sys...

引用

2nd international conference on Power, control and Computing Technologies, ICPC2T 2022

作者： Krishna, P.J. Meena, V.P. Singh, V.P. M. National Institute of Technology Department of Electrical Engineering Jaipur302017 India

ISBN: (纸本)9781665458580

Load frequency control (LFC) in an interconnected power system is advancing towards a crucial path due to the modern penetration of highly uncertain renewable energy sources in power systems. The modern LFC systems should be capable of handling complex regulation problems with a high degree of renewable source diversification to assure the generation-load balance. In this contribution, a rule-based fuzzy PI controller is implemented in a four-Area interconnected power system with penetration of renewable energy sources like wind energy, hydro-gen aqua electrolyzer-fuel cell (HAE-FC), photo-voltaic (PV) system, and geothermal energy sources separately in each area along with a conventional energy source. The frequency response of four-Area interconnected power system is obtained employing a rule-based fuzzy PI controller for load disturbances and is compared with the conventional PI controller. The simulation results demonstrate the superiority of the fuzzy PI controller over the conventional PI controller. © 2022 IEEE.

关键词： Renewable energy sources PI control Wind energy Simulation Power system dynamics Power control Frequency response

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The Future of Traffic Management: Embracing AI for Sustainable Urban Mobility

The Future of Traffic Management: Embracing AI for Sustainab...

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IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Prashant Sharma Siddharth Sharma Raj Pratap Rosey Chauhan Shikha Verma Computer Science And Engineering Sharda University Greater Noida India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The management of traffic in urban environments has become increasingly complex due to rapid urbanization and the growing number of vehicles on the road. This literature review synthesizes research on various traffic management systems and technologies, focusing on solutions such as real-time image processing, IoT-based systems, wireless sensor networks, and deep learning models. The review highlights key advancements, including intelligent traffic controllers utilizing real-time video analytics, IoT for congestion control, and AI-enabled traffic monitoring systems.

关键词： YOLO Wireless sensor networks Smart cities Biological system modeling Visual analytics Traffic control Real-time systems Stability analysis Safety Systematic literature review

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Intelligent-Aided Design for control Parameters of Kitchen Waste Pretreatment Based on the Specific Bayesian Network Expert System

Intelligent-Aided Design for Control Parameters of Kitchen W...

引用

complex systems and Intelligent Science (CSIS-IAC), international Annual conference on

作者： WeiLai Jiang WenLu Wang ChengHong Zheng XueSong Bai YaoNan Wang School of Electrical and Information Engineering Hunan University Changsha China Changsha Zoomlion Environmental Industry Co. LTD Changsha China

ISBN: (数字)9798331504755

ISBN: (纸本)9798331504762

Kitchen waste treatment is an essential issue in the process of urbanization. Aiming at problems such as the dependence of engineer experience on the parameter setting of various equipment in the whole process of kitchen waste pretreatment, the failure to adapt to the characteristics of kitchen waste, and poor overall performance, this paper studies the intelligent assistant design method of kitchen waste pretreatment control parameters based on Bayesian network expert system. The overall framework of the kitchen waste pretreatment process was built using the experience of experts in the kitchen waste industry, and the correlation network between kitchen waste characteristics and pretreatment equipment parameters was established to form a Bayesian causal probability table. The Bayesian network structure was built to realize the inference prediction of equipment parameter changes based on the characteristics of kitchen waste. The verification experiment shows that the design performs well in the kitchen waste pretreatment parameter decision. It provides a feasible scheme for effectively solving the problem of kitchen waste pretreatment.

关键词： Industries Correlation Design methodology Urban areas Process control Biology Bayes methods Expert systems complex systems

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A Deep Reinforcement Learning Approach for Navigation and control of Autonomous Underwater Vehicles in complex Environments

A Deep Reinforcement Learning Approach for Navigation and Co...

引用

international conference on control, Automation, Robotics and Vision (ICARCV)

作者： Artemis Stefanidou Elena Politi Christos Chronis George Dimitrakopoulos Iraklis Varlamis Department of Informatics and Telematics Harokopio University Athens Greece

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

The comprehension of the underwater environment is recently being accelerated by technological advances in sensors, robotics and Artificial Intelligence (AI). At the forefront of this evolution, lies the Autonomous Underwater Vehicle (AUV), a sophisticated ocean exploration tool that is capable of performing underwater mapping, leveraging data obtained by onboard sensors. AUVs can navigate autonomously in unknown environments without any human interaction, while their level of autonomy is tightly linked to their path planning strategy. In this study, we perform a comparative analysis of a Deep Reinforcement Learning (DRL) method utilising two neural network models, a Linear Model (LM) that consists only of linear layers, and a Convolutional Model (CM) that consists of convolution layers for feature extraction that are merged with linear layers. Our evaluation focuses on assessing the performance of the proposed models for generating optimal paths in 3D underwater environments based on path length and obstacle avoidance. Through comprehensive simulations, we showcase the efficiency of our solution and present a comprehensive framework tailored for solving path planning problems in 3D complex underwater settings.

关键词： Analytical models Solid modeling Autonomous underwater vehicles Three-dimensional displays Navigation Deep reinforcement learning Robot sensing systems Path planning Artificial intelligence Intelligent sensors

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Design of Simulation Device for Greenhouse control

Design of Simulation Device for Greenhouse Control

引用

international conference on Image, Vision and Intelligent systems, ICIVIS 2021

作者： Jia, Yunsong Huang, Shuaiqi Xiao, Liang Yang, Shaochen Li, Xiang College of Information and Electrical Engineering China Agricultural University Beijing100083 China

ISBN: (纸本)9789811669620

To avoid the cases that control algorithm can`t work well after application in green-house, and then cause losses, it`s essential to test the performance of the control algorithm before putting it into practical use. To achieve this, we need a method to test the control algorithm rapidly and effectively. However, there is no such effective method to test the performance of the control algorithm in a greenhouse. With the rapid development of the IoT technology in the field of agricultural production, the method of process data modeling is proposed to help solve such problems. Based on the method of process data modeling, this paper proposes a new model which includes Z-transformation, scope control, OFF response and difference bias, and developed a greenhouse simulation test model. The experimental results show that our model can capture the changes of both temperature and humidity, and also limit the humidity within the set range. This proves that our model is effective and precise. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Greenhouses

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Fault-Tolerant Neural Network Flight control for Helicopter Altitude and Attitude systems Under Output Constraints

Fault-Tolerant Neural Network Flight Control for Helicopter ...

引用

IEEE international conference on Unmanned systems (ICUS)

作者： Tianhong Li Yankai Li Yingmin Yi Yue Tian Dongping Li Kenan Yong School of Automation and Information Engineering Shaanxi Collaborative Innovation Center for Modern Equipment Green Manufacturing Xi'an University of Technology Xi'an China School of Automation and Information Engineering Xi'an University of Technology Xi'an China School of Sciences Xi'an Technological University Xi'an China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

In this paper, an adaptive radial basis function neural network flight control scheme is proposed for the unmanned helicopter altitude and attitude systems. For the flying helicopters, the issues of modeling error, output constraints and actuator failure cannot be ignored when constructing the flight controller, which have great impact on the control performance of unmanned helicopters. To solve these problems, the adaptive radial basis function neural network method is utilized to approximate the missing parts in the input channel due to modeling errors and actuator failures. Then, the barrier Lyapunov function technology is used to constrain the control output, and the flight controller is designed via uniting the backstepping control scheme. In which, the virtual control law is adjusted according to the tracking errors, and the online weight is updated by using the adaptive radial basis function neural network. The tracking errors are proved to converge to a reasonable scope via the Lyapunov stability theory. Finally, a numerical experiment is used to verify the effectiveness of the designed flight controller.

关键词： Adaptation models Actuators Adaptive systems Backstepping Attitude control Helicopters Radial basis function networks Tail Stability analysis Lyapunov methods

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Ensemble-based Offline Data-driven Evolutionary Optimization for Operation Parameter of Blast Furnace

Ensemble-based Offline Data-driven Evolutionary Optimization...

引用

international conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Qiucheng Ye Hongxia Cai Yunqi Bian Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Stable control of the iron-making process through the adjustment of operating parameters is essential to improve blast furnace productivity. However, due to the complex and dynamic nature of the reaction process, it is difficult to perform accurate modeling and simulation experiments for a blast furnace. This poses great difficulties in performing the evaluation of control strategies in real-time. Offline evolutionary optimization, as an optimization technique that relies only on historical data, enables the search for optimal operation parameters through the introduction of surrogates. Based on the characteristics of historical time-series data of the iron-making process, this study proposes an offline evolutionary optimization method based on advanced time-series surrogates. Ensemble strategy combining surrogates with different prediction time windows contributes robustness during the optimization process. Additionally, multiple sub-regions are maintained to enable extensive searching of the decision space with a multi-objective nature. The efficiency of the proposed pooling method is demonstrated through verification in actual production cases.

关键词： Productivity Accuracy Computational modeling Process control Optimization methods Blast furnaces Knowledge discovery Robustness Real-time systems Fuzzy systems

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An Attention-Based Approach for Enhanced Robot Grasp Detection in Unstructured Environments

An Attention-Based Approach for Enhanced Robot Grasp Detecti...

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IEEE international conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Han Li Xinggang Fan Dejun Zheng Heping Chen Yaonan Li Diqing Yu Yuao Jin College of Computer Science and Technology Zhejiang University of Technology Hangzhou China Zhijiang College of Zhejiang University of Technology Shaoxing China Shenzhen Academy of Robotics Shenzhen China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

Accurate grasp detection in unstructured environments remains a challenging problem in robot manipulation tasks due to irregular layouts, occlusion, complex backgrounds, and different object shapes. In order to better understand the feature relationship between different positions in the image, a new grasp detection network is proposed in this paper. The attention part of this network combines the advantages of Softmax and linear attention and is named SoLiNet. SoLiNet introduces an additional set of agent labels in the traditional attention module and computes them for each window in each agent. By adjusting the attention weight and position bias of input features, the model can better capture global context information and thus obtain better performance when grasping unknown objects. SoLiNet's accuracy was 98.9% on the Cornell dataset and 96.2% on the Jacquard dataset. It has been successfully used in the actual robot arm grasping system.

关键词： Accuracy Shape Computational modeling Layout Grasping Manipulators Feature extraction control systems Intelligent systems Context modeling

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modeling and Route Planning for Collaborative Multi-Agent Inspection

Modeling and Route Planning for Collaborative Multi-Agent In...

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international conference on control, Automation, Robotics and Vision (ICARCV)

作者： Jinbo Wang Jia Xu Yuanqiang Zhou Li Li Shuai Sui Yuanxin Li Department of Control Science and Engineering College of Electronic and Information Engineering Tongji University Shanghai China Liaoning University of Technology Ji nzhou China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

In various practical applications, collaborative inspection systems in which multiple agents work together to accomplish inspection tasks are becoming increasingly important for enhancing operational efficiency. This paper addresses the routing problem in multi-agent collaborative inspection systems, where certain inspection points require the simultaneous presence of multiple agents to perform inspection operations. A novel approach using max-plus algebra is presented to model the collaborative inspection process and it provides a foundation for research and applications in system control and scheduling optimization. The max-plus linear (MPL) model is then converted into a Mixed Integer Linear Programming (MILP) formulation to tackle the routing problem with complex constraints inherent in the collaborative scene. Experimental results validate that the proposed MPL model and MILP approach reduce inspection completion time and waiting time at collaborative points.

关键词： Multiprotocol label switching Adaptation models Solid modeling Algebra Collaboration Inspection Routing Solids Real-time systems Robots

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