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第25届中国控制与决策会议

作者： Weipeng Liu Ning Liu Hexu Sun Guansheng Xing Yan Dong Haiyong Chen School of control Science and Engineering Hebei University of Technology Education Ministry Engineering Research Center of Intelligent Rehabilitation Equipment and Detection Technology Hebei University of Science and Technology

ISBN: (纸本)9781467355339

A dispatching algorithm of elevator group control system is proposed based on reinforcement learning. elevator dispatching is modeled by a Markov Decision Process. Then an internally recurrent neural network based reinforcement learning method is designed to find the optimal dispatching policy while the state-action value function is iteratively approximated. Finally, several simulated experiments are done to compare the trained dispatching policy with other traditional ones. The experimental results demonstrate the effectiveness of proposed dispatching method.

关键词： elevator group control Dispatching Algorithm Reinforcement Learning Neural Network

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Research for Distributed elevator group control Base on Fuzzy Neural Network

Research for Distributed Elevator Group Control Base on Fuzz...

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第二十九届中国控制会议

作者： LIU Tundong 1,LIAO Xiaosheng 2,ZENG Jianping 2 1.School of Information Science and Technology,Xiamen University,Xiamen 361005,P.R.China2.School of Information Science and Technology,Xiamen University,Xiamen 361005,P.R.China

In order to meet the increasing requirements on real-time and reliability of elevator group control,ARM chip is applied to construct hardware platform of elevator’s main-board in this *** on ARM embedded hardware platform,distributed architecture of elevator group control is designed,aiming to solve the problems caused by elevator group control’s non-linearity,uncertainty and *** on the basis of distributed architecture,fuzzy neural network technology is used to design intelligent scoring algorithm for elevator group ***,test finished on elevator model proves the rationality and practical value of the distributed architecture as well as the intelligent scoring algorithm.

关键词： elevator group control Distributed Architecture Fuzzy Neural Network

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RESEARCH OF elevator group control SYSTEM BASED ON INTELLIGENT control

RESEARCH OF ELEVATOR GROUP CONTROL SYSTEM BASED ON INTELLIGE...

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2007中国宁波国际计算机科学与技术学术大会

作者： Song Han Wang Fengmei Software School Dahongying Vocational Technical College

This paper proposes a new mathematic model for elevator group control algorithm based on multi-target planning,and establishes an multi-target evaluate function,considering the average waiting time,average riding time,the crowded degree of the car and the power consumption of the system.A multi-target planning intelligent dispatching algorithm is designed which can be applied in four different traffic patterns:Up-Peak Traffic Pattern,Down-Peak Traffic Pattern,Random Inter-Floor Traffic Pattern and Idle Traffic ***-target planning intelligent dispatching algorithm are embedded in the simulating software and environment which are programmed with Visual *** simulating result proves the superiority of multi-target planning intelligent dispatching algorithm.

关键词： elevator group control Multi-target planning Intelligent dispatching-algorithm Traffic pattern recognition

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elevator group-control Policy Based on Neural Network Optimized by Genetic Algorithm

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Transactions of Tianjin University 2009年第4期15卷 245-248页

作者：沈虹万健如张志超刘英培李光叶 School of Electrical Engineering and Automation Tianjin University Baoding Power Supply Company

Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algorithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.

关键词： elevator group control genetic algorithm neural network hybrid algorithm

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Prediction and Analysis of elevator Traffic Flow under the LSTM Neural Network

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Intelligent control and Automation 2024年第2期15卷 63-82页

作者： Mo Shi Entao Sun Xiaoyan Xu Yeol Choi School of Architecture Kyungpook National University Daegu Republic of Korea School of Software Engineering East China Normal University Shanghai China HaXell elevator Co. Ltd. Shanghai China

elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.

关键词： elevator Traffic Flow Neural Network LSTM elevator group control

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