多轿厢电梯系统多目标优化方法研究
发布时间：2019-01-02 08:36
【摘要】：高层和超高层建筑随着城市化的发展不断增多,电梯作为建筑物内仅能使用的垂直运输系统,受到人们的广泛关注。人们都电梯的性能和服务质量的要求也在提高,传统的单一井道内一个电梯轿厢的单轿厢电梯已经无法满足现代化高层建筑对垂直交通的需求。种新型的“一井多梯”多轿厢电梯系统应运而生,通过在一条电梯井道内增加电梯轿厢数量来提高电梯的运载效率,是当前国际上解决高层建筑输送效率、减小占地面积、节约能源和减少建筑成本的最新方式。本文对多轿厢电梯的候梯时间、乘梯时间、系统能耗制定多目标优化,同时对多轿厢电梯系统的优化策略和多轿厢电梯系统的安全避撞问题进行了探讨,提出优化避撞逻辑规划的安全验证方法以及一种基于粒子群遗传混合算法的优化调度算法,本课题的研究具有良好的应用前景和实用性。本论文研究内容主要工作如下：(1)查阅大量的国内外相关资料,结合多轿厢电梯系统控制技术的国内外发展趋势,制定了针对多轿厢电梯系统的优化避撞逻辑规划和粒子群-遗传混合算法进行安全验证和优化调度的课题研究方案。(2)探讨了多轿厢电梯系统的系统构成,根据电梯轿厢运行方式的不同将多轿厢电梯系统分为双层轿厢电梯系统、循环式多轿厢电梯系统和单井道多轿厢电梯系统。(3)对多轿厢电梯系统建立多目标优化数学模型,针对平均候梯时间、平均乘梯时间以及多轿厢电梯系统运行能耗三个性能控制目标,建立了循环式多轿厢电梯控制系统的多目标优化模型。(4)针对循环式多轿厢电梯系统的避撞问题,根据循环式多轿厢电梯控制系统的轿厢安全运行准则,分析了循环式多轿厢电梯系统的避撞控制问题,研究了基于优化避撞逻辑规划方法。提出了一种电梯轿厢安全避障新算法,建立了禁止轿厢危险运行的优化避撞算式。从而避免了派梯中所选轿厢在井道中发生碰撞。通过实例仿真验证了所提方法的可行性、有效性,能够保证循环式多轿厢电梯控制系统的安全运行。(5)针对多轿厢电梯的候梯时间、乘梯时间、系统能耗三个目标所建立的多目标优化算式,以候梯时间、乘梯时间、系统能耗为目标,提出一种基于粒子群-遗传混合算法的多轿厢电梯系统的优化调度方案,对比传统遗传算法,进行了实验仿真,根据仿真图进行分析,仿真图所显示的结果验证了粒子群-遗传混合算法新方法的有效性,对多轿厢多目标优化的可实现性和优越性。(6)对本文章所研究的内容进行总结,并对接下来的工作进行展望。
[Abstract]:With the development of urbanization, elevators, as the only vertical transportation system in buildings, have been paid more and more attention. The performance and service quality of elevators are also being improved. The traditional single-car elevator in a single shaft can no longer meet the need of vertical traffic in modern high-rise buildings. A new type of "one well and more ladder" multi-car elevator system emerges as the times require. Increasing the number of elevator cars in an elevator shaft to improve the lift transportation efficiency is the current international solution to the transport efficiency of high-rise buildings and reduce the area occupied. The latest way to save energy and reduce construction costs. In this paper, the multi-objective optimization of the waiting time, riding time and energy consumption of multi-car elevator is made, and the optimization strategy of multi-car elevator system and the problem of safe collision avoidance of multi-car elevator system are discussed. A security verification method for optimal collision avoidance logic programming and an optimal scheduling algorithm based on particle swarm optimization and genetic hybrid algorithm are proposed. The research in this paper has good application prospects and practicability. The main work of this paper is as follows: (1) referring to a large number of domestic and foreign related materials, combined with the multi-car elevator system control technology development trends at home and abroad, In this paper, the optimal collision avoidance logic programming and Particle Swarm genetic Hybrid algorithm for multi-car elevator system are studied. (2) the system structure of multi-car elevator system is discussed. According to the different operation modes of the elevator car, the multi-car elevator system is divided into the double-deck car elevator system, the circulation multi-car elevator system and the single-shaft multi-car elevator system. (3) the multi-objective optimization mathematical model is established for the multi-car elevator system. Aiming at the three performance control objectives, the average waiting time, the average riding time and the running energy consumption of the multi-car elevator system, three performance control objectives are proposed. A multi-objective optimization model for the control system of the circulatory multi-car elevator is established. (4) aiming at the collision avoidance problem of the multi-car elevator system, according to the safe operation criteria of the control system of the multi-car elevator, In this paper, the collision avoidance control problem of cyclic multi-car elevator system is analyzed, and the logic programming method based on optimal collision avoidance is studied. In this paper, a new algorithm of safe obstacle avoidance for elevator car is proposed, and an optimized formula for avoiding collision is established. Thus avoiding the collision of the car selected in the ladder in the well. The simulation results show that the proposed method is feasible and effective, and can ensure the safe operation of the control system of the circulatory multi-car elevator. (5) for the waiting time and the ladder time of the multi-car elevator, This paper presents an optimal scheduling scheme for multi-compartment elevator system based on particle swarm optimization (PSO) and genetic hybrid algorithm (PGA), which is based on the multi-objective optimization formula established by three objectives of system energy consumption, such as waiting time, ladder time and system energy consumption. Compared with the traditional genetic algorithm, the experimental simulation is carried out, and the simulation diagram is analyzed. The results show that the PSO / GA method is effective. The realizability and superiority of multi-car and multi-objective optimization are discussed. (6) the contents of this paper are summarized and the future work is prospected.
【学位授予单位】：沈阳建筑大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TU857
本文编号：2398269
[Abstract]:With the development of urbanization, elevators, as the only vertical transportation system in buildings, have been paid more and more attention. The performance and service quality of elevators are also being improved. The traditional single-car elevator in a single shaft can no longer meet the need of vertical traffic in modern high-rise buildings. A new type of "one well and more ladder" multi-car elevator system emerges as the times require. Increasing the number of elevator cars in an elevator shaft to improve the lift transportation efficiency is the current international solution to the transport efficiency of high-rise buildings and reduce the area occupied. The latest way to save energy and reduce construction costs. In this paper, the multi-objective optimization of the waiting time, riding time and energy consumption of multi-car elevator is made, and the optimization strategy of multi-car elevator system and the problem of safe collision avoidance of multi-car elevator system are discussed. A security verification method for optimal collision avoidance logic programming and an optimal scheduling algorithm based on particle swarm optimization and genetic hybrid algorithm are proposed. The research in this paper has good application prospects and practicability. The main work of this paper is as follows: (1) referring to a large number of domestic and foreign related materials, combined with the multi-car elevator system control technology development trends at home and abroad, In this paper, the optimal collision avoidance logic programming and Particle Swarm genetic Hybrid algorithm for multi-car elevator system are studied. (2) the system structure of multi-car elevator system is discussed. According to the different operation modes of the elevator car, the multi-car elevator system is divided into the double-deck car elevator system, the circulation multi-car elevator system and the single-shaft multi-car elevator system. (3) the multi-objective optimization mathematical model is established for the multi-car elevator system. Aiming at the three performance control objectives, the average waiting time, the average riding time and the running energy consumption of the multi-car elevator system, three performance control objectives are proposed. A multi-objective optimization model for the control system of the circulatory multi-car elevator is established. (4) aiming at the collision avoidance problem of the multi-car elevator system, according to the safe operation criteria of the control system of the multi-car elevator, In this paper, the collision avoidance control problem of cyclic multi-car elevator system is analyzed, and the logic programming method based on optimal collision avoidance is studied. In this paper, a new algorithm of safe obstacle avoidance for elevator car is proposed, and an optimized formula for avoiding collision is established. Thus avoiding the collision of the car selected in the ladder in the well. The simulation results show that the proposed method is feasible and effective, and can ensure the safe operation of the control system of the circulatory multi-car elevator. (5) for the waiting time and the ladder time of the multi-car elevator, This paper presents an optimal scheduling scheme for multi-compartment elevator system based on particle swarm optimization (PSO) and genetic hybrid algorithm (PGA), which is based on the multi-objective optimization formula established by three objectives of system energy consumption, such as waiting time, ladder time and system energy consumption. Compared with the traditional genetic algorithm, the experimental simulation is carried out, and the simulation diagram is analyzed. The results show that the PSO / GA method is effective. The realizability and superiority of multi-car and multi-objective optimization are discussed. (6) the contents of this paper are summarized and the future work is prospected.
【学位授予单位】：沈阳建筑大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TU857
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