桥式起重机先进控制研究

发布时间：2018-03-30 22:29

本文选题：桥式起重机　切入点：小车吊重系统　出处：《北京化工大学》2011年硕士论文

【摘要】：桥式起重机作为现代物流装备之一,广泛应用于各种工业场合,消除或控制吊重的摇摆对提高起重机工作效率和安全性具有重要意义。吊重防摇控制技术是起重机作为现代物流装备所必需具备的功能之一,对桥式起重机系统的动力学分析是解决起重机快速对位和吊重防遥问题的基础。本文首先采用拉格朗日方法推导具有普遍意义的桥式起重机系统的动力学方程——三维、二维和一维桥式起重机系统的数学模型。在合理的范围内对起重机系统的非线性动力学方程进行简化,得到桥式起重机系统的线性动力学方程——三维、二维和一维桥式起重机系统的线性模型,为研究桥式起重机防摆问题提供了理论依据。针对吊重摆角等变量现场测量的难度和成本,利用小车位置信息设计全状态观测器。通过设置全状态观测器重构相关状态变量空间,从而将包括小车位置在内的所有状态变量的估计信息,提供给防摇控制系统。论文采用极点配置的状态反馈控制方法、线性二次型调节器(LQR)最优控制及比例积分微分(PID)控制方法,对起重机防摆问题进行仿真研究。仿真结果表明,上述现代控制方法具有一定的局限性。在对神经网络理论进行分析研究的基础上,将径向基函数(RBF)神经网络的自适应PID控制算法应用于桥式起重机吊重防摆系统中。利用二个RBF神经网络自适应PID控制器对小车的位置和负载的摆动分别进行控制。通过神经网络的自适应学习能力,在线整定PID控制器的比例(P)、积分(I)和微分(D)三个内部参数,实现具有最佳参数组合的PID控制。仿真结果表明,该算法对起重机定位无静差、无超调,同时迅速消除负载的摆动。
[Abstract]:As one of the modern logistics equipment, bridge crane is widely used in various industrial occasions. It is of great significance to eliminate or control the swaying of hoisting load, which is one of the necessary functions of crane as modern logistics equipment, and it is of great significance to improve the efficiency and safety of crane. The dynamic analysis of bridge crane system is the foundation to solve the problem of quick alignment and hoisting remote control. In this paper, the Lagrangian method is used to deduce the dynamic equation of the bridge crane system. The mathematical model of two-dimensional and one-dimensional bridge crane system. The nonlinear dynamic equation of crane system is simplified within a reasonable range, and the linear dynamic equation of bridge crane system is obtained. The linear model of two-dimensional and one-dimensional bridge crane system provides a theoretical basis for studying the anti-swinging problem of bridge crane. In view of the difficulty and cost of the field measurement of swinging angle and other variables, the full state observer is designed by using the vehicle position information, and the relevant state variable space is reconstructed by setting the full state observer. Thus, the estimation information of all state variables, including the position of the car, is provided to the anti-rolling control system. In this paper, the state feedback control method of pole assignment, the optimal control of linear quadratic regulator LQR and the PID-proportional integral differential control method are used to simulate the anti-swinging problem of crane. These modern control methods have some limitations. Based on the analysis of neural network theory, The adaptive PID control algorithm of radial basis function (RBF) neural network is applied to the crane crane anti-swing system. Two RBF neural network adaptive PID controllers are used to control the position and load swing of the trolley respectively. Through the adaptive learning ability of neural networks, In order to realize the PID control with the best combination of parameters, the three internal parameters of the PID controller are adjusted on line. The simulation results show that the algorithm has no static error and no overshoot for the crane positioning, and the load swing is eliminated rapidly at the same time.
【学位授予单位】：北京化工大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH215

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