论文总字数：28710字

摘 要

塔式起重机作为现代化工业和建筑业等方面应用的主要大型机械，占地面积小且易于安装运输等特点。塔式起重机主要用于运输重型货物，是安装在现场的关键机械，有着非常广泛的应用。通常，塔式起重机运行过程中很容易受到外界环境干扰，因为负载在运输过程中不仅容易受到风速、高度、湿度等自然因素影响，也容易受到电磁干扰等导致信号不稳定而引起波动。这种摆动会使危险系数增高，对自动控制系统的设计也有了更高的要求。

目前的起重机大多是人工操作，等到负载的摆动衰减到一定程度再进行定位，这种控制策略不仅工作效率低，也存在大量安全隐患。本文正是设计一种基于滑模控制的塔式起重机定位防摆控制器，解决这个问题。考虑到实际情况，本文针对变幅运动设计了相关的滑模控制器，经MATLAB验证，能够很好的实现定位防摆控制。

本文对系统的非线性模型进行了线性化，针对两种模型分别进行了控制器设计，对于线性模型采用了基于趋近律的滑模控制，并将不同输入信号的波形进行横向对比；另外对与非线性模型采用了反步自适应滑模控制和神经网络反步滑模控制两种设计方法进行对比设计。

关键词：塔式起重机 滑模变结构 反步 定位防摆 RBF神经网络

Research on anti-pendulum positioning control of tower crane based on sliding mode control

Abstract

Tower crane, as a major machine used in modern industry and construction, occupies a small area and is easy to install and transport.Tower crane is mainly used to transport heavy goods, is a key machinery installed in the field, has a very wide range of applications.In general, tower crane is easy to be disturbed by the outside environment in the process of operation, because the load in the process of transportation is not only easy to be affected by the custom, height, humidity and other natural factors, but also easy to be affected by electromagnetic interference and other signal instability and cause fluctuations.This kind of swing will make the dangerous coefficient increase, to the automatic control system design also has the higher request.At present, most cranes are operated manually. When the swing of the load is attenuated to a certain extent, positioning will be carried out. This control strategy is not only inefficient, but also has a lot of security risks. Think of the actual situation, this paper designed a sliding mode controller for Amplitude variation, which was verified by MATLAB and could well realize the positioning and anti-pendulum control.

In this paper, the nonlinear model of the system is linearized, and the controller is designed for the two models respectively. For the linear model, the sliding mode control based on the approach law is adopted, and the waveforms of different input signals are compared horizontally.In addition, two design methods of back-step adaptive sliding mode control and neural network back-step sliding mode control are compared with the nonlinear model.

Keywords：Tower crane; Sliding mode variable structure; Back step; Positioning anti-swing; RBF neural network

目 录

摘要 I

Abstract II

第一章 绪论 1

1.1研究背景及意义 1

1.2研究现状及主要内容 3

1.2.1国外研究现状 3

1.2.2国内研究现状 4

1.2.3 研究内容 5

第二章 塔式起重机模型的建立 7

2.1引言 7

2.2塔式起重机的工作原理及模型建立 7

2.2.1塔式起重机模型介绍 7

2.2.2塔式起重机工作原理 8

2.3塔式起重机的机理建模 8

2.4系统模型的线性化 10

2.5滑模控制的相关概念 11

第三章 线性模型下塔式起重机的控制器设计 14

3.1基于趋近律的滑模控制定义 14

3.2稳定性分析 15

3.3系统的能控性和能观性分析 15

3.4控制器设计 15

3.5仿真结果 16

第四章 非线性模型下塔式起重机控制器设计 20

4.1反步自适应滑模控制 20

4.1.1控制算法概述 20

4.1.2控制器设计 20

4.1.3仿真结果 21

4.2神经网络自适应反步滑模控制 24

4.2.1定义 24

4.2.2控制器设计 24

4.2.3仿真结果 26

4.3小结 28

第五章 总结与期望 29

5.1总结 29

5.2系统评价 29

5.3展望 29

参考文献 31

附录 33

基于趋近律的滑模控制方法控制器代码： 33

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