自适应神经网络控制器在主动隔振控制中的应用研究.doc

自适应神经网络控制器在主动隔振控制中的应用研究

目  录
摘要 III
Abstract V
第 1 章 绪论 1
1.1 引言 1
1.2 主动隔振控制技术国内外研究现状与发展 1
1.2.1 控制器的研究方面 3
第2 章 隔振平台设计及其动力学分析 5
2.1隔振机理 5
2.2 主动隔振系统设计 12
2.3 两级主动隔振系统的动力学模型 23
2.4 两级隔振系统主动控制原理 24
2.5 本章小结 25
第3 章 直接自适应神经网络主动隔振控制技术研究 26
3.1 神经网络概述 26
3.2 神经网络基本理论 26
3.2.1 神经元的基本模型 26
3.2.2 神经网络的结构及其算法 27
3.3 直接自适应神经网络振动主动控制技术研究 33
3.3.1控制系统结构 33
3.3.2直接自适应神经网络结构及学习算法研究 34
3.4 基于Matlab 的自适应神经网络控制器设计及仿真 36
3.4.1利用GUI设计自适应神经网络控制器的拓 36
3.4.2数字仿真及结果分析 36
3.5 本章小结 38
结论 39
参考文献 41
致 谢 43

自适应神经网络控制器在主动隔振控制中的应用研究
摘要
振动给人类和机械带来的危害是不言而喻的，从而主动隔振变得相当重要。本文将预测与神经网络相结合，构造了一种振动主动控制系统, 运用 3 层BP(Back Propagation)网络对系统进行了试验研究。
本文分析了致动器在双层隔振系统中不同安装方式下的主动控制力与隔振系统参数之间的关系。在该两级主动隔振系统中，主动隔振致动器可以有三种不同的安装方式，采用仿真的方法选定了最优的一种安装方式使各种激振源产生最佳的减振效果佳的。基于三层BP 网络，构造了直接自适应神经网络控制器，并对隔振系统进行了振动主动控制，避免了以前神经网络振动主动控制的双网络结构形式，该方法采用系统的输出作为神经网络的学习输入，不需要预先或在线辨识系统，能实时调整控制率找到最优的控制力，具有良好的自适应性。利用GUI 设计自适应神经网络控制器的拓扑结构，基于Matlab 设计了自适应神经网络控制器。研究结果表明，采用该法设计的控制器能够实现从频率为0.1Hz 到50Hz 的隔振能力，而且随着输入层和隐含层神经元个数的增加，低频隔振效果也有一些改善，与此同时，神经网络控制器的结构也复杂了，网络规模增大，计算和训练的速度变慢了，难以满足振动控制系统的实时性要求。

关键词：神经网络，主动隔振，执行器，动力学，MATLAB
 

Adaptive neural network controller in active vibration isolation control of the Applied Research
Abstract
Human and mechanical vibration to the harm is self-evident, active vibration isolation has become very important. This article will neural network technology in isolation system, a tectonic active vibration control systems, use of 3-BP (Back Propagation) network to the system were studied, forecast and neural network integration, in the structure of the two isolation , Active vibration isolation actuator is installed on the dynamics of isolation system have a greater impact .
This paper analyses the actuator in the double-isolation system installed in different ways under the control and active vibration isolation system parameters of the relationship between. In the two active vibration isolation system, active vibration isolation actuators can have three different installation methods, the use of simulation has been selected the best way to install a variety of exciting ways to produce the best source of the vibration Excellent results. BP based on the three-tier network, constructed directly adaptive neural network controller and isolation system for the active vibration control and avoid the previous neural network active vibration control of dual-network structure, the method used the system as a neural network output Enter the study, or do not need to advance on-line identification system capable of real-time adjustments to find the optimal control of the controlling power, has a good adaptability. GUI design using adaptive neural network controller topology, the design based on Matlab adaptive neural network controller. The results show that the use of the design of the controller can be realized from the frequency of 0.1 Hz to 50 Hz in isolation, but also with the input of the hidden layer and the increase in the number of neurons, the effect of low-frequency vibration isolation have some improvement, and Meanwhile, the neural network controller is also complicated by the structure, size of network increases, computing and slow down the pace of training, vibration control systems to meet the real-time requirements

Key words: Neural networks,Active vibration isolation, the Executive, Dynamics, MATLAB