臺灣博碩士論文加值系統

本研究的目的在比較兩種不同摩擦力模型補償的精密平台循跡控制器性能。這兩種摩擦模型分別是具有直接應用激勵的動態非線性回歸（DNLRX）模型和具有外來輸入的非線性自回歸（NARX）神經網絡模型。該模型的目的是使用它來逆轉摩擦力模型和系統的特性。首先，將測試信號用於移動平台，然後將導出的數據用於估計DNLRX模型並訓練NARX神經工作模型。在兩個系統中，該模型都將用於估計驅動器所需的驅動電流 (u)。 DNLRX模型和人工神經網絡模型都是系統和摩擦力的動態逆模型。本文分析了NARX體系結構方法，顯示可有效的應用在動態系統程序模型辨識。在本研究中該模型是用在系統動力學相關的驅動力估計（使用位移），然後用作補償摩擦的前饋控制器。最後，我們建構一個實驗系統，實驗結果表明這兩個控制器之間的差異，同時證實它們在精密平台的跟踪性能上的有效性。

The objective of this thesis is to compare precision tracking controller with two various of friction model compensation for precision stages. These friction models are Dynamic NonLinear Regression with direct application of eXcitation (DNLRX) model and Nonlinear Autoregressive with Exogenous Inputs (NARX) neural network model. The purpose of the model is to use it to inverse the frictional force model and the properties of the system. Firstly, the test signal is used to move the stage then the derived data is used to estimate the DNLRX model and to train the NARX neural work model. In both systems, the model will be used to estimate the needed driving current (u) for the motor. Both DNLRX model and artificial neural network model are the dynamic inverse model of the stages and friction forces. The NARX architectural approach, shows promising qualities for dynamic systems applications, is analyzed in this paper. Utilization of this model as an estimate of the driving force associated with the dynamics of the system to the displacement, then to compensate the feedforward controller is used. Finally, an experimental system was formed and the results showed the efficiency of the differences between these two controllers in the tracking performance of the precision stage.

摘要.........................................................................i
Abstract....................................................................ii
Acknowledgement............................................................iii
Table of Contents...........................................................iv
List of Table...............................................................vi
List of Figures............................................................vii
Nomenclature.................................................................x
Chapter 1 Introduction.......................................................1
1.1 Preface..................................................................1
1.2 Research Purpose.........................................................1
1.3 Thesis Structure.........................................................2
Chapter 2 Literature Review..................................................3
2.1 Mechanical Systems with Friction.........................................3
2.2 System Modelling.........................................................5
2.2.1 Dynamic Linear System..................................................5
2.2.2 Control System for Mechanical System with Friction.....................7
2.3 PID Controller...........................................................8
2.4 Neural Network...........................................................9
2.4.1 Neural Network Identification..........................................9
2.4.2 NARX Neural Network Architecture......................................10
2.5 Maxwell Slip Model Structure Based Friction Identification..............13
2.5.1 The NonLinear Regression (NLR)........................................14
2.5.2 Dynamic NonLinear Regression with direct application of eXcitation....14
Chapter 3 Methodology.......................................................18
3.1 Experimental Setup......................................................18
3.1.1 Experimental Platform.................................................18
3.1.2 Laser Measurement System..............................................21
3.2 Feedback Control System.................................................23
3.3 Design of The Controller................................................26
3.3.1 Design of PID Controller..............................................27
3.3.2 Design of NARX Controller.............................................29
3.4 Error Metric............................................................32
Chapter 4 Experimental Result...............................................33
4.1 The Result of Dynamics Model............................................35
4.2 Identified Results of NARX..............................................37
4.3 Identified Results of DNLRX.............................................44
4.4 Control Results.........................................................47
4.4.1 Results of tracking 10 μm sinusoid signal.............................48
4.4.2 Results of tracking 100 μm sinusoid signal............................54
4.4.3 Results of tracking 10 μm repeating sequence signal...................60
4.4.4 Results of tracking 100 μm repeating sequence signal..................62
Chapter 5 Conclusion and Discussion.........................................64
5.1 Conclusion..............................................................64
5.2 Discussions.............................................................64
References..................................................................65
Extended Abstract...........................................................70

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