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研究生:余致賢
研究生(外文):Chih-Hsien Yu
論文名稱:超音波馬達驅動系統識別及控制之研究
論文名稱(外文):Study of the Identification and Control of Ultrasonic Motor Driving System
指導教授:陳添智陳添智引用關係
指導教授(外文):Tien-Chi Chen
學位類別:博士
校院名稱:國立成功大學
系所名稱:工程科學系碩博士班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:115
中文關鍵詞:nono
外文關鍵詞:traveling wave ultrasonic motorfeedback linearization methodgeneralized regression neural network
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As the dynamic characteristics of the traveling wave ultrasonic motor (TWUSM) are highly nonlinear and time varying, and the analysis model difficult to be obtained. It is difficult to design a suitable controller to achieve high-precision position control using conventional control techniques. A new identification approach is proposed and implemented, and it aims to provide a practical model for controller design and simulation. As a result, a generalized regression neural network (GRNN) based model is developed to identify the relation between input excited frequency and phase difference of two-phase AC voltages and the output driving torque generated by the TWUSM. One major advantage is that transfer function identification is no longer required. The other advantage is that it allows for the application of traditional controller design and standard software simulation.
On the driving design aspect, the design of a traveling-wave ultrasonic motor drive circuit, intended to simultaneously employ both driving frequency and phase modulation control. The drive frequency control offers fast speed tracking ability, and the phase difference control is necessary to control both rotational directions continuously. The operating principles and a detailed analysis of the proposed driving circuit, consisting of voltage-controlled oscillator (VCO), voltage-controlled phase-shifter circuit and non-resonant power amplifier converter, are introduced. To drive the TWUSM effectively, a two-phase power amplifier converter using non-resonant scheme was designed to provide a balanced two-phase voltage source. Two-phase AC driving voltages can be maintained at the same peak voltage value as the driving frequency under varying phase-modulation processes. Detailed experimental results are provided to demonstrate the effectiveness of the proposed driving circuit.
The ultrasonic motor driving system has a strong nonlinearity, with varies with driving conditions and possesses variable deadzone in the control input associated with the phase difference of applied two phase driving voltage. This deadzone is a problem as an accurate positioning actuator for industrial application and it is important to eliminate the deadzone in order to improve the control performance. To overcome the above problems, a new motion control scheme with deadzone estimation and compensation using generalized regression neural network is proposed in this study to improve the control performance of the TWUSM driving system. One of them is to approach the nonlinearity deadzone part of the TWUSM driving system, and another one with extension form is to approach the inverse of nonlinearity deadzone to realize the dynamic compensation. Once the nonlinearity of deadzone is compensated exactly by its inverse compensation, the whole TWUSM driving system can be treated as linear, and the estimated parameters of the linear system can be adopted to design an adaptive controller for a desired reference model.
Based on the estimated linear dynamic model, a traditional feedback linearization method (FLM) is applied in TWUSM driving system. However, in the proposed control system, the lumped uncertainty d(t) is hard to know in practical application. Therefore, a novel disturbance observer (NDO) is adopted as an uncertainty observer in order to adapt the value of the lumped uncertainty, which comprises the imprecise system parameters and external load. The NDO is initially applied to estimate lumped uncertainty d(t), but an observer error does not converge to zero since ≠0, especially . The fuzzy neural network robust compensator (FNNRC) is then presented to assist the conventional feedback linearization method (FLM) control strategy, and adopted to achieve robust stability and robust performance under parametric uncertainty and disturbance variation.
The proposed new learning control scheme, based on a new disturbance observer coupled with a fuzzy neural network robust compensator strategy, has stable on-line learning ability, and is able to maintain high control performance in the presence of disturbance. In addition, it guarantees the stability of closed-loop systems according to the Lyapunov stability theorem. By using the proposed motion control system, the integrated control system possesses the advantages of high-precision tracking performance with robustness to system uncertainties. The effectiveness and robustness of the proposed control system is demonstrated through simulation results and hardware experiments of the TWUSM motion control under the conditions of uncertainties.
Abstract I
Acknowledgement VI
Contents V
List of Tables and Figures VII
Nomenclature XI
Chapter 1 Introduction 1
1.1 Background and motivation 1
1.2 Structure of the dissertation 14
Chapter 2 Modeling of TWUSM Driving System 16
2.1 Literature review 16
2.2 Generalized regression neural network 22
2.3 Identification of the TWUSM model using GRNN 25
2.4 Simulation and model validation 30
2.5 Summary 34
Chapter 3 TWUSM Driver Design 36
3.1 Motivation 36
3.2 Drive circuit design 38
3.2.1 Voltage-controlled oscillator 40
3.2.2 Voltage-controlled phase shifter 41
3.2.3 Voltage-controlled resistance 43
3.2.4 Power amplifiers and transformer 47
3.3 Experimental results 48
3.4 Summary 53
Chapter 4 Robust Control of TWUSM Driving System 55
4.1 Motivation 55
4.2 Problem formulation 58
4.3 Design of nonlinear inverse compensation using 62
GRNN
4.3.1 The weight tuning method of nonlinear 64
inverse compensation
4.3.2 Stability analysis of GRNN deadzone 67
compensation
4.4 Controller design 71
4.5 NDO design for estimation the external 73
disturbance
4.6 Fuzzy neural network robust compensator design 74
4.7 Stability analysis and adaptive laws 77
4.8 Simulation results 79
4.9 Experimental results 88
4.10 Summary 96
Chapter 5 Conclusions 97
References 104
Publish List 113
Curriculum Vitae 115
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