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系統識別號 U0026-1701201319451800
論文名稱(中文) 小腦模型控制器之研究及其於壓電致動微運動平台控制之應用
論文名稱(英文) Study on Cerebellar Model Articulation Controller and Its Applications of Piezoelectric Actuated Micro Motion Stage Control
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 101
學期 1
出版年 102
研究生(中文) 溫峻明
研究生(英文) Chun-Ming Wen
學號 n28951170
學位類別 博士
語文別 英文
論文頁數 104頁
口試委員 召集委員-張帆人
口試委員-莊季高
口試委員-江青瓉
口試委員-蔡聖鴻
口試委員-李祖聖
口試委員-莊智清
指導教授-鄭銘揚
中文關鍵字 壓電致動微運動平台  磁滯效應  小腦模型控制器  自我調整學習率 
英文關鍵字 Piezoelectric actuated micro motion stage  cerebellar model articulation controller  hysteresis effect  self-tuning learning rate 
學科別分類
中文摘要 有鑑於壓電致動微運動平台的運動精度和性能會受到其內在非線性磁滯效應與時變參數的影響,本論文提出了三種植基於小腦模型控制器之不同類型控制架構來克服上述缺點。分別是為改善壓電致動視覺光纖對位平台的性能,本論文發展一基於小腦模型的前饋控制器並結合比例積分回授控制器用以消除磁滯效應的影響。而針對運動控制系統和視覺回授系統之間的時間延遲問題,本論文採用多取樣頻率控制策略來克服。為提升雙軸壓電致動微運動平台的追蹤與循跡運動精度,本論文提出一整合型運動控制架構。其中整合性運動控制架構中的模糊小腦模型前饋控制器結合比例積分回饋控制器和學習評論機制是用以改善微運動平台的各軸追踪性能,而模糊小腦模型交叉耦合控制器則用以消除不同軸之間的交叉耦合效應,改善循跡運動精度。為克服傳統小腦模型控制器的缺點,本論文發展出包含遞迴模糊小腦模型控制器、可調整輸入空間量化法和自我調整學習率法的控制架構。在此控制架構中,可調整輸入空間量化法用以解決傳統均分量化法所產生的記憶體大小與學習精度取捨問題。而基於離散型式Lyapunov function的自我調整學習率法可保證本論文所提學習方法可更快速收斂。實驗結果顯示本論文所提之方法能有效降低追蹤與輪廓誤差。
英文摘要 The motion accuracy and performance of piezoelectric actuated micro motion stage is hampered by its inherent hysteresis nonlinearity and time-varying parameters. In order to cope with the aforementioned problems, this dissertation presents three different types of cerebellar model articulation controller (CMAC) based control schemes. Firstly, in order to improve the accuracy of vision-based optical fiber alignment, the CMAC-based feedforward controller is combined with the proportional-integral (PI) feedback controller to eliminate the hysteresis effect. In addition, the multi-rate control paradigm is exploited to overcome the adverse effect due to the time delay between the motion control system and the vision feedback system. Secondly, in order to improve the tracking and contour following accuracy in a dual-axis piezoelectric actuated micro motion stage, this dissertation develops an integrated motion control scheme. In this control scheme, for the motion in each axis of the micro motion stage, a fuzzy CMAC feedforward controller combined with a PI feedback controller and a critic-based learning mechanism (FCMAC-CLM) is used to improve the tracking performance. Moreover, an FCMAC-based cross-coupled controller (FCMAC-CCC) is proposed to deal with the cross-coupling effects between different axes so as to substantially improve the contouring accuracy. Finally, a recurrent fuzzy CMAC (RFCMAC) with adjustable input space and self-tuning learning rate is proposed to overcome the drawbacks of the conventional CMAC control schemes. In particular, the RFCMAC employs an adjustable input space quantization method to avoid the memory size and learning accuracy problem often encountered in equal size quantization approaches. Furthermore, the self-tuning law for the learning rate of the proposed RFCMAC is derived based on the discrete Lyapunov function so that the convergence and faster learning can be guaranteed. Several experiments have been conducted to evaluate the tracking performance and contour accuracy of the proposed approaches. Experimental results validate the effectiveness of the proposed approaches.
論文目次 摘要 ................................................ I
Abstract .................................... ......II
Acknowledgment .................................... IV
Contents ........................................... V
List of Figures .................................. VII
List of Tables ..................................... X
Chapter 1 Introduction ............................. 1
1.1 Motivation ..................................... 1
1.2 Literature Review .............................. 2
1.3 The Cerebellar Model Articulation Controller.... 6
1.4 Contributions of the Dissertation ............. 10
1.5 Organization of the Dissertation .............. 12
Chapter 2 Positioning Accuracy Improvement of a
Vision-based Optical Fiber Alignment Micro Motion
Stage Powered by a Piezoelectric Actuator ......... 13
2.1 Introduction .................................. 13
2.2 Supervised Learning Control Structure Based on CMAC .............................................. 15
2.3 Vision Feedback and Multi-Rate Control ........ 16
2.3.1 Vision Feedback ............................. 16
2.3.2 Multi-Rate Control .......................... 18
2.3.3 Feature Extraction .......................... 19
2.4 Experimental Setup and Results ................ 21
2.4.1 Experimental Setup .......................... 22
2.4.2 Positioning Experiment of Piezoelectric Actuator .......................................... 24
2.4.3 Tracking Control Experiment of Piezoelectric Actuator .......................................... 27
2.4.4 Optical Fiber Alignment Experiment with
Multi-Rate Control ................................ 30
2.5 Summary ....................................... 32
Chapter 3 Contouring Accuracy Improvement of a
Piezoelectric Actuated Micro Motion Stage based on
Fuzzy Cerebellar Model Articulation Controller ........................................ 33
3.1 Introduction .................................. 33
3.2 FCMAC-CLM Position Control Scheme ............. 36
3.2.1 The Conventional CMAC Position Control Scheme ............................................ 36
3.2.2 Description of the Fuzzy CMAC Feedforward Controller ........................................ 37
3.2.3 Modified FCMAC with a Critic-Based Learning Mechanism ......................................... 41
3.3 The Integrated Motion Control Scheme .......... 43
3.4 Experimental Setup and Results ................ 49
3.4.1 Experimental Setup .......................... 49
3.4.2 Tracking Control Experiment of Single-Axis
Micro Motion Stage ................................ 53
3.4.3 Contour Following Experiment of Dual-Axis
Micro Motion Stage ................................ 60
3.5 Summary ....................................... 63
Chapter 4 Development of a Recurrent Fuzzy CMAC
with Adjustable Input Space Quantization and
Self-Tuning Learning Rate for Control of a
Dual-Axis Piezoelectric Actuated Micro Motion Stage 64
4.1 Introduction .................................. 64
4.2 RFCMAC with Adjustable Input Space Quantization
and Self-Tuning Learning Rate .............................................. 66
4.2.1 Adjustable Quantization Method for the RFCMAC ............................................ 67
4.2.2 Description of the Recurrent Fuzzy CMAC .............................................. 69
4.2.3 Self-Tuning Learning Rate and Convergence Analysis .......................................... 71
4.3 Experimental Setup and Results ................ 78
4.3.1 Experimental Setup .......................... 78
4.3.2 Experimental Results ........................ 80
4.4 Summary ....................................... 89
Chapter 5 Conclusions ............................. 91
5.1 Conclusions ................................... 91
5.2 Future Works .................................. 92
References ........................................ 94
Publication List ................................. 103
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