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系統識別號 U0026-0808201723180000
論文名稱(中文) 智能控制與系統建模於電磁感應熱療之研究
論文名稱(英文) Research on Intelligent Control and System Modeling to the Electromagnetic Induction Hyperthermia
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 105
學期 2
出版年 106
研究生(中文) 王瑋成
研究生(英文) Wei-Cheng Wang
學號 n28004101
學位類別 博士
語文別 英文
論文頁數 105頁
口試委員 召集委員-陳添智
口試委員-黃世杰
口試委員-白富升
口試委員-鄭銘揚
口試委員-林志隆
指導教授-戴政祺
中文關鍵字 電磁感應  熱治療  有限元素法  自適應網絡模糊推理系統  遞歸神經網絡  模糊邏輯控制  溫度控制器  模糊模型參考學習控制  自學習模糊控制 
英文關鍵字 electromagnetic induction  hyperthermia  finite element method (FEM)  adaptive network fuzzy inference system (ANFIS)  recurrent neural network (RNN)  fuzzy logic control (FLC)  temperature controller  fuzzy model reference learning control (FMRLC)  self-learning FLC 
學科別分類
中文摘要 本文的目的主要是開發溫度控制器和系統建模,用於預測與控制電磁感應熱療系統的組織溫度響應。當電磁感應熱療應用於深層組織時,存在著兩個關鍵的挑戰,即溫度難以被精確測量,並且溫度控制易受干擾由於不可預測的受控體參數變化。有限元素法適用於電磁場和熱傳遞的耦合分析,可用於預測磁性材料植入於深層組織中的溫度分佈。為提高有限元素法模型的準確性,自適應網絡模糊推理系統模型被實現用於改善有限元素法模型的準確性。此模型的建立基於有限元元素法模型產生的模擬數據,以及真實實驗的量測數據。這樣的模型可以提供大量的測試數據,去優化有限元素法模型中的參數,並且搭配本文所提出優化程序可以用於加快優化過程。此外,電磁感應熱療的系統辨識採用遞歸神經網絡模型進行分析,以近似在各種實驗條件下的溫度過程。由模擬結果可證明,儘管電流的大小或組織的深淺不同,所提出的優化程序皆能協助有限元元素法模型去準確地估算組織的溫度響應。
閉環控制器被用於追踪參考模型以保證所期望的溫度響應。電磁感應熱療系統產生交變磁場用以加熱高磁導率材料。這種無線感應的加熱方式廣泛地應用於癌症治療中,且幾乎沒有副作用。熱療的功效強烈依賴於溫度的精確控制。然而,在處理過程中,控制性能由於嚴重的擾動和參數變化而惡化。在本研究中,實現了一種具有改良的增益調整機制的自學習模糊邏輯控制器,以獲得高控制性能在更廣泛的熱治療情況下。此方法的實現藉由適當地改變模糊逆模型的輸出縮放因子去調整控制規則。所提出的自學習模糊邏輯控制器與經典自調整模糊邏輯控制器,以及模糊模型參考學習控制進行控制性能的比較。另外,通過體外豬肝的實驗,去測試所提出的自學習模糊邏輯控制器的控制性能與負載自適應能力。實驗結果表明,該控制器在電磁感應熱療系統的溫度控制方面表現出較好的強健性和優秀的適應性。
英文摘要 The aim of this dissertation involved developing a temperature controller and system modeling for predicting the temperature response of tissues in electromagnetic induction hyperthermia (EIH). There are two critical challenges when applied to deep-seated tissue using EIH, i.e., the temperature may not be measured accurately and the temperature control is susceptible to disturbance due to unpredictable plant parameter variations. The finite element method (FEM) was suitable for coupled analysis with electromagnetic fields and heat transfer, which could be used to predict temperature profiles in deep tissue implanted with magnetic materials. To improve the accuracy of the FEM model, an adaptive network fuzzy inference system (ANFIS) model was implemented on the basis of measured data and simulated data, which were generated by the FEM model. The ANFIS model can provide a large number of testing data to optimize the parameters in the FEM model, and it can be used to expedite the optimization process. Moreover, system identification for EIH was analysed and selected with recurrent neural networks models to approximate various conditions of the temperature process. The recurrent neural network was useful for establishing the temperature prediction model with the advantages of simple design and stable efficacy.
A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The EIH system generates an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating has few side effects when it is extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with gain tuning mechanism was implemented to obtain high control performance in a wider range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. The proposed SLFLC was compared to the classical self-tuning fuzzy logic controller (STFLC) and fuzzy model reference learning control (FMRLC). Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicate that the proposed controller shows greater robustness and excellent adaptability with respect to temperature control of the EIH system.
論文目次 摘要 i
Abstract iii
Acknowledgement v
Table of Contents vi
List of Tables viii
List of Figures ix
Chapter 1 Introduction 1
1.1. Background 1
1.2. Motivation 3
1.3. Dissertation Organization 6
Chapter 2 Modeling of Electromagnetic Induction Hyperthermia for temperature Prediction and Optimization 8
2.1. Introduction 8
2.2. Analytical and Numerical Methods 8
2.3. Modeling Based on Finite Element Methods 15
2.4. Adaptive Network Fuzzy Inference System 21
2.5. Optimization Methods Using Genetic Algorithms 24
2.6. Experimental Results and Discussion 26
2.7. Summary 31
Chapter 3 System Identification using Neural Network for Electromagnetic Thermotherapy Systems 32
3.1. Introduction 32
3.2. Modeling Based on Neural Networks 33
3.3. Elman Neural Network 34
3.4. Experimental Results and Discussion 36
3.5. Summary 41
Chapter 4 Temperature Controller for Magnetic Induction Hyperthermia Using PID and Fuzzy Logic Control 42
4.1. Introduction 42
4.2. Inverter with Transformer 42
4.3. Control of COMSOL using MATLAB 47
4.4. PID Control 48
4.5. Fuzzy Logic Control 51
4.6. Simulation and Experimental Results 52
4.7. Summary 60
Chapter 5 Temperature Controller for Electromagnetic Induction Hyperthermia Using Self- Tuning and Learning Fuzzy Logic Control 62
5.1. Introduction 62
5.2. Equivalent Treatment Time Calculation 62
5.3. Self-Tuning Fuzzy Logic Control 64
5.4. Proposed Self-Learning Fuzzy Logic Control 69
5.5. Experiment System 77
5.6. Experimental Results and Discussion 80
5.7. Summary 92
Chapter 6 Conclusions and Future Works 93
6.1. Conclusions 93
6.2. Future Works 94
Reference 95
Publication List 103
Vita 105

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