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研究生:黃勤益
研究生(外文):Huang, Chini
論文名稱:以FPGA為基礎整合軟運算技術之強健適應控制系統設計於線性感應馬達效能改善
論文名稱(外文):FPGA-Based Robust Adaptive Control System Design Integrated with Soft-Computing Approach for the Performance Improvement of Linear Induction Motors
指導教授:蔣欣翰徐國政徐國政引用關係
指導教授(外文):Chiang, HsinhanHsu, Koucheng
口試委員:蔣欣翰徐國政陳彥霖
口試委員(外文):Chiang, HsinhanHsu, KouchengChen, Yenlin
口試日期:2012-07-06
學位類別:碩士
校院名稱:輔仁大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:80
中文關鍵詞:步階控制適應性控制軟運算技術最佳化位置追隨線型感應馬達
外文關鍵詞:Backstepping controladaptive controlsoft-computingoptimizationposition trackinglinear induction motors
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本論文主旨在考慮感應馬達端點效應與不確定量干擾下發展出適應性速度/位置追隨控制系統。首先推導出間接磁場導向線型感應馬達動態模型,以步階控制法為基礎,設計出補償週期性參考軌跡追隨控制之集成不確定量模糊邊界的模糊順滑模態控制器。因實際應用時難以得到集成不確定量的邊界,故藉由Lyapunov穩定性理論基礎所推導的適應性調整器,以即時動態調整控制器參數,並能抑制干擾與不確定量等因素影響。此外系統因不確定性干擾影響難以發展精確的數學模型,導致在控制器參數調整上實為一複雜問題。因而結合適應性模糊類神經推論系統與基因演算法的特性,使控制器參數達到最佳化。運用適應性模糊類神經推論系統與基因演算法的完整程序,從控制器參數的調整到設計目標函數之建模,最後決定出最佳化之控制器增益值。透過數個模擬與實驗結果顯示,經過最佳化程序能提昇控制器效能與強健性,也驗證本論文所提出的控制架構設計於馬達系統的有效性。
In this thesis, a robust adaptive speed/position tracking control system is proposed for a linear induction motor (LIM) taking into account the longitudinal end effects and uncertainties including the friction force. The dynamic mathematical model of an indirect field-oriented LIM drive is firstly derived for controlling the LIM. On the basis of a backstepping control law, a sliding mode controller (SMC) with embedded fuzzy boundary layer is designed to compensate the lumped uncertainties during the tracking control of periodic reference trajectories. Since it is difficult to obtain the bound of lumped uncertainties in advance in practical applications, an adaptive tuner based on the sense of Lyapunov stability theorem is derived to adjust the controller parameters in real-time. Furthermore, it also confronts the increasing disturbance and uncertainties. In addition, it is a quite complicated process of parameter tuning for the proposed controller due to the difficulty arisen from lacking of the accurate mathematical model of a system accompanied with unknown disturbance. The combination of the adaptive neural fuzzy inference system (ANFIS) and genetic algorithm (GA) is adopted to optimize the controller parameters. The whole process of using ANFIS and GA is applied to tune the controller parameters, model the designed objective function, and determine the optimized control gain, in sequence. The effectiveness of the proposed control scheme is validated through simulations and experiments for several scenarios. In addition, the advantages of performance improvement and robustness are illustrated at the end of the optimization procedure.
Abstract (In Chinese) i
Abstract ii
Acknowledge iii
Contents iv
List of Tables vi
List of Figures vii
Nomenclature x
Chapter 1. Introduction 1
1.1. Motivation 1
1.2. Survey of Previous Work 2
1.3. Main Task and Organization 5
Chapter 2. System Description 7
2.1. Introduction to Liner Induction Motors 7
2.2. Dynamic Model of a LIM Considering End Effects 9
2.3. The Overview of Developing Control System 13
2.3.1. Background to Develop System Structure 13
2.3.2. Description for a Closed Loop System 14
Chapter 3. Adaptive Fuzzy Sliding Mode Control System 16
3.1. Sliding Mode Control 16
3.1.1. SMC Design 16
3.2. Fuzzy Logic Control 19
3.2.1. FSMC Design 20
3.3. AFSMC Design 23
Chapter 4. Parameters Optimization 26
4.1. Adaptive Neural Fuzzy Inference System 27
4.1.1. ANFIS Architecture 28
4.1.2. Procedure of ANFIS 32
4.2. Genetic Algorithm 35
4.2.1. Procedure of Genetic Algorithm 36
Chapter 5. Simulation and Experimental Results 42
5.1. Introduction to Experiment Equipments 43
5.1.1. Embedded Control Board 43
5.1.2. Decoding Function throughout FPGA Module 45
5.1.3. Implementation of Control Algorithm on Real-Time System 47
5.2. Simulation Results 49
5.2.1. Sinusoidal Command Tracking 49
5.2.2. Triangular Command Tracking 54
5.3. Experimental Results 58
5.3.1. Sinusoidal Command Tracking 58
5.3.2. Triangular Command Tracking 67
Chapter 6. Conclusions and Future Works 75
6.1. Conclusions 75
6.2. Future Works 75
References 77

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