臺灣博碩士論文加值系統

比例積分控制器為現階段工業界慣用之控制器，在各種不同的控制環境下，如何調變比例與積分兩項參數用以滿足特定之所需條件(如：最大超越量、上升時間、安定時間、穩態誤差等)，一直是控制工程師所追求的一項開放性問題。本篇論文架構在定能量輸入能維持定輸出之系統下，先從能量的觀點重新詮釋比例積分控制器，進而提出一套能滿足特定條件下之自調式標準流程，並使用本文所提出之梯度二分搜尋法最佳化比例增益與積分增益。而後，分別透過兩大典型系統(溫控系統與伺服馬達速度控制系統)，對提出之調整方法，進行模擬驗證：以溫控系統為例，採用耦合效應嚴重與具長延遲時間特性之射出成型機溫控模組進行驗證；而伺服馬達速度控制系統則分別對有無電流控制迴路進行比較，模擬結果顯示，本文所提出之自調式調整法，具有高度可靠性與實現性。最末，再透過一伺服馬達速度控制實驗，證實本篇所提之調整法確實可達成一最佳化之自調式PI控制器。

A Proportional-Integral (PI) controller is an important instrument in industry control application. In control engineering field, to find out the optimal PI parameters by observing system response is very important, especially when system model is difficult to identify exactly. Therefore, this paper proposes a self-tuning methodology with a proposed Gradient Bisection Algorithm (GBA) to achieve finding the optimal PI parameters in fixed-input sustaining fixed-output (FIFO) system. The whole tuning tactic is constructed under desired constraints (such as overshoot, occurrence times of oscillation etc) with the minimum integral absolute error (IAE). Two kinds of typical FIFO system which are thermal system and servo motor velocity control system have simulated in this paper. Besides, a servo motor velocity control experiment has also been realized. The simulation and experiment results verify that our proposed optimal self tuning methodology is a reliable and practicable way to searching the optimal PI gains under desired indexes.

摘　要 i
ABSTRACT ii
Content iii
Table Content v
Figure Content vi
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Literature Survey 2
1.2.1 Ziegler-Nichols method 2
1.2.2 Internal Mode Control (IMC) 4
1.2.3 Iterative Feedback Tuning (IFT) 6
1.3 Distribution and Outline 9
Chapter 2 11
The PI Controller with Energy Respect 11
2.1 Fundamental structure of PID Controller 11
2.2 Basic PID Effect 13
2.3 PI Control with Energy Respect 19
Chapter 3 23
Optimal Tuning Methodology by Gradient Bisection Algorithm 23
3.1 Design Index 23
3.2 Gradient Method and Cost Function 25
3.3 Gradient Bisection Algorithm (GBA) 27
3.4 Optimal PI Tuning Methodology 31
Chapter 4 Simulation & Experiment Result 37
4.1 Injection Machine Model 37
4.1.1 Single Control Volume Simulation 40
4.1.2 Injection Machine Simulation 44
4.2 Motor Model 47
4.2.1 Motor 2nd Order Model with Current Loop 47
4.2.2 Motor 2nd Order Model without Current Loop 49
4.3 Experiment of Motor Velocity Control 52
Chapter 5 Conclusion 54
References 55

[1] F. Johannaber, Injection Molding Machines: a user’s guide, New York, Hanser, 1994.
[2] H. Havlicsek, A. Alleyne, “Nonlinear control of an electrohydraulic injection molding machine via iterative adaptive learning”, IEEE/ASME Transactions on Mechatronics, Vol. 4, No3, 1999, pp.312-323.
[3] M. S. Wang, C. M. Chang, “DSP-based adaptive fuzzy velocity/pressure control of injection molding machines”, Journal of Chinese Institute of Engineer, Vol. 30, No.5, 2007, pp. 819-827.
[4] K. J. Wstrom and T. Hagglund, PID Controllers: Theory, Design and Tuning, 2nd, Instrument Society of America, NC, 1995.
[5] K. Ibrahim, “IMC based automatic tuning method for PID controllers in a Smith predictor configuration”, Computers and Chemical Engineering, Vol. 28, No3, 2004, pp. 281-290.
[6] R. Vilanova, “IMC based Robust PID design: Tuning guidelines and automatic tuning”, Journal of Process Control, Vol. 18, 2008, pp. 61-70.
[7] Q. G. Wang, C. C. Hang, X. P. Yang, “Single-loop controller design via IMC principles”, Automatica, Vol.37, 2001, pp. 2041-2048.
[8] H. Hjalmarsson, M. Gevers, S. Gunnarsoon, O. Lequlin, “Iterative feedback tuning: Theory and application”, Control Systems Magazine IEEE, Vol. 18, 1998, pp. 26-41.
[9] O. Lequin, M. Gevers, M. Mossberg, E. Bosmans, L. Triest, “Iterative feedback tuning of PID parameters: Comparison with classical tuning rules”, Control Engineering Practice, Vol. 11, 2003, pp. 1023-1033.
[10] F. L. Lin, H. J. Shieh, K. K. Shyu, P. K. Huang, “On-line gain-tuning IP controller using real-coded genetic algorithm”, Electrical Power Systems Research, Vol. 72, No. 2, 2004, pp. 157-169.
[11] C. T. Lin, C. S. G. Lee, Neural Fuzzy Systems: A Neural-Fuzzy Synergism to Intelligent Systems, Prentice-Hall, NJ, Englewood Cliffs, 1996.
[12] C. F. Juang, “An automatic building approach to special Takagi-Sugeno fuzzy network for unknown plant modeling and stable control”, Asian Journal of Control, Vol 5, No 2, 2003, pp. 176–186.
[13] H. C. W. Lau, T. T. Wong, K. F. Pun, “Neural-fuzzy modeling of plastic injection molding machine for intelligent control”, Expert Systems with Applications, Vol.17, 1999, pp. 33-43.
[14] H. K. Khalil, Nonlinear Systems, 2nd, New Jersey, Prentice-Hall, 1996.
[15] C. C. Tsong, Linear System Theory and Design, 3nd, New York, Oxford University Press, 1999.