臺灣博碩士論文加值系統

PID控制器可分成傳統式PID和數位式PID，後者以離散型式當訊號。本文應用粒子群最佳化演算法(Particle Swarm Optimization, PSO)於數位PI控制器的參數調整設計，另外與加權型齊格勒-尼可斯(Ziegler-Nichols, Z-N)演算法PI控制器作比較。PSO相似於基因演算法(Genetic Algorithms, GA)，皆是使用連續疊代方式來最佳化。由於PSO只有尋找最佳解及更新公式比GA之複製、交配、突變步驟簡單，所以在合理的搜尋範圍內PSO尋優最佳值的速度比GA快。
本文以永磁同步馬達為控制受控體，模擬程式採用Matlab軟體內建的M-File檔撰寫模擬程式，由模擬結果證實本文的PSO應用在數位PI控制器調整參數效果比加權型Z-N PI控制器好。

PID controller can be divided into traditional PID and digital PID. The latter is consisted in the time series form. In this paper, applied Particle Swarm Optimization algorithm to parameters tuning of the digital PI controller, and compared with the Weighting Ziegler – Nichols (Z-N) algorithm. Particle Swarm Optimization algorithm uses a continuous iterative method to optimize, as genetic algorithm. Particle Swarm Optimization only processes the optimal solution and refreshing formula. It is easier than those steps of genetic algorithm. So, in the reasonable searching range, the speed of searching optimal solution of Particle Swarm Optimization algorithm is more quick than that of genetic algorithm.
The paper uses permanent magnet synchronous motor as a controlled plant. The simulation program uses Matlab software. Simulation results confirm that the Particle Swarm Optimization applied to parameters tuning of the digital PI controller is better than weighted Z-N PI controller.

摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
符號說明 ix
第一章 序論 1
1.1 研究背景 1
1.2 文獻回顧 2
1.3 論文架構 3
第二章 控制理論背景 5
2.1 傳統PID控制器之基礎理論 5
2.2 新型態數位PID控制器 7
第三章 永磁同步馬達 9
3.1 永磁式馬達基本簡介 9
3.2 永磁同步馬達數學模型推導 11
第四章 參數調整法之理論 15
4.1 齊格勒-尼可斯參數調整法 15
4.2 加權型齊格勒-尼可斯PI/PID參數調整法 16
4.3 粒子群最佳化演算法 17
第五章 模擬結果 29
5.1 應用加權型齊格勒-尼可斯PI控制器 30
5.2 應用粒子群最佳化演算法PI控制器 31
5.3 應用控制器於角度定位效能 33
5.4 均方根誤差方程式 35
第六章 結論與未來展望 36
6.1 結論 36
6.2 未來展望 36
參考文獻 37

1.Gee. D, “The how's and why's of PC based control,” Pulp and Paper Industry Technical Conference, 2001 Conference Record of, p.67-74, (2001).
2.Ziegler J. G. and Nichol N. B.s, “Optimum Settings for Automatic Controller,” Trans. ASME, Vol. 65, pp. 433-444, (1942).
3.Cohen G. H. and G. A. Coon., “Theoretical Investigations of Retarded Control,” Trans ASME, Vol.75, pp.827-834, (1953).
4.Hopfield J. J., “Neural networks and physical systems with emergentcollective computational abilities,” Proceedings of the National Academy ofSciences, Vol. 79, pp. 2554-2558, April.(1982).
5.Faa-Jeng Ling, and Hsin-Jang Shieh, and Po-Huang Shieh, and Po-Hung Shen, “An Adaptive Recurrent-Neural-Network Motion Controller for X-Y Table in CNC Machine,” IEEE Transactions on Systems, Vol. 36, No. 2, Apr. (2006).
6.Jianhua Yang, and Wei Lu, and Wenqi Liu, and Linlin Teng, “Application of Ann PID Controller in District Heating System,” Proceedings of the 6th World Congress on Intelligent Control and Automation, Vol. 2, pp. 6714-6718, Jun. (2006).
7.Zhao, Z. Y. , and Tomizuka, M., and Isaka, S., “Fuzzy gain scheduling of PID
controllers,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23,
No.5, pp. 1392-1398,(1993).
8.Jingwei Xu, and Xin Feng, and Mirafzal, B., and Demerdash, N.A., “Application of Optimal Fuzzy PID Controller Design: PI Control for Nonlinear Induction Motor,” Proceedings of the 6th World Congress on Intelligent Control and Automation, Vol. 1, pp. 3953-3957, Jun. (2006).
9.葉智宇，以基因演算法設計數位PID控制器且應用於X-Y平台定位控制，碩士論文，龍華科技大學，（2008）。
10.王修平、葉智宇，極點配置法應用於直流馬達之數位PI控制器參數調整，中華民國第二十八屆電力工程研討會，高雄，(2007)。
11.Jong-Hwan Kim, and Hyun Myung “Evolutionary programming techniques for constrained optimization problems;” IEEE Transactions on Evolutionary Computation, Volume 1, Issue 2, pp 129-40, July (1997).
12.Qing Zhou, and Yanda Li “Directed variation in evolution strategies,” IEEE Transactions on Evolutionary Computation, Volume 7, Issue 4 pp 356-66, Aug. (2003).
13.Bandyopadhyay, S. “Simulated annealing using a reversible jump Markov chain Monte Carlo algorithm for fuzzy clustering;” IEEE Transactions on Knowledge and Data Engineering,Volume 17, Issue 4, pp 479 -490, April. (2005).
14.Whei-Min Lin, and Fu-Sheng Cheng, and Ming-Tong Tsay; “An improved tabu search for economic dispatch with multiple minima,” IEEE Transactions on Power Systems, Volume 17, Issue 1, pp 108-112, Feb. (2002).
15.Harmer P.K., and Williams P.D., and Gunsch G.H., and Lamont G.B., “An artificial immune system architecture for computer security applications,” IEEE Transactions on Evolutionary Computation, Volume 6, Issue 3, pp 252-280, June. (2002).
16.Parsopoulos K.E., and Vrahatis M.N., “On the computation of all global minimizers through particle swarm optimization;” IEEE Transactions on Evolutionary Computation, Volume 8, Issue 3, pp 211 -224, June. (2004).
17.Merkle D., and Middendorf M., and Schmeck H., “Ant colony optimization for resource-constrained project scheduling;” IEEE Transactions on Evolutionary Computation, Volume 6, Issue 4, pp 333 -346, Aug. (2002).
18.Abbass H.A., “MBO: marriage in honey bees optimization－a Haplometrosis polygynous swarming approach;” 2001. Proceedings of the 2001 Congress on Evolutionary Computation, Volume 1, pp 207 – 214, May. (2001).
19.Eberhart, R.C., and Xiaohui Hu, “Human Tremor Analysis Using Particle Swarm
Optimization,” Congress on Evolutionary Computation, Vol.3, pp.1927-1930, (1999).
20.Yoshida, H., and Kawata, K., and Fukuyama, Y., and Takayama, S., and Nakanishi, Y., “A particle swarm optimization for reactive power and voltage control considering voltage security assessment,” IEEE Transactions on Power Systems, Vol.15, pp.1232-1239, (2000).
21.Yijian Liu, Jianming Zhang, Shuqing Wang, “Optimization design based on PSO algorithm for PID controller,” Proceedings of the 5th World Congress on Intelligent Control and Automation, Vol.3, pp.2419-2422, (2004).
22.Minorsky N., “Directional Stability of Automatically Steered Body,”J.Amer.Soc.Naval Engineers, 42(2), pp.280-309, (1992).
23.Callender A., and Hartree D. R., and Porter A., “Time-Lag in a Control System,” Phil. Trans. Roy. Soc. London, pp. 415-444, (1936).
24.張道弘，“PID 控制理論與實務”，全華科技圖書股份有限公司，(1995)。
25.蔡貴夙，使用粒子群聚演算法最佳化PID控制器，碩士論文，龍華科技大學，(2005)。
26.H. P. Huang and C. H. Lin, “Applying LQR Strategy to Discrete-Time PID Controller Design,”中國機械工程學會第二十二屆全國學術研討會論文集, 論文編號: B1-034, (2005) 。
27.盟立自動化股份有限公司，AC伺服系統MSDA系列安裝、操作手冊
28.Nasar, S. A., and Boldea, I., “Linear Electric Motors Theory, Design,and Practical Applications”, New Jersey: Prentice-Hall Inc., (1987).
29.Boldea, I., and Nasar, S. A., “Linear Electric Actuators and Generators.London,” U. K., Cambridge University Press, (1997).
30.廖福聲，全電式塑膠射出成型機控制系統設計與研製，碩士論文，國立中興大學，（2002）。
31.劉昌煥，AC伺服系統的理論與設計實務，文笙書局，(1992)。
32.Macia N. F., and Thaler G. J., “Modeling and Control of Dynamic Systems”, Thomson Delmar Learning: New York, 2005.
33.Hang C.C., and Astrom K.J., and Ho W. K., “Refinement of the Ziehler-Nichols tuning formula,” EEE Proc.Control Theory Appl., Vol. 138, no. 2,pp. 111-118,(1991).
34.Basilio J. C. , an Matos S. R., “Design of PI and PID Controllers With Transient Performance Specification,” IEEE Trans. on Educ., Vol. 45, no. 4, pp. 364-370, (2002).
35.Smith A.E., “Swarm Intelligence: From Natural to Artificial Systems” IEEE Transactions on Evolutionary Computation, Volume 6, Issue 2, pp 192-193, Aug. (2002).
36.Dorigo M., and Di Caro, G.,“Ant Colony Optimation: A New Meta-Heuristic,”Congress on Evolutionary Computation, Vol.2, pp.1470-1477, (1999).
37.吳信璋，遞減式螞蟻族群演算法在PID控制參數調整之研究，碩士論文，國立高雄海洋科技大學，(2006)。
38.鄭立奇，輔以粒子群最佳化於適應性模糊小腦控制器設計，碩士論文，銘傳大學，(2009)。
39.Kennedy, J., and Eberhart, R. C., “Swarm Intelligence,” Morgan Kaufmann Press, (2001).
40.周鵬程，遺傳演算法原理與應用－活用MATLAB，全華科技圖書股份有限公司，(2002)。