模糊比例積分混合微分控制於冰水主機自動化之應用

Fuzzy PI+D Control in HVAC Automation Application

林文奇(Wen-Chi Lin)

指導教授 ： 呂虹慶

大同大學/電機資訊學院/電機工程學系所/碩士(2006年)

摘要

傳統的PID控制方式雖然能使系統獲得良好的精確穩定度，但對於非線性系統的控制或者系統受到外界干擾時，都很難達到設計者預期的結果；反觀模糊控制設計則具有相當大的靈活性。然而模糊控制的規則庫是依據累積的經驗及知識所建立的，因此如果環境或是受控體是過去所沒遭遇的情況，那麼既有的規則庫也將無法滿足實際的需求.基於以上的考量, 我們在這篇論文中，應用一個實作的可程式邏輯控制器( Programmable Logic Controller, PLC) 為例，同時以國際 IEC 61131-7的模糊控制程序標準為依據，在傳統的PID控制器中加入模糊控制的參數，以改善系統在參數不確定性時的追蹤控制功能，進而藉由模糊理論來發展出一種為改善控制響應的模糊邏輯比率混合積分微分控制器 (Fuzzy PI+D)，最後並且完成數據採集與監控系統(SCADA) 的設計，以實現對控制器及模糊控制程序的追蹤及驗證。

關鍵字

PID控制 模糊邏輯 可程式邏輯控制器

並列摘要

Conventional PID control is good for the accuracy of system stability, but it is difficult to reach the demand when the system exists the disturbance or nonlinear phenomenon. On the contrary, fuzzy control has great flexibility although but fuzzy rule base building depends on the experience and knowledge from expertise designer. In this thesis, we develop a fuzzy PI+D for improving control effect according to fuzzy theory. On the strength of the above mentioned, we adopt the practical Programmable Logic Controller (PLC) and use international IEC61131- 7 fuzzy control standard procedure to add fuzzy control parameter in conventional PID. We expect to improve the tracer control function while system parameter is unstable and development a fuzzy PI+D for improving control according to fuzzy theory. At last, we achieve SCADA (Supervisory Control and Data Acquisition) design and accomplish the desired purpose that trace and verify for controller and fuzzy control procedure.

並列關鍵字

Fuzzy PID PLC SCADA

參考文獻

[1]K.T. Erickson, “Programmable logic controllers,” IEEE Trans. on Industry Applications, vol. 15, pp. 14 – 17, 1996.

[2]Y. Simokawa, T. Matsushita, H. Furuno, and Y. Shimanuki, “A high-performance VLSI chip of programmable controller and its language for instrumentation and electric control” IEEE Int. Conf. Tokyo, Japan, vol.2, pp. 884 - 889, 1991.

[5]L. A. Zadeh, ”Fuzzy sets, ” Inform. Contr., vol. 8, pp. 339-353, 1965.

[6]L. A. Zadeh, ”Fuzzy sets as a basis for a theory of possibility, ” Fuzzy Sets and Systems., vol. 1, pp.3-28, 1978.

[11]C. P. Chang, ”Design of a hybrid fuzzy Logic proportional plus integral-derivative controller for a class of nonlinear system” Thesis for Master of Tatung, pp.1-72, 2005.