臺灣博碩士論文加值系統

本篇論文旨在提出一種新的感應馬達定位控制方法,此方法採用人工
智慧和間接向量驅動控制(indirect vector control)兩種技術。 控
制器主體在於應用模糊相平面理論(fuzzy phase-plane theory)的設計,
本論文進而發展成五種不同的控制器結構;這些控制器基於模糊相平面理
論的應用,所以皆具寬廣範圍應用的優點,以及比一般模糊控制器擁有更快
的響應能力。其主要原因在於,模糊相平面理論不用繁瑣的專家經驗和解
模糊(defuzzy)程序,所以需求的運算時間較一般模糊控制器為短;在加上
控制信號可表示成連續非線性函數型態,十分有利於最佳化控制設計及穩
定度度分析。關於最佳化問題,我們採用以基因演算法(genetic
algorithms)結合梯度(gradient)理論所發展的演算法則,名為改良型的進
化方向基因演算法(genetic algorithms of evol-utionary direction
operator of improvement type)來完成我們的最佳化問題;這種方法兼具
局部(local)及整體(global)的搜尋能力,不僅明顯提昇基因演算法搜尋最
佳解的效能,而且也減少了搜尋時間的浪費。 本論文所提的人工智慧
技術,經由間接向量驅動系統,模擬感應馬達之最佳定位控制,得到了成功
的印證;從模擬結果顯示,模糊相平面理論配合改良型的進化方向基因演算
法操作,不但具有優異的控制性能,也同時兼具控制器設計的可撓性及簡易
性等優點。

This thesis proposes a novel method using artificial
intelligence and indirectvector-control techniques for position
control of induction motor.The fuzzy phase-plane theory is
employed for the design of the controllers.Five controllers are
proposed, which are able to achieve good performance
underchanges of operating conditions of wide range. The proposed
controllers applythe method of genetic algorithms of
evolutionary direction operator of improvementtype to
requirement of exerting multifarious experiences of expert and
proceduresof defuzziness, the controllers can attain more quick
response than that of theconventonal fuzzy controllers. What is
more, since the output signal of thecontrolls is operated by
continues nonlinear function, it is in favor of thedesign of
optimal control and the analysis of stability.The control
technique using artificial intelligence and indirect vector-
controlwere successfully explored and simulated. Simulation
results show that theapproach is computationally efficient and
has exceedingly good control performance.Furthermore, because of
employing the fuzzy phase-plane technique, no transferfunction
development is required for the implementation of the method.
Thusethe proposed controllers have the advantages of easiness
and flexibility fordesign.