使用Takagi-Sugeno模糊磁通估測器之感應馬達適應性向量控制系統設計

Design of Induction Motor Adaptive Vector Control System Using Takagi-Sugeno Fuzzy Flux Estimator.

陳芷棽(Chih-Chen Chen)

指導教授 ： 王順源

國立臺北科技大學/電資學院/電機工程系研究所/碩士(2009年)

摘要

本研究之感應馬達向量控制系統結合直接轉子磁場導向與適應性控制法則，利用LMIs方法求出使系統穩定之估測增益，以T-S模糊估測理論設計一降階型磁通估測器，並將此估測器植入感應馬達向量控制系統中，以精確估測轉子磁通。由於感應馬達在長時間運轉下，定轉子電阻會受內部溫升效應影響造成電阻值變動，因而降低轉子磁通估測器的準確性。因此本研究採用小腦模型PI控制器估測定轉子電阻與轉速，以調適感應馬達的參數，將所估測出的電阻值回授至T-S模糊磁通估測器中調適，使磁通估測不受感應馬達的轉子參數變動影響，令系統更具強健性。本研究並利用小腦模型轉速估測器作轉速估測，將估測之轉速回授至適應性監督式模糊小腦模型速度控制器以達到無轉速量測器向量控制之目的。
經由模擬與實驗結果證明，感應馬達向量控制系統中，植入T-S模糊磁通估測器、適應性小腦模型定轉子電阻估測器與轉速估測器，運轉在2%到100%之額定轉速下，負載為8Nm時轉速皆有優異的動態響應，同時估測馬達狀態在參數變動之環境下仍具有很好的強健性。

關鍵字

Takagi-Sugeno磁通估測器 ； 模型參考適應系統 ； 小腦模型控制器 ； 向量控制

並列摘要

This thesis presents an adaptive pseudo reduced-order T-S fuzzy flux estimator for the induction motor direct field orientation control system. The estimator gain can be obtained by solving a set of linear matrix inequalities (LMIs) to estimate the rotor flux accurately. It’s well known that, due to the changes of temperature, variations of stator and rotor resistances affect the accuracy of rotor flux estimation. To resolve the problem, a cerebellar model articulation PI controller (CMAPIC) is proposed in this thesis to estimate the stator and rotor resistances in the presence of temperature variations. These estimated quantities, including stator and rotor resistances, are taken as the inputs of T-S fuzzy flux estimator. Moreover, the thesis uses a cerebellar model articulation controller to estimate the rotor speed, and the speed is fed back to the adaptive supervisory fuzzy cerebellar model articulation speed controller (AFCMAC) to achieve the sensorless control.
To verify the practicality and effectiveness of the proposed method, experiments are performed under the conditions that the speed command varies from 2% to 100% of rated speed and the system starts up with 8 Nm torque load. The experimental results indicate that the proposed system not only has superior speed dynamic response but also could remain robust in the variations of motor parameters.

並列關鍵字

Takagi-Sugeno Fuzzy Flux Estimator ； Model Reference Adaptive System ； Cerebellar Model Articulation Controller ； Vector Control

參考文獻

[33] 孫敏男，適應性模糊小腦模型控制器於高性能向量控制系統之設計，台北：國立台北科技大學，2008。

[7] K. K. Doowon and J. J. Changwon and R. L. Texas and W. K. Kyungpook, "LMI-based Design of T-S fuzzy controllers using fuzzy estimator," IEEE 1999 38th Proceedings Conference of Decision and Control, vol. 5, 1999, pp. 4343-4348.

[1] J. A. Santisteban and R. M. Stephan, "Vector control methods for induction machines: an overview," IEEE Transactions on Eduction, vol. 44, no. 2, 2001, pp. 170-174.

[2] M. N. Uddin, T. S. Radwan and M. A. Rahman, "Performance of fuzzy logic based indirect vector control for induction motor drive," IEEE Transactions on Industry Applications, vol. 38, no. 5, 2002, pp. 1219-1225.

[3] S. H. Kim, T. S. Park, J. Y. Yoo and G. T. Park, "Speed sensorless vector control of an induction motor using neural network speed estimstion," IEEE Transactions on Industrial Electronics, vol. 48, no. 3, 2001, pp. 609-614.