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  • 學位論文

感應馬達驅動系統之適應性對角線遞迴小腦模型控制器設計

Design of Adaptive Diagonal Recurrent Cerebellar Model Articulation Controller for Induction Motor Drive Systems

指導教授 : 王順源
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摘要


本研究旨在設計對角線遞迴小腦模型控制器(diagonal recurrent cerebellar model articulation controller, DRCMAC),並分別植入感應馬達向量控制系統與直接轉矩控制系統中作速度控制。此對角線遞迴小腦模型控制器一樣具有小腦模型控制器(cerebellar model articulation controller, CMAC)之快速學習、結構簡單、線上訓練和非線性之學習能力等優點,再加上高斯基底函數與遞迴概念,即具備有良好的動態響應。由於對角線遞迴小腦模型,是將高斯基底函數引入小腦模型控制器的一種控制方式,因此它的輸入層的值可以是0到1之間的實數,如此便可提高其系統誤差收斂的準確度。再者,將根據對角線遞迴類神經(diagonal recurrent neural network, DRNN)的架構修改了CMAC,使原本靜態的CMAC具有動態特性。最後,其權重記憶體會根據適應法則而進行線上調適,並透過Lyapunov理論來確保系統之穩定性。 在向量控制系統架構的參數估測方面,本研究利用基於適應性參考模型系統(model reference adaptive system, MRAS)之適應性轉子磁通估測器架構,來建立適應性轉速估測器與適應性轉子電阻估測器,並將其應用於向量控制系統中,並透過模擬與實驗來證明本控制器對於參數變動與外加負載變動影響之強健性。 在直接轉矩控制系統架構的參數估測方面,本研究將參考模型適應系統理論和模糊控制理論結合設計出模糊定子電阻估測器,即時調適定子電阻值,以準確估測磁通量。並將其應用於直接轉矩控制驅動系統中,透過模擬與實驗來證明本控制器對於參數變動與外加負載變動影響之強健性。 最後,將適應性對角線遞迴小腦模型控制器、適應性模糊小腦模型控制器控制的結果做比較,並以均方根誤差作為性能評估指標。經由模擬與實驗結果證明,本文控制器的轉速不但能快速響應且性能優於適應性模糊小腦模型控制器,同時在馬達參數變動及加入外部負載擾動下仍具有很好的強健性。

並列摘要


The purpose of this thesis was designed a diagonal recurrent cerebellar model articulation controller (DRCMAC) that was embedded into the induction motor vector control system and direct torque control system to control speed. The DRCMAC exhibits the same rapid learning convergence as the cerebellar model articulation controller (CMAC) does. Moreover, the DRCMAC features a simple structure and can be used for online training and nonlinear learning. With the aid of Gaussian basis functions and recursion, the DRCMAC provides a good dynamic response. DRCMAC is a controlling method that introduces the Gaussian basis function into a CMAC; therefore, the value of the input layer can be an arbitrary real number in the interval between 0 and 1, thus increasing the convergence accuracy of a system. Furthermore, a diagonal recurrent neural network structure was used to modify the CMAC and convert the originally static CMAC into a dynamic controller. Finally, the connective weight memory was adjusted online according to the adaptive rules described in Lyapunov stability theory for ensuring system stability. Regarding estimating its parameters with regard to its vector control system structure, an adaptive speed observer and rotor resistance observer were designed using the structure of the model reference adaptive system (MRAS). The rotor speed observer and the rotor resistance observer were integrated and implemented in a field-oriented control (FOC) induction motor drive. The robustness of the proposed adaptive DRCMAC (ADRCMAC) to parameter variation and external load torque disturbance was verified by performing a simulation and an experiment. Regarding estimating the parameters with regard to the direct torque control system structure, a MRAS and fuzzy control theory were applied in designing a fuzzy stator resistance estimator (FSRE) to adjust the stator resistance value immediately in order to estimate its flux linkage accurately. The rotor speed observer, and the rotor resistance observer were integrated and implemented in a direct torque control (DTC) induction motor drive. The robustness of the proposed ADRCMAC against parameter variation and external load torque disturbance was verified by performing a simulation and an experiment. Finally, the ADRCMAC was compared an adaptive fuzzy CMAC (AFCMAC), and the root mean square error was used as the index for evaluating of the ADRCMAC. In addition, a simulation and an experiment were conducted to prove that the controllers proposed in this thesis can respond quickly and outperform an AFCMAC. The ADRCMAC exhibited greater robustness to parameter variation and external load torque disturbance than did the AFCMAC.

參考文獻


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