本論文的研究目的是研製以數位訊號處理器為基礎之適應性步階迴歸控制，並利用函數連結放射狀基底函數網路當作不確定性觀測器，用於六相永磁同步馬達定位驅動系統。首先，本研究先推導出六相永磁同步馬達以磁場導向控制的動態模型。接著，將所設計好的步階迴歸控制系統應用於定位驅動系統控制上作馬達轉子機械位置命令的追隨。由於六相永磁同步馬達定位系統上所存在的不確定項是難以預先得到的，因此在實際應用上要設計一有效益之步階迴歸控制系統是很困難的。有鑒於此，本研究提出了適應性步階迴歸控制系統，利用適應律來估測步階迴歸控制系統中的總集不確定項。但之後為了增加六相永磁同步馬達定位驅動系統的強健及精確性，使用函數連結放射狀基底函數網路當作總集不確定項之觀測器，並以補償控制器來消除其最小重建誤差。除此之外，利用李亞普諾夫穩定性理論推導出線上學習演算法，並藉由線上訓練的方式來更新函數連結放射狀基底函數網路之參數。最後，本研究以32位元浮點運算數位訊號處理器TMS320F28335完成所提出之六相永磁同步馬達定位驅動系統，且利用實驗結果來驗證所提出之智慧型適應性步階迴歸控制系統的強健控制成效。

An adaptive backstepping control (ABSC) using a functional link radial
basis function network (FLRBFN) uncertainty observer is proposed in this study
to construct a high-performance six-phase permanent magnet synchronous
motor (PMSM) position servo drive system. The dynamic model of a
field-oriented six-phase PMSM position servo drive is described first. Next, a
backstepping control (BSC) system is designed for the tracking of the position
reference. Since the lumped uncertainty of the six-phase PMSM position servo
drive system is difficult to obtain in advance, it is very difficult to design an
effective BSC for practical applications. Therefore, an ABSC system is
designed using an adaptive law to estimate the required lumped uncertainty in
the BSC system. To further increase the robustness of the six-phase PMSM
position servo drive, an FLRBFN uncertainty observer is proposed to estimate
the lumped uncertainty of the position servo drive with a compensated
controller to eliminate the minimum reconstructed error. In addition, an onl ine
learning algorithm is derived using Lyapunov stability theorem to learn the
parameters of the FLRBFN online. Finally, the proposed position control
system is implemented in a 32-bit floating-point DSP, TMS320F28335. The
effectiveness and robustness of the proposed intelligent ABSC system are
verified by some experimental results.

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