本論文針對永磁式同步馬達(PMSMs)在未知參數及不確定性擾動下，提出各類適應性模糊控制法則。其主要控制目標為設計一適應性模糊控制器來達到抑制PMSM中的混沌現象，且在未知參數與不確定性下，仍能成功地使馬達速度達到指定軌跡。本論文為了達到此控制目標，發展了多種控制方式，包含直接式適應模糊控制、基於滑動模態控制之直接式適應模糊控制與改良型適應模糊控制等。這些控制器在架構上包含兩個部分：模糊類神經控制器與監督式補償控制器。模糊類神經控制器使用模糊類神經網路直接估測存在於PMSM數學模型中的未知非線性模型，實現適應性控制器。另一方面，監督式補償控制器被用於減少模糊類神經網路的估測誤差之影響並確保系統的強健性。藉由李亞普諾夫穩定法則，可確保系統的穩定性，且達到軌跡追蹤誤差漸近收斂到零之效能。此外，所提出的改良型適應模糊控制方法中，所設計之控制器除了達到控制目標外，亦能完整地避免常發生於直接式適應模糊控制中的奇異值問題。最後，利用數值模擬方式，驗證所提出之控制架構可成功地去除PMSM中混沌現象及因未知模型與不確性擾動所造成的不穩定現象，並能迅速的達到軌跡追蹤之目的。

In this dissertation, adaptive fuzzy control methods are proposed for chaotic permanent magnet synchronous motors (PMSMs) subjected unknown parameters and uncertainties. The control objective is to design an adaptive fuzzy controller to suppress chaos in a PMSM and force the motor speed to follow the desired trajectory successfully even with the the existences of the unknown parameters and uncertainties. In order to meet the control objective, various control methods such as direct adaptive fuzzy control, direct adaptive fuzzy control based on sliding mode control and improved adaptive fuzzy control are developed. These controller schemes generally have two parts: fuzzy neural and supervisory controllers. The fuzzy neural controllers use fuzzy neural networks to online estimate the control laws directly or to online estimate the unknown nonlinear models existing in the mathematical models of PMSMs for constructing the control laws. On the other hand, the supervisory controllers are employed to reduce the estimation error effects of the neural networks and ensure the robustness of the systems. By using Lyapunov synthesis approach, the system stability is ensured and the perfect tracking performance with zero convergence of tracking error can be obtained. Moreover, in the improved adaptive fuzzy control method, the designed controller not only meets the control objective but also completely avoids the singularity problem, which usually appears in the direct adaptive fuzzy control method based on sliding mode control. Finally, the numerical simulations are carried out to demonstrate that the proposed control schemes can successfully remove chaotic oscillations and allow the speed to follow the desired trajectory in a chaotic PMSM despite the existences of unknown models and uncertainties.

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