本論文提出叁種植基於智慧型演算法之適應性模糊控制器設計。首先，引用免疫概念建立以模糊免疫為基礎之演算法以設計控制器，並將此控制器應用至一類混沌系統的控制上，測試此控制器之控制性能與穩定性。其次，利用粒子群演算法(PSO)與具有跳躍機制之模擬退火法(SA)結合的混合搜尋法，名為PSO-SA，以群智慧為基礎之演算法來設計控制器。此法能使粒子群有效地進行全域和局部搜尋，而適當調整模糊歸屬函數，並藉一類非線性系統測試此提方法的可行性。此外，利用本論文所提出之PSO-SA結合新穎慣性因子更新演算法，將其應用在適應性模糊神經控制器設計上，且利用質量彈簧阻尼系統來驗證所提方法之有效性及可行性。
在適應性模糊控制及適應性模糊神經控制方面，利用模糊邏輯系統及模糊類神經系統結合適應律近似受控系統內未知的非線性函數。另外，對於受控系統內之干擾，一個降低干擾之方法被提出，使控制器達到良好控制性能。除此之外，在控制器設計上，利用群智慧演算法將控制器內欲調整參數予以最佳化。最後，利用本文所提出的智慧型演算法所設計之控器，經模擬結果驗證所設計之控制器是有效的。

This dissertation presents three adaptive fuzzy controllers design based on three intelligent algorithms. Firstly, by utilizing the concept of immune algorithm, a fuzzy-immune-based algorithm is proposed for designing a controller. In addition, a class of chaotic systems is illustrated to demonstrate the performance and validity of proposed controller. Secondly, an effective hybrid search algorithm by combining the particle swarm optimization (PSO) with the jumping property of simulated annealing (SA), namely PSOSA, is propounded. Since the swarm intelligence-based optimization algorithm can utilize to find the local and global optimum solution effectively, we adopt to turn the fuzzy membership functions. Besides, the feasibility of the proposed method is illustrated by a class of nonlinear systems. Furthermore, a novel dynamically changing inertia weight algorithm based on search state of PSO-SA is presented. Finally, apply the proposed algorithm to the adaptive fuzzy neural network controller design, and illustrate a mass-spring-damper system to demonstrate the effectiveness of the proposed control scheme.
By examination of the adaptive fuzzy control and adaptive fuzzy neural control, the fuzzy logic systems and fuzzy neural system combine with adaptive laws are adopted to uniformly approximate some unknown nonlinear functions in the system. Besides, a suitable approach is proposed to reduce the interference and improve the control performance. In addition, for controller design, the swarm intelligence algorithm is adopted to adjust the parameters of controller for obtaining the optimum values. Finally, some examples are illustrated the availability of the proposed controller based on the intelligent algorithms.

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