本論文針對三軸音圈馬達定位平台發展具高精密度與強健性之智慧型定位控制系統。在本論文中，首先對所設計之三軸音圈馬達定位平台進行工作原理分析、運動模式討論與數學模型推導，再對平台進行系統鑑別以獲得各項系統參數值。接著，本論文先以滑動模式控制為基礎發展三軸音圈馬達定位控制系統，再以基於非線性滑動平面之終端滑動模式控制改良傳統滑動模式控制不能在有限時間使系統狀態收斂至零的缺點。而為了提高系統之控制精準度，本論文再引入分數階微積分運算，以分數階積分型終端滑動模式控制改善傳統滑動模式控制之位置追隨效果。最後為了確保系統在參數變化、外在干擾與摩擦力等影響下系統均具備強健性，再利用函數鏈結模糊類神經網路估測系統之不確定項，提出智慧型分數階積分終端滑動模式控制，可解決傳統滑動模式控制中切換控制之抖動現象。由於所設計之函數鏈結模糊類神經網路改良了原本模糊類神經網路之架構，並以柴比雪夫正交基底函數作為激發函數，可有效增加函數逼近能力。本論文以數位訊號處理器實現上述控制法則，並設計兩種追隨軌跡與三種控制模式，最後由實驗結果驗證所設計之控制系統確實具備良好之控制精密度與強健性。

This dissertation aimed to design robust and precise control systems for the position control of three-axis voice coil motors (VCMs)-based positioning stage. First, the theoretical principle of the VCM is analyzed. Subsequently, the operation modes and the dynamic model of the stage are introduced. To design the model-based control systems, the system parameters identification is completed in advance. Afterward, a sliding-mode control (SMC) and a terminal SMC (TSMC) are developed to control the stage upon the system stability. Because the finite time convergence of the system state is ensured, the TSMC can improve the control performance of the SMC. Moreover, a fractional order integral TSMC (FITSMC) using fractional operator is developed to perform better transient response compared with the conventional SMC. Furthermore, to improve the robustness of the FITSMC system, an intelligent FITSMC (IFITSMC) with functional-link-based fuzzy neural network (FLFNN) uncertainty estimator is further proposed. The proposed FLFNN is able to improve the nonlinear approximation capability of the conventional fuzzy neural network (FNN) based on the adopted Chebyshev orthogonal polynomial functions.
In this study, all the real-time control systems were implemented via the digital signal processor (DSP). Moreover, two reference trajectories and three test conditions were provided to evaluate the control performances of different control systems. The experimental results demonstrated the effectiveness and validity of the proposed control approaches.

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