撓性樑常見於各式結構系統中，其低阻尼特性易受外界干擾影響系統效能、甚至影響結構體的穩定與安全性，然使用傳統之主動抑振方法已無法滿足日愈嚴苛的效能與限制要求，因此近來年陸續有許多學者開始應用各種進階控制方法改善撓性樑與結構系統之主動抑振響應。本研究主要探討模型預測控制於撓性樑之抑振效能與其實現結果。本文分別以模型預測控制、包含終端狀態之模型預測控制、結合反覆控制之模型預測控制等控制問題型式進行主動抑振控制器設計，並著重模型預測控制於週期性干擾之抑振效果與特性。為了減少模型預測控制最佳化求解過程中龐大運算量的問題，本文一併將以類神經網路最佳化求解器之快速模型預測控制方法納入探討，並進行撓性樑週期性干擾和脈衝干擾輸入之主動抑振模擬及實驗。除了分析多種設計參數對於撓性樑主動抑振效能之影響外，文中亦針對模型預測控制的重要功能「限制處理」進行一系列結果探討。綜合上述本論文最後歸納幾點參數設計之建議以供設計者日後參考。

As a common example in various kinds of structural systems, flexible beam has the light damping property which easily affects the system performance and structure safety. To compensate for this drawback active vibration control is one proven-effective technique which embeds actuators and sensors into the structures to reduce the influences of disturbances in real-time. Because applying classical active vibration control methods is unable to satisfy the increasingly stringent requirements and constraints, recently researchers have started to use advanced control methods for vibration suppression of flexible structures. This research focuses on the experimental investigation on vibration control of a flexible beam using Model Predictive Control (MPC), an advanced optimal control method which can handle constraints at each sampling step. Using the Hildreth’s optimization solver, the work considers three different MPC strategies, including a basic MPC, a terminal-state involved MPC, and a MPC combined with repetitive control (RMPC), for control design and performance evaluation. The study specifically investigates the periodic disturbance rejection performance of applied MPC methods, showing the effectiveness of RMPC. To reduce the computational burden and improve the practicability of MPC in active vibration control applications, this study also applies a recurrent neural network as fast optimization solver, to implement the aforementioned MPC strategies. Besides the parameter analysis, the experimental results particularly show the constraint handling and performance improvement by considering the input constraints in the MPC design. Finally, this thesis summarizes several concluding remarks on control parameters selection as a guideline for future designers.

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