本文討論的範疇主要可分為兩大部分，分別是結合類神經求解器之模型預測控制(Neunal Network Based Model Predictive Control)研究，以及模型預測控制於主動抑振平台的相關研究。自80年代以來，模型預測控制因為其能夠處理多變數問題以及限制問題的能力而相當受到歡迎，但受限於其運算過程的複雜度，使得其一直難以實現於較高取樣率的機電伺服平台，儘管現今微處理器計算能力提升之後已增加了其實現於機電伺服系統的可能性；但是卻仍然容易因為過大的計算量而使實現上取樣率低落的問題產生。因此為了簡化其演算過程，並且持續保有主要特性(如多變數及限制問題的處理能力)，許多最佳化領域的專家學者也開始紛紛投入發展快速模型預測控制理論(Fast Model Predictive Control)的行列，本文的第一部分即為參考快速模型預測控制理論的其中一種，使用較易實現的?簡化雙層類神經網路?來進行最佳化問題的求解，並討論其對於控制器計算量縮減的程度。在主動抑振方面的研究部分，早在80、90年代模型預測控制應用於化工產業時期，就已經有許多干擾抑制方面的研究出現，主要是將干擾估測器及干擾訊號的內部模型結合於其中，並於最佳化過程當中同時考慮受控系統之最終狀態，使其獲得干擾抑制的能力；但是由於此種作法在機電伺服系統的干擾抑制研究卻相對少見，因此本文的第二部分即為使用以往化工產業之干擾抑制方法，並將此具有干擾抑制能力的模型預測控制應用於主動抑振平台，以驗證其抑制能力。

Model Predictive Control (MPC) is an advanced optimal control method which can effectively deal with multi-variable process control systems with constraints. Since past several decades this technique has been very popular in the chemical process control community but is rarely seen in mechatronics research field due to its inherently complicated optimization process and limited computational resources. Recently the advance of microprocessor technology has provided a potential solution to release this constraint and made implementation of MPC on fast dynamic systems a possible task. However, as the interested system becomes complicated with more constraints, purely relying on using extremely fast and expensive hardware for MPC implementation may not be a practical solution for general engineers. In light of this, developing MPC algorithms such that the optimization process can be streamlined with reduced computational burden as much as possible, namely Fast MPC technique, has drawn much attention from researchers in this sense. The first part of thesis is to evaluate one Fast MPC method using a recurrent neural network developed by Prof. Jun Wang in the Chinese University of Hong Kong. The method is applied as an optimizer for Repetitive Model Repetitive Control (RMPC), an MPC algorithm which contains a repetitive controller for periodic signal tracking or rejection. Experimental results on a piezo-acutated system for tracking control are provided. The second part of thesis is to investigate the applicability of MPC/RMPC on vibration control of active structures. Some initial results by an experimental study are provided with comments.

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