本論文提出了以Hammerstein遞迴類神經網路(Hammerstein recurrent neural network, HRNN)之非線性預測控制(nonlinear model predictive control, NMPC)的結構來控制未知的系統。首先，我們利用HRNN對未知系統予以模式化，HRNN能將未知的動態系統利用狀態空間來表示之，其中包含了一個靜態非線性的子系統串接著一個動態線性的子系統。之後，我們開發一個有效系統的演算法，結合了維度估測、參數的設定與性能的最佳化等方法，利用此演算法可以建構一個精簡但卻能擁有良好效能的HRNN架構。NMPC的原理是建立一個非線性的消除器，功用等同於在靜態非線性模式的逆轉換器，可以消除未知系統中非線性的作用。假如系統的模式鑑別以及經過逆轉換的非線性模式都是精確的，則由未知系統串接非線性消除器的合成模式，其特性會近似於線性的動態系統模式；因此，線性模式預測控制器設計理論便可直接拿來應用，並且達到良好的控制效能。最後，利用有關非線性系統問題的電腦模擬，成功的證實了此論文所提出之控制架構的有效性。

This thesis presents a nonlinear model predictive control (NMPC) scheme based on a Hammerstein recurrent neural network (HRNN) for controlling unknown systems. The unknown system is first modeled by the HRNN that consists of a static nonlinear subsystem cascaded by a dynamic linear subsystem. The HRNN is capable of transferring an unknown dynamic system into a state-space representation. An effective construction algorithm, which integrates the methods of order determination, parameter initialization and performance optimization, is utilized to construct a parsimonious HRNN with a satisfactory performance. The philosophy of our NMPC scheme system is to establish a nonlinearity eliminator that functions as the inverse of the static nonlinear model to remove the nonlinear behavior of the unknown system. If the system modeling and the inverse of the nonlinear model are accurate, the compound model, the unknown system cascaded with the nonlinearity eliminator, will behave like the linear dynamic model. Hence, the theories of linear model predictive controller design can be applied directly to achieve good control performance. Computer simulations on nonlinear system control problems have successfully validated the effectiveness of the proposed control scheme.

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