本論文提出一個新的多目標進化演算法基底的數位再設計方法針對具有輸入限制之非線性多輸入多輸出系統尋找一個最佳低增益數位追蹤器。新的多目標進化演算法能有效區別違反限制的程度、縮短收斂的時間並且可以找到較好的數位追蹤器參數。有兩種不同的系統被考慮：i)針對一個未知非線性多輸入多輸出系統，其離散時間的系統矩陣經由觀測/卡爾曼濾波器鑑別方法得到。ii)針對一個已知非線性多輸入多輸出系統，其連續時間的系統矩陣經由最佳線性化方法得到。多目標進化演算法基底的數位再設計方法可以在輸入限制下得到相對大的取樣時間且對應的低增益脈波振幅調變的控制輸入可以有效地轉換到脈波寬度調變。一些說明例子可以證明提出方法的有效性。

This thesis presents a new multi-objective evolutionary programming (MOEP)-based digital redesign scheme to find an optimal low-gain digital tracker of nonlinear MIMO systems under input constraints. The new MOEP is more effective to distinguish the level of violating constraints, reduce the convergent time and find more good parameters of the digital tracker. There are two different systems to be considered. i) For a unknown nonlinear MIMO systems, the discrete-time system matrices are gotten through observer/Kalman filter identification (OKID) method. ii) For a given nonlinear MIMO systems, the continuous-time system matrices are gotten through optimal linearization method. The MOEP-based digital redesign method can obtain the relatively large sampling time under input constraints, and the corresponding constrained control inputs with the low-gain pulse amplitude modulated (PAM), which can be transferred into pulse width modulated (PWM) effectively. Some illustrative examples show the effectiveness of the proposed method.

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