針對不確定非線性系統使用類神經網路之多目標強健適應觀察器設計

Multiobjective robust adaptive observer design for uncertain nonlinear systems using neural networks

黃麒安(Chi-An Huang)

指導教授 ： 游文雄

大同大學/電機資訊學院/電機工程學系所/碩士(2004年)

摘要

在本篇論文中，我們針對可經由狀態空間轉換成為特殊等效可觀標準形式(observable canonical form)的非線性系統類型，其在具有未知系統參數以及有界干擾下，提出一個包含H2追蹤性能、H∞追蹤性能、以及區域性極點限制多重目標的混合類神經網路消去法則與滑動模式控制行為強健適應觀察器(robust adaptive observer)。此適應類神經網路與滑動模式控制行為分別被用來估測系統的不確定性以及抵銷經由類神經近似所產生近似誤差的影響。我們將具有區域性極點限制的H∞追蹤性能以及具有區域性極點限制的混合H2/H∞追蹤性能的兩個不同目標，以線性矩陣不等(linear matrix inequality)公式發展閉迴路系統穩定之充分條件。在論文的最後，我們將經由兩個模擬的結果來驗證所提出理論的有效性。

關鍵字

觀察器 ； 非線性 ； 適應 ； 線性矩陣不等式 ； 類神經網路

並列摘要

In this thesis, a robust adaptive observer incorporating neural network elimination scheme and sliding-mode control action for multiobjectives including H2 tracking performance, H∞ tracking performance, and regional pole constraints is proposed in some class of single-output nonlinear systems with unknown internal parameters and bounded external disturbances. The nonlinear systems can be transformed by state-space change of coordinates into a special observable canonical form. The adaptive neural networks and the sliding-mode control action are used for plant uncertainty estimates and to eliminate the effect of approximation error via neural network approximation, respectively. The sufficient conditions are developed for different objectives in terms of linear matrix inequality (LMI) formulations, which include H∞ tracking performance with regional pole constraints and mixed H2/H∞ tracking performance with regional pole constraints. Finally, two simulation results show the effectiveness of the proposed scheme.

並列關鍵字

adaptive ； LMI ； neural networks ； nonlinear ； observer

參考文獻

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