非線性系統之LMI模糊模型預測控制－以類神經網路調整系統之等級函數

模型預測控制亦可稱為移動區間控制，在工業界中是相當受注目的控制策略之一，它的控制概念即在每一個取樣時間，最佳化一個目標函數。然而在大多數的相關研究中，預測控制都局限於處理線性的系統，但在物理系統中有許多都是非線性系統。對於非線性系統，在近幾年中T-S模糊模式已被廣泛的使用，此方法可近似或完整的表示原非線性系統。在本論文中，我們將結合模型預測控制策略與T-S模糊模型方法來處理非線性系統。在設計控制器的過程中，發現模糊控制器的等級函數與系統之穩定性無關，故利用類神經網路調整此等級函數以獲取較快速的暫態響應。此論文另一重點乃介紹所謂虛擬預期變數綜合法來完成輸出追蹤控制。此設計方法的優點可在一個拖車系統的例子中明確地展現出來。雖然拖車系統是一個單輸入控制的系統，但是我們透過單一的控制架構即可控制不同的輸出，因此在不改變控制架構下，我們可適當地切換追蹤輸出值以達成饒富意思的控制任務。最後探討追蹤控制在狀態不可量測的問題，這裡我們知道很多物理系統的模糊集合的歸屬函數是滿足Lipschitz-like的特性。如果滿足前述條件，便可利用分離原理分別設計模糊控制器與模糊估測器，再由線性矩陣不等式求得控制增益及估測增益。在數值模擬方面，以H'enon map和拖車系統為例子來驗證理論的結果。

關鍵字

模型預測控制；模糊追蹤控制；虛擬預期變數； T-S模糊模式；模糊估測器

並列摘要

Model predictive control (MPC) is also known as receding horizon control (RHC) or moving horizon control (MHC). It is the most popular industrial control strategy, based on the idea of optimizing an objective function at each sampling. Although, many physical models are nonlinear, most researches on this issue are limited to linear systems. Recently, the Takagi-Sugeno (T-S) fuzzy approach has been used to model nonlinear systems using the decomposition of a nonlinear system into a set of linear subsystems. In this thesis, we will combine the T-S fuzzy model with the MPC strategy to deal with nonlinear systems. Since the grade functions of the fuzzy controller are independent to the system, in our control design we will update the grade functions via neural networks to achieve the better system performance. In addition, we will discuss the output tracking control based on output feedback design. To this end, the new concept, virtual- desired-variable (VDV) synthesis will be presented. The advantage of using the VDV synthesis is fully illustrated when we consider the example of the truck-trailer system. Although the system is only with a single input, we can control the different outputs via a unified manner. Therefore, we can switch the desired output arbitrarily without changing the control structure. Finally, observer-based control design is proposed to cope with the immeasurable state variables. For the most parts we focus on a common feature held by many physical systems where their membership functions of fuzzy sets satisfy a Lipschitz-like property. Based on this setting, control gains and observer gains can be designed separately. Two different types of systems, H'enon map and truck-trailer systems are considered to demonstrate the design procedure using satisfactory numerical simulation results.

並列關鍵字

fuzzy tracking control ； VDV ； fuzzy observer ； T-S fuzzy model ； model predictive control

參考文獻

[1] E. F. Camacho and C.Bordons, Model Predictive Control," Springer, 2003.

[2] S. Mollov, R. Babuska, J. Abonyi, and H. B. Verbruggen, Effective Optimization for fuzzy model predictive control," IEEE Trans. Fuzzy Syst., Vol. 12, pp.

661-675, 2004.

[3] A. Bemporad and M. Morari, Control of systems integrating logic, dynamics,

[4] E. Granado, W. Colmenares, J.Bernussou and G. Garcia, Linear matrix inequalities

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非線性系統之LMI模糊模型預測控制－以類神經網路調整系統之等級函數

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