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題名 | 主動式懸吊系統的灰預測模糊控制=Grey Prediction Fuzzy Control of Active Suspension Systems |
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作者姓名(中文) | 林百福; 連瑞敬; | 書刊名 | 臺北科技大學學報 |
卷期 | 38:1 2005.03[民94.03] |
頁次 | 頁107-121 |
分類號 | 447.11 |
關鍵詞 | 懸吊系統; 模糊控制; 灰預測模糊控制器; Suspension system; Fuzzy control; Grey prediction fuzzy controller; |
語文 | 中文(Chinese) |
中文摘要 | 主動式懸吊系統控制逐漸成為改善汽車路面操控性及乘坐的品質之重要技術。然而,懸吊系統通常有著非線性與不確定動態的特性。設計以模式為基礎的控制器以控制懸吊系統是困難的,因為其數學模式難以正確的被建立出來。因此,本研究應用不需建立數學模式的模糊控制器,以控制懸吊系統,達到主動式懸吊系統控制的目的。但是,傳統模糊控制器在尋找模糊控制規則及適當的隸屬函數有其困難所在。為了解決此問題,將灰色理論演算法導入傳統模糊控制器中,以預測系統下一步的輸出誤差及誤差變化,而不是目前的輸出誤差及誤差變化,作為傳統模糊控制器的輸入變數。此灰預測模糊控制器,不但簡化傳統模糊控制器的設計困難,也方便傳統模糊控制器的實現。此外,儘管使用灰預測模糊控制器能夠壓抑懸吊系統之振動振輻,但是,其輪胎變形量依舊無法有效的減少以改善汽車對路面的操控性。因為輪胎通常會產生彈性變形,而造成彈跳現象,特別是當輪胎旋轉時,此彈跳現象更加的明顯。為了消除此輪胎彈跳的現象,本研究發展補償型灰預測模糊控制器以控制主動式懸吊系統,其應用灰色理論演算法以預測輪胎下一步驟的變形誤差及誤差變化,作為傳統模糊控制器的輸入變數,以設計另一個灰預測模糊控制器去輔助原先以系統下一步輸出誤差及誤差變化所設計的灰預測模糊控制器。為了驗證所提出之智慧型控制器的可用性,本研究設計與構建一個四分之一車油壓伺服懸吊系統,以評估主動式懸吊系統控制的可行性。實驗結果顯示,補償型灰預測模糊控制器在壓抑車體之振動振幅,加速度及輪胎變形量上比單獨使用灰預測模糊控制器有較佳的控制性能。 |
英文摘要 | Active suspension system control is gradually becoming important technology in improving the road holding ability and riding quality of cars. However, suspension systems generally have nonlinear with uncertainty dynamic characteristics. Design a model-based controller for suspension system control is difficult because its mathematical model is hard to accurately establish. Hence, this study employed a model-free fuzzy controller to control the suspension system in order to achieve the object of the active suspension system control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. To solve this problem, a grey-theory algorithm was introduced into the TFC to predict the next output error of the system and the error change, rather than the current output error of the system and the current error change, as input variables of the TFC. This design of grey prediction fuzzy controller (GPFC) cannot only simply the TFC design, but also facilitates implementation of the TFC. Additionally, despite using the GPFC can improve control performance in suppressing vibration amplitude of the suspension system, but the tire deformation still is not effectively reduced for improving road holding ability of cars because the tire will generally cause the elastic deformation; especially, when the tire is rotating, its deformation is very obvious. To eliminate the bounding phenomenon of the tire, this work develops a compensated grey prediction fuzzy controller (CGPFC) for controlling active suspension systems, employing the grey-theory algorithm to predict the next tire deformation as the input variable of the TFC, to design another GPFC to assist the original GPFC according to the next output error of the system and the error change designed. To confirm the applicability of the proposed intelligent controllers, this study designed and constructed a quarter-car hydraulic-servo suspension system for evaluating the feasibility of active suspension system control. The CGPFC achieves better control performance in suppressing vibration amplitude and acceleration for the sprung mass, and reducing the tire deformation than does GPFC only, as indicated in experimental results. |
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