連結車的倒車問題經常困擾著剛剛開始嘗試倒車的駕駛，當他們由向前開車轉換成向後開車時經常容易搞混方向盤的轉向。除了這些駕駛人外，一些從事智慧開車系統開發的研究人員也會對這樣的停車問題感到興趣，因為他們經常需要面對類似的問題。停車控制器的性能往往受限於可以倒車的空間，因為倒車系統限制了車輛只能向後行駛。為了解決設計上的困難許多改良的方法相繼被提出， 但卻相對少有小波理論專注在這方面的研究。

小波理論在最近幾年的應用越來越廣泛，特別是在影像處理與信號處理的領域。小波理論不但具有分析頻域和時域上的特性，還同時具有多解析度分析的優點，使的非穩態的信號透過小波轉換後的處理變的更加容易。本篇論文實現一個使用小波轉換的模糊控制器，其實驗結果可以提供給日後相關研究作為參考。

本篇論文實現一小波轉換模糊控制器以改善連結車的倒車問題。 當連結車進行轉彎時會產生比較大的車身角度變化，而小波轉換像是濾波可以提取輸入信號的高頻部分，被提取出來的高頻訊號被用來增加車輛轉彎的速度。本論文設計一三階段的實驗：第一階段實現典型的模糊控制器作為比較基準；

第二階段實現沒有輸出權重值的小波模糊控制器；最後階段實現適當權重值的小波模糊控制器。

電腦模擬實驗結果顯示，小波轉換模糊控制器提供了較高的效能。在適當的初始條件下，停車位置誤差大致改善了0.38m (33%)，車身角度誤差大致改善了0.78度 (14%)，車身與車頭的角度誤差則改善了0.32度 (10%)。實驗結果顯示小波模糊控制器可以有效解決停車問題。

The truck-and-trailer problem often bothers those beginning drivers since they easily get confused when switching from driving forwards to driving backwards. Besides some human drivers, intelligent vehicle researchers show great interest in parking problem too, because of parking problems are frequently encountered in automatic vehicle designs. The performance of a parking controller is limited by the parking area space because of the backing problem only allow the truck move towards the back. Although, a lot of system optimization methods haven been proposed for overcoming the difficulties in designing system, comparatively little research has focused on the relationship between wavelets and the parking problem.

Wavelets have been used more and more intensively in recent years, especially in the field of image processing and signal processing. Wavelets not only catches the characteristic of a signal both in the frequency and time domains, but also possess the advantage of multiresolution analysis. It is very efficient to represent the unsteady signals by using wavelet transforms. This thesis implemented a wavelet-based fuzzy backing controller to show the performance of a fuzzy backing controller can be improved by decomposing the error signal into its wavelet components.

In this thesis we implemented a wavelet-based fuzzy controller to solve the truck-and-trailer parking problem. The angle of the steering wheel will produce a great change when the truck turns around a corner. Therefore, the high frequency part of a signal is relevant to the control of a truck when turning around a corner. Like filtering, wavelet transformation can extract the high frequency part of an input signal. In this thesis, a three-phase experiment was designed to explore the advantage of applying wavelet transformation to error signals of the parking problem. In phase I, a typical fuzzy controller was designed as a reference of benchmark. Then, a wavelet-based fuzzy controller without output weighting was designed to solve the parking problem in phase II. Finally, in the last phase, a wavelet-based fuzzy controller, with suitable output weighting was designed.

The computer simulation reported in this thesis demonstrated that the wavelet-based fuzzy controller practically can be implemented and provides satisfactory results. The improvements over the basic FLC are 0.38m (33%) in the error of location, 0.78 degree (14%) in the error of orientation (truck) and 0.32 degree (10%) in the error of orientation (truck and cab). These findings lead us to believe that the wavelet method is powerful in designing the fuzzy controller for the parking problem.

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