電動獨輪車載具之設計與實現

Design and Implementation of Electric Unicycle Vehicle

周明鴻(Ming-Hung Chou)

指導教授 ： 邱智煇

元智大學/電機通訊學院/電機工程學系/碩士(2011年)

https://doi.org/10.6838/YZU.2011.00233

摘要

在本論文中，我們實現了一個以單輪為移動機制之電動獨輪車。為完成電動獨輪車的平衡控制之目標，我們設計了一個自我平衡控制器，利用傳統倒單擺控制之概念，以車身傾斜之角度與角速度當作控制變數，主動控制馬達的出力達到獨輪車直立不倒之目的。本論文中，我們主要提出了兩種控制演算法來分別做比較，其中包含模糊控制器及適應性模糊控制器。傳統模糊理論之模糊規則是藉由使用者經驗所設計，故對於控制非線性系統，是一簡單、好設計之控制理論。然而，此電動獨輪車存在著許多不確定之因素及雜訊，會影響系統動作，為了解決此問題，我們將使用適應性模糊控制器，其中適應性模糊控制器之歸屬函數型態為高斯函數，並藉由李亞普諾夫定理(Lyapunov theory)推導其學習速率且以達誤差之收斂；因此於本系統中我們將使用適應性模糊控制器來當系統主控制器。最後，我們也將透過電動獨輪車平台之模擬與實驗，分別比較模糊控制器與適應性模糊控制器之模擬與實驗結果，驗證了適應性模糊控制器因能調整學習速率使誤差快速收歛，故在平衡與控制上能有較佳的成效。

關鍵字

電動獨輪車 ； 適應性模糊控制器 ； 學習速率 ； 李亞普諾夫定理

並列摘要

In this thesis, we design and implement an electric unicycle vehicle. In this thesis, we compared two control algorithms including traditional fuzzy controller and adaptive fuzzy controller. For nonlinear systems, the traditional fuzzy is a control theory that is simple and good design. Its rule table can be designed by user experience. However, there are many uncertain noise and factors that the system motion can be affected in the electric unicycle. In order to solve these problems, we use adaptive fuzzy controller, where the membership function of adaptive fuzzy controller is Gaussian function. The parameters of fuzzy membership functions are adjusted online using the gradient descent method. The learning rates of the controller are determined using an analytical method based on a Lyapunov function, such that system convergence is achieved. The variable and optimal learning rates are derived to achieve rapid tracking-error convergence. Finally, by the simulation and experimentation of electric unicycle vehicle, we compare the fuzzy controller and adaptive fuzzy controller, respectively. From the results, the adaptive fuzzy controller can be demonstrated that it has better performance for control and balance.

並列關鍵字

electric unicycle ； adaptive fuzzy controller ； learning rate ； Lyapunov theory

參考文獻

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