臺灣博碩士論文加值系統

本論文首先提出一個以模糊控制為基礎之控制器對主動式車輛懸吊系統實驗所需的路面模擬系統進行控制，路面模擬系統以氣壓缸為致動器產生路面波動，以用來模擬真實的路面。並設計類神經網路學習不同環境下系統控制器之參數，以達到快速且能即時調整控制參數之目的。針對氣壓缸系統的高度非線性與不確定性，本研究加入基因演算法來輔助類神經網路，當類神經網路所得到的控制參數無法呈現好的路面追蹤效果時，本研究嘗試加入基因演算法尋找最佳的控制參數。論文第二部分，則是針對氣壓肌肉作為主動式車輛懸吊系統之致動器的可行性與有效性進行研究與分析，並設計LQR基礎之最佳控制器進行氣壓肌肉主動式車輛懸吊系統的路面抑振控制，為了提升懸吊控制系統的抑振效果，本論文也利用路面變化資訊設計車體位置控制的前饋補償。最後本論文透過不同的路面測試實驗，驗證氣壓肌肉致動器於主動式車輛懸吊系統的可行性與有效性。

This thesis firstly proposed a pneumatic road actuation system based on fuzzy logic control technique for the developed suspension test bench. The road actuation system can provide the simulated road profile for the analysis in the vehicle suspension control system. Further, the neural network (NN) is applied to learn the control parameters of fuzzy logic controller in different road surface conditions. Due to high nonlinearity and uncertainty of the utilized pneumatic actuation system in the developed road surface simulator, the genetic algorithm (GA) optimization is adopted to assist NN to gain the optimized control parameters for the fuzzy controller. In the second part of thesis, LQR-based optimal controller is designed for the pneumatic-muscle active vehicle suspension system against the road disturbance. Besides, the road profile is employed into the feedforward compensation with the vehicle body control loop so that the suspension control performance can be enhanced. Finally, the experimental results under different road conditions are given to verify the superior performance of the active suspension controller using pneumatic muscle actuators.

摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 前言 1
1.2 車輛懸吊系統種類 1
1.3 研究動機 3
1.4 文獻回顧 4
1.4.1 主動式車輛懸吊系統 4
1.4.2 氣壓致動系統 5
1.4.3 氣壓肌肉致動器 6
1.5 章節架構 6
第二章 路面模擬系統之控制器設計 8
2.1 氣壓動態系統簡介 8
2.2 控制理論 8
2.2.1 模糊理論 9
2.2.2 類神經網路 10
2.2.3 基因演算法 11
2.3 控制器架構與設計 13
第三章 氣壓肌肉主動式懸吊系統之控制器設計 19
3.1 氣壓肌肉致動器 19
3.1.1 氣壓肌肉工作原理與特性 19
3.1.2 氣壓肌肉致動器數學模型 22
3.2 四分之一車氣壓肌肉主動式懸吊系統動態 24
3.3 主動式懸吊系統控制器設計 25
第四章 實驗系統架構與設備 30
4.1 實驗系統架構 30
4.2 路面模擬系統 33
4.3 氣壓肌肉致動系統 35
4.4 嵌入式控制系統 41
第五章 實驗結果 43
5.1 路面模擬系統控制實驗 43
5.2 氣壓肌肉主動式懸吊系統控制實驗 47
第六章 結論 53
6.1 結論 53
6.2 未來展望 53
參考文獻 55

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