本論文主要探討在路面不平滑狀況下，系統能估測出車輛在行駛彎道時之各參數，其參數包含了車輛側滑角(sideslip angle)、車輛側傾角(roll angle)、車輛偏航角(yaw angle)和道路坡度(road angle)等。在一般車輛估測研究中，皆假設無外在輸入干擾，只存在已知輸入輪胎轉向角度，故可用傳統的卡曼濾波器完成估測。本論文假設系統中存在ㄧ未知輸入干擾，且此未知輸入隨著時間而變化，同時前輪因路面因素，造成輪胎剛度素產生一不確定參數，故需應用強健估測器解決此問題。論文一開始先建立車輛動態模型，接著運用兩種不同的量測方程式，搭配三種估測器：最佳連續時間估測器、最佳離散時間狀態估測器和最佳離散時間強健估測器，來完成車輛狀態估測。最後比較不同估測器的模擬結果，也說明本研究探討理論之可行性。

This thesis presents methods for the filter estimate of the parameters of a vehicle model while the vehicle is turning on the inclined road. The parameters of the vehicle model include the sideslip angle, roll angle, yaw angle, and road angle. Different from the conventional vehicle estimation, without an unknown input, which could be implemented easily by Kalman Filter, this paper considers the system interfered by a time-varying unknown input. Due to the road condition, there exists an uncertainty in tire cornering stiffness. In order to solve the uncertainty, this paper applies the robust Kalman Filter. This study derives the vehicle dynamic model at first and defines two type of observers. Then, we combine three different estimators including the optimal continuous-time estimator, optimal discrete-time estimator, and optimal discrete-time robust estimator to estimate the vehicle model. Finally, the simulation results for different estimators are compared to demonstrate the feasibility of the discussed theory.

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