藉由差動煞車(Differential braking)以及直接偏航轉矩(Direct Yaw-moment Comtrol, DYC)的架構可提供部份翻覆預防的功能，本研究使用模型預估控制法(model predictive control, MPC)設計DYC，再藉由縮小型車輛的左右輪差速控制來產生預防翻覆的偏航矩。考慮到了車輛上的乘載狀況變化的問題，透過車輛動力學模型以及遞迴最小平方法設計一線上車輛質量估測器，再將估測而得的車輛質量用以更新於MPC內部所需的車輛模型，以使得此DYC設計能適應未知的車輛質量所產生的影響。經實驗證明由於模型以及訊號的精確度不高，因此質量估算的結果並不佳，但是若質量估算的品質能藉由訊號篩選以及模型修正而得較正確之值時，MPC設計的DYC能有效針對不同的車輛質量提供更正確的翻覆預防控制命令。
偵測翻覆發生的翻覆指標，常可作為啟動翻覆預防控制器或警示所需的依據。由於翻覆偵測的目的在於預防，而近年來車輛前方道路模型偵測的技術愈見成熟，本研究探討結合前方道路資訊在翻覆偵測上所能提供的改善。藉由道路模型以及車速資訊，建立未來可能發生的車輛偏航率以及前輪轉向角，再透過車輛動力學預估車輛未來的動態，再經由這些未來資訊應用於翻覆指標，從而建立預估式的翻覆指標，最後利用TruckSim模擬來驗證預估效果。

The framework of utilizing direct yaw moment control (DYC) and differential braking has been applied to vehicle rollover prevention. In this research the model predictive control (MPC) has been employed to design the DYC for rollover prevention. A scaled vehicle with independent driving wheel control is used to emulate the differential braking to implement the desired yaw moment. Considering the variation in the loading condition, the vehicle mass is estimated on-line using a recursive least square method based on the vehicle model structure. The estimated mass is used to update the vehicle model that is needed inside the MPC, so that the MPC can adapt to the change in the loading condition. Experimental results indicate that due to the quality of the measured signals and the model, the estimated mass fluctuates significantly and is practically un-usable. However, in the future if the mass estimation can be improved by introducing more high quality sensors or better model information, a correctly estimated mass will be beneficial to the MPC and the computed yaw moment can provide the desired function even when the mass is unknown.
The roll index to predict vehicle rollover is generally used to determine whether to issue a warning or intervention for rollover prevention. Since the purpose of the roll index is to prevent, and the vast progress in the road information detection technology, in this research the advantage of utilizing upcoming road information to the rollover detection. Using the assumed available road geometry model and the instantaneous vehicle speed, the possible vehicle yaw rate and front wheel steering angle is computed. Applying the computed signals to the vehicle model, the future vehicle dynamics is estimated on-line in discrete time instants. With the predicted vehicle dynamic states, the predictive roll indices are established and evaluated using TruckSim simulations.

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