運用狀態觀察器技術之車輛翻覆預測系統

Abstract

在本篇論文中，我們提出了一個車輛翻覆預測系統。此一系統主要是以含有道路狀況之＂完整車輛模型＂為出發點，藉由狀態觀察器技術來獲得車輛即時動態，並且將即時動態傳輸至車輛模型中，來預測在未來時間內的車輛動態，進而以在未來時間內的車輛側傾角，判斷車輛是否即將翻覆。此種預測方法能應用於各種不同動態的車型，並提供可靠的物理根據來宣告車輛翻覆事件。 此預測系統的挑戰之一為如何建立以完整車輛模型（高階、高度非線性系統）為基礎之狀態觀察器。我們提出一適用於非線性系統之新型觀察矩陣，藉由此觀察矩陣來簡化完整車輛模型，並拆解成兩個低階子模型：側傾、橫擺子模型。如此一來，即可針對兩低階子模型分別建立狀態觀察器，再經由相似於傳統ADI(Alternative Direction Implicit)之切換式數值演算法，來進行車輛即時動態估測。由本論文中的ADI-like切換式演算機制之收斂穩定度分析中可知，此演算機制能成功的使兩個從複雜系統中解析出之子系統，近似於原複雜系統之動態行為。 由模擬結果得知，當車輛在一斜坡上做快速轉彎之操作行為下，上述之觀察器演算機制可藉由三種感測器：縱向速度感測器、側向加速度感測器以及懸掛系統位移感測器，來正確地預測出車輛翻覆之發生。

In this thesis, we present a vehicle rollover prediction method, which employs the “full-car model” accompanied with road conditions and states observer techniques, to predict vehicle dynamics and declare a rollover happening by the vehicle roll angle in future time. This prediction method presents a strong evidence for a rollover occurrence, and the methodology can be widely applied to vehicles with different dynamic characteristics. Based on the novel observability matrix proposed in this thesis, the “full-car model” is broken down into two subsystems. Two states observers are constructed for each subsystem respectively and do the switching scheme for the vehicle states estimation, which the approach is similar to the conventional alternative direction implicit method (ADI). The proposed ADI-like computation scheme enables a states observer design for a highly nonlinear and high order dynamic system. Simulation results indicate that, with the following three sensors: longitudinal velocity sensor, lateral accelerometer and suspension displacement sensor, we are able to predict a vehicle rollover occurrence correctly, which is initiated by a quick wheels maneuvering on a slope.