基於參數識別模型之適應性巡航控制與車身動態穩定輔助系統開發實務驗證

Development and Validation of Adaptive Cruise Control and Electronic Stability Control System Based on Vehicle Models with Parameters Identification

劉書廷(Shu-Ting Liu)

指導教授 ： 鄭榮和

國立臺灣大學/工學院/機械工程學研究所/碩士(2021年)

https://doi.org/10.6342/NTU202102128

摘要

考量現有先進駕駛輔助系統( Advanced Driver Assistance Systems, ADAS )多以商用小客車為開發主體進行設計，較少關注大型車輛(貨車、卡車)的需求。本研究考量大型車輛需求，透過建置估測器系統所獲取車輛狀態來開發一套能應對大型車輛負載變化之適應性巡航控制( Adaptive Cruise Control, ACC )系統，透過車載狀態的偵測來調整ACC系統所應維持理想車距，以提高ACC系統在不同車載下的適應性並針對高負載情況提高車距以策安全，從而降低駕駛因疲勞而發生交通事故的機率。針對車輛安全防護部分，本研究也透過估測器資訊開發一套具備適應性調整的車身電子穩定控制( Electronic Stability Control, ESC )系統，透過估測車載狀態與路面摩擦係數來調整ESC系統煞車力，以期加強系統安全性與適應性。本研究為驗證所開發ACC與ESC策略在實際硬體系統上運作的可行性，也利用自行開發雙軸動力計平台完成硬體在環迴路( Hardware in The Loop, HiL )測試，並透過雙軸動力與煞車系統測試平台的驗證程序確認控制策略運行可靠度，也確認其在實際硬體運作上安全且具備效力。為開發上述駕駛輔助系統，需建立目標車輛模型，藉由系統模型模擬才能有效體現所開發控制策略的優劣。本研究提出一套基於基因演算法的參數識別策略，透過實車測試數據來識別系統參數，以得到符合實車規格之車輛模型，後續開發之控制策略皆可透過此模型來進行模擬及測試。本研究整合車輛系統建模與先進駕駛輔助系統開發兩大主軸，完成整套ACC與ESC策略開發驗證程序。

關鍵字

參數識別 ； 基因演算法 ； 慣性量測單元校準 ； 適應性巡航控制 ； 車身動態穩定控制 ； 質量估測

並列摘要

Considering that existing Advanced Driver Assistance Systems (ADAS) are mostly designed for passenger vehicles, and less attention is paid to commercial vehicles (lorries, trucks). However, commercial drivers usually drive for long hours, which can lead to traffic accidents due to fatigue. Thus, this research develops an adaptive cruise control (ACC) system with observer that can detect of vehicle load change to modify vehicle distance to improve the adaptability of the ACC system and increase the vehicle distance for safety under high load condition, thereby reducing the risk of traffic accidents. This research also develops an adaptive electronic stability control (ESC) system with observer information to adjust the braking force, expecting to enhance the safety and adaptability of vehicle. In order to verify the feasibility of the developed ACC and ESC system working on actual hardware systems, a validation platform is developed to execute the hardware in the loop (HiL) test. The HiL test result proves the reliability of the developed strategy operation, and confirms the safety and effectiveness during hardware operation. For developing the ADAS strategy, a vehicle model is required for simulation. In order to build target vehicle model, vehicle parameters estimation becomes necessary. Therefore, this research proposes a set of parameter identification strategy based on genetic algorithm. Through the parameter identification process, a vehicle model that meets the actual vehicle specifications can be obtain, and subsequent development of control strategies can be simulated and tested through this model. This research integrates vehicle modeling and ADAS development, and completes ACC and ESC strategy verification procedures.

並列關鍵字

Parameter Identification ； Genetic Algorithm ； IMU Calibration ； Adaptive Cruise Control ； Electronic Stability Control ； Mass Estimation

參考文獻

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