基于改進粒子濾波的半掛汽車列車狀態估計

中圖分類號：U469.5 文獻標志碼：A

Abstract：Aiming at the problem that some key dynamical states of tractor semi-trailer cannot be measured and the values ofsensors are interfered by random factors such as engine vibration noise，an improved particle filter is proposed toestimatethe dynamical states of the driving tractor semi-trailerinreal-time.This paper establishes a17 degrees of freedom dynamical modelof tractor semi-trailerfirst.Bycombining theparticle filter principleand the adaptive geneticalgorithm to enhance the particle diversity，the piecewise proposal distribution function is designed，and the systematic resampling method is used to suppress the particle regression.The in-time and accurate estimation of longitudinal speed，lateral speed，yawrate，and other states of tractor semi-trailer was realized.A hardware-in-the-loop（HIL）simulation test platform was builtto verifythealgorithm under dierent conditions.The testing results show that compared with the unscented particle filter algorithm，the improved particle filter algorithm proposed in this papercan realize the state estimationof the whole vehicle under both ideal and random noise environments，and has higher estimation accuracy.

Key Words：vehicle engineering;state estimation;particle filter;tractor semi-trailer

半掛汽車列車由于能夠在運輸驛站快速更換掛車，避免了貨物裝卸的等待時間，已成為道路運輸的主力車型.相比于四輪車輛，半掛汽車列車的車身更長，裝載后重心位置更高，因此存在更多的失穩形式及更大的失穩可能性[1-2].半掛汽車列車的整車穩定性控制已成為自前車輛領域的研究熱點，而行駛過程中的動力學狀態實時獲取是影響穩定性控制效果的關鍵因素.鑒于某些關鍵狀態難以通過現有的車載傳感器進行測量，研究人員已經開始采用光學傳感器來應對這一挑戰，例如利用攝像機來測量半掛汽車列車的鉸接角度等[3.然而，該類傳感器顯著提升了整車的制造成本[4]，更實用的解決方案是通過可測量得到的部分狀態信息結合數字濾波技術對不可測狀態進行估計.

目前估計方法主要分為試驗法和模型法5.試驗法包括參數回歸方法、神經網絡、基于規則的方法等.人工神經網絡已被應用于估算車輛的側傾角度-7.非線性最小二乘法作為參數回歸方法已被用于半掛汽車列車質量估計[8-9].試驗法雖然在實現手段上較為簡便，但是需要進行大量的預試驗，獲得足夠的數據集后方能保證估計精度.此外，大量的數據集對于整車控制器的存儲空間也提出了較高要求[.與試驗法相比，模型法無須預先標定，對整車控制器的存儲空間需求也較低.在眾多試驗法中，基于卡爾曼濾波原理的估……