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基于雙無跡卡爾曼濾波的自動(dòng)駕駛狀態(tài)慣性監(jiān)測
黃亞成 2023/12/24 8:34:27
福建省南平市閩北高級(jí)技工學(xué)校，機(jī)械教研組，福建南平 354000
摘要：自動(dòng)駕駛的完成需要設(shè)計(jì)合適的線控轉(zhuǎn)向系統(tǒng)。為了能夠?qū)囕v動(dòng)力慣性參數(shù)開展非線性評(píng)價(jià)，開發(fā)了一種分布結(jié)構(gòu)驅(qū)動(dòng)力電動(dòng)汽車雙無跡卡爾曼濾波(Dual unscented Kalman filter, DUKF)方法與狀態(tài)觀測系統(tǒng)聯(lián)合系統(tǒng)車輛慣性監(jiān)測方法。在分布結(jié)構(gòu)驅(qū)動(dòng)電動(dòng)汽車傳感器中，除了具備傳統(tǒng)傳感器慣性量參數(shù)如質(zhì)心橫擺角速度、縱向和側(cè)向加速度以外，還可以提供輪轂電機(jī)傳感器進(jìn)行車輪角速度測試。研究結(jié)果表明：采用DUKF方法觀測數(shù)據(jù)比DEKF方法更加符合實(shí)際情況，促進(jìn)觀測精度的顯著提升。本研究有助于提高自動(dòng)駕駛狀態(tài)慣性監(jiān)測能力，對(duì)自動(dòng)駕駛技術(shù)的提高有一定的理論支撐意義。
關(guān)鍵詞：電動(dòng)汽車；狀態(tài)觀測；慣性參數(shù)；雙無跡卡爾曼濾波
中圖分類號(hào)：U461
Status inertial monitoring of electric vehicle based on double untracked Kalman filter
Huang Yacheng
Mechanical Teaching and Research Group, Minbei Advanced Technical School, Nanping 354000, China
Abstract: The completion of autonomous driving requires the design of a suitable steering system by wire. In order to carry out nonlinear evaluation of vehicle dynamic inertia parameters, a kind of integrated vehicle inertial monitoring method of Dual unscented Kalman filter (DUKF) and state observation system with distributed driving force is developed. In the distributed structure driven electric vehicle sensor, in addition to the traditional sensor inertia parameters such as yaw velocity of the center of mass, longitudinal and lateral acceleration, it can also provide hub motor sensor for wheel angular velocity measurement. The results show that the DUKF method is more consistent with the actual situation than the DEKF method, and the observation accuracy is improved significantly. This study is helpful to improve the inertial monitoring ability of autopilot state and has a certain theoretical significance for the improvement of autopilot technology.
Key words: Electric vehicle; State observation; Inertia parameter; Double unscented Kalman filter
1 前言
為了對(duì)分布動(dòng)力結(jié)構(gòu)汽車進(jìn)行主動(dòng)控制，需要對(duì)其行駛階段的各項(xiàng)動(dòng)力學(xué)參數(shù)開展精確采集與分析，但在信息測試過程中需要配備高成本的傳感器檢測設(shè)備，并且也無法滿足信號(hào)可靠度要求，同時(shí)還需關(guān)注車輛質(zhì)心偏角等指標(biāo)[1-2]。
為了達(dá)到自動(dòng)駕駛目標(biāo)，需要設(shè)計(jì)合適的線控轉(zhuǎn)向系統(tǒng)，當(dāng)傳統(tǒng)汽車發(fā)生轉(zhuǎn)向系統(tǒng)的失效問題時(shí)，汽車只能進(jìn)入減速狀態(tài)并最終完成停車，不能對(duì)軌跡進(jìn)行精確跟蹤，甚至還會(huì)造成交通事故[5-6]。由此可見，如何調(diào)節(jié)轉(zhuǎn)向系統(tǒng)故障容錯(cuò)能力已經(jīng)成為一項(xiàng)關(guān)鍵措施。在分布結(jié)構(gòu)電驅(qū)動(dòng)汽車系統(tǒng)中可以設(shè)置多個(gè)電機(jī)形成橫擺力作用再對(duì)轉(zhuǎn)向系統(tǒng)橫向偏差進(jìn)行補(bǔ)償，由此確保轉(zhuǎn)（未完，下一頁）
附件下載：基于雙無跡卡爾曼濾波的自動(dòng)駕駛狀態(tài)慣性監(jiān)測
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