發(fā)布時間：2018-06-13 10:06

  本文選題：鉸接式車輛 + 駕駛��； 參考：《農(nóng)業(yè)機械學報》2017年03期

【摘要】：針對無人駕駛鉸接式運輸車輛無人駕駛智能控制問題,提出了一種強化學習自適應(yīng)PID路徑跟蹤控制算法。首先推導了鉸接車的運動學模型,根據(jù)該模型建立實際行駛路徑與參考路徑偏差的模型,以PID控制算法為基礎(chǔ),設(shè)計了基于強化學習的自適應(yīng)PID路徑跟蹤控制器,該控制器以橫向位置偏差、航向角偏差、曲率偏差為輸入,以轉(zhuǎn)角控制量為輸出,通過強化學習算法對PID參數(shù)進行在線自適應(yīng)整定。最后在實車道路試驗中驗證了控制器的路徑跟蹤質(zhì)量并與傳統(tǒng)PID控制結(jié)果進行了對比。結(jié)果表明,相比于傳統(tǒng)PID控制器,強化學習自適應(yīng)PID控制器能夠有效減小超調(diào)和震蕩,實現(xiàn)精確跟蹤參考路徑,可以較好地實現(xiàn)系統(tǒng)動態(tài)性能和穩(wěn)態(tài)誤差性能的優(yōu)化。
[Abstract]:To solve the problem of driverless intelligent control for unmanned articulated vehicles, an enhanced learning adaptive pid path tracking control algorithm is proposed. Firstly, the kinematics model of articulated vehicle is derived. According to the model, the model of deviation between actual driving path and reference path is established. Based on pid control algorithm, an adaptive pid path tracking controller based on reinforcement learning is designed. The controller takes lateral position deviation, heading angle deviation, curvature deviation as input and angle control quantity as output, and adaptively adjusts pid parameters online by reinforcement learning algorithm. Finally, the path tracking quality of the controller is verified in the real vehicle road test and compared with the traditional pid control results. The results show that, compared with the traditional pid controller, the reinforcement learning adaptive pid controller can effectively reduce the overharmonic oscillation, track the reference path accurately, and optimize the dynamic performance and steady-state error performance of the system.
【作者單位】： 北京科技大學機械工程學院;北京華為數(shù)字技術(shù)有限公司;
【基金】：國家高技術(shù)研究發(fā)展計劃(863計劃)項目(2011AA060404) 中央高校基本科研業(yè)務(wù)費專項資金項目(FRF-TP-16-004A1)
【分類號】：TP273;U463.6

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相關(guān)期刊論文 前6條

1 李建國;戰(zhàn)凱;石峰;郭鑫;李恒通;;基于最優(yōu)軌跡跟蹤的地下鏟運機無人駕駛技術(shù)[J];農(nóng)業(yè)機械學報;2015年12期

2 趙，

本文編號：2013660