Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and cancelation.An extended state observer(ESO...Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and cancelation.An extended state observer(ESO)based controller is the key to the LADRC method.In this article,the LADRC scheme combined with a fractional-order integral action(FOILADRC)is proposed to improve the robustness of the standard LADRC.Using the robust closed-loop Bode’s ideal transfer function(BITF),an appropriate pole placement method is proposed to design the set-point tracking controller of the FOI-LADRC scheme.Numerical simulations and experimental results on a cart-pendulum system will illustrate the effectiveness of the proposed FOI-LADRC scheme for the disturbance rejection,the set-point tracking and the improved robustness.To illustrate the LADRC control schemes and to verify the performance of the proposed FOI-LADRC,compared to the standard LADRC and IOI-LADRC structures,two tests will be carried out.First,simulation tests on an academic example will be presented to show the effect of the different parameters of the control law on the performance of the closed-loop system.Then,these three control structures are implemented on an experimental test bench,the cart-pendulum system,to show their efficiency and to show the superiority of the proposed method compared to the two other structures.展开更多
Whenthere are multiple lanes to choose from downstream of a turning movement,drivers should choose the innermost lane so that drivers at other approaches of the intersection may make concurrent turning movements in th...Whenthere are multiple lanes to choose from downstream of a turning movement,drivers should choose the innermost lane so that drivers at other approaches of the intersection may make concurrent turning movements in the outermost lane(s).However,human dri vers do not always choose the innermost lane,which could lead to crashes with other vehi cles.Therefore,predicting human driver behaviors is vital in reducing crashes,as the need to share the roadways with automated vehicles(AVs)continues to grow.In this research,various machine learning models have been used to predict the left turn destination lane choice of human-driven vehicles(HDVs)at urban intersections based on several quantifi able parameters.A total of 174 subject vehicles were extracted and analyzed in Los Angeles,California,and Atlanta,Georgia,using HDV trajectory data from the Next Generation SIMulation(NGSIM)database.Five machine learning techniques,namely bin ary logistic regression,k nearest neighbors,support vector machines,random forest,and adaptive neuro-fuzzy inference system,were applied to the extracted data to predict the lane choice behavior of drivers.The k nearest neighbors model showed the most promising results for the evaluated data with a correct decision score of over 93%for the unseen test data.This model may be programmed into:(i)AVs,in conjunction with sensors,to predict if an HDV is about to turn into the incorrect destination lane;and(ii)microscopic traffic simulation tools so that modelers can identify potential conflicts when HDVs do not select the appropriate destination lane.展开更多
基金This project was supported by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.(DF-474-135-1441).The authors,therefore,gratefully acknowledge DSR technical and financial support.
文摘Linear active disturbance rejection control(LADRC)is a powerful control structure thanks to its performance in uncertainties,internal and external disturbances estimation and cancelation.An extended state observer(ESO)based controller is the key to the LADRC method.In this article,the LADRC scheme combined with a fractional-order integral action(FOILADRC)is proposed to improve the robustness of the standard LADRC.Using the robust closed-loop Bode’s ideal transfer function(BITF),an appropriate pole placement method is proposed to design the set-point tracking controller of the FOI-LADRC scheme.Numerical simulations and experimental results on a cart-pendulum system will illustrate the effectiveness of the proposed FOI-LADRC scheme for the disturbance rejection,the set-point tracking and the improved robustness.To illustrate the LADRC control schemes and to verify the performance of the proposed FOI-LADRC,compared to the standard LADRC and IOI-LADRC structures,two tests will be carried out.First,simulation tests on an academic example will be presented to show the effect of the different parameters of the control law on the performance of the closed-loop system.Then,these three control structures are implemented on an experimental test bench,the cart-pendulum system,to show their efficiency and to show the superiority of the proposed method compared to the two other structures.
文摘Whenthere are multiple lanes to choose from downstream of a turning movement,drivers should choose the innermost lane so that drivers at other approaches of the intersection may make concurrent turning movements in the outermost lane(s).However,human dri vers do not always choose the innermost lane,which could lead to crashes with other vehi cles.Therefore,predicting human driver behaviors is vital in reducing crashes,as the need to share the roadways with automated vehicles(AVs)continues to grow.In this research,various machine learning models have been used to predict the left turn destination lane choice of human-driven vehicles(HDVs)at urban intersections based on several quantifi able parameters.A total of 174 subject vehicles were extracted and analyzed in Los Angeles,California,and Atlanta,Georgia,using HDV trajectory data from the Next Generation SIMulation(NGSIM)database.Five machine learning techniques,namely bin ary logistic regression,k nearest neighbors,support vector machines,random forest,and adaptive neuro-fuzzy inference system,were applied to the extracted data to predict the lane choice behavior of drivers.The k nearest neighbors model showed the most promising results for the evaluated data with a correct decision score of over 93%for the unseen test data.This model may be programmed into:(i)AVs,in conjunction with sensors,to predict if an HDV is about to turn into the incorrect destination lane;and(ii)microscopic traffic simulation tools so that modelers can identify potential conflicts when HDVs do not select the appropriate destination lane.
