Full Text:   <449>

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CLC number: TP273

On-line Access: 2020-07-10

Received: 2019-04-08

Revision Accepted: 2019-09-16

Crosschecked: 2020-06-03

Cited: 0

Clicked: 671

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Hong-chao Wang

https://orcid.org/0000-0002-9190-179X

Wei-wei Zhang

https://orcid.org/0000-0002-9768-2620

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.7 P.1059-1073

http://doi.org/10.1631/FITEE.1900185


A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions


Author(s):  Hong-chao Wang, Wei-wei Zhang, Xun-cheng Wu, Hao-tian Cao, Qiao-ming Gao, Su-yun Luo

Affiliation(s):  College of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China; more

Corresponding email(s):   17721336541@163.com, zwwsues@163.com, longxd2714@163.com, yjs_liqing@163.com, mosxsues@163.com, ly18362885604@163.com

Key Words:  Automated truck, Trajectory planning, Nonlinear model predictive control, Longitudinal slip


Hong-chao Wang, Wei-wei Zhang, Xun-cheng Wu, Hao-tian Cao, Qiao-ming Gao, Su-yun Luo. A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(7): 1059-1073.

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journal="Frontiers of Information Technology & Electronic Engineering",
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publisher="Zhejiang University Press & Springer",
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Abstract: 
We present a double-layered control algorithm to plan the local trajectory for automated trucks equipped with four hub motors. The main layer of the proposed control algorithm consists of a main layer nonlinear model predictive control (MLN-MPC) controller and a secondary layer nonlinear MPC (SLN-MPC) controller. The MLN-MPC controller is applied to plan a dynamically feasible trajectory, and the SLN-MPC controller is designed to limit the longitudinal slip of wheels within a stable zone to avoid the tire excessively slipping during traction. Overall, this is a closed-loop control system. Under the off-line co-simulation environments of AMESim, Simulink, dSPACE, and TruckSim, a dynamically feasible trajectory with collision avoidance operation can be generated using the proposed method, and the longitudinal wheel slip can be constrained within a stable zone so that the driving safety of the truck can be ensured under uncertain road surface conditions. In addition, the stability and robustness of the method are verified by adding a driver model to evaluate the application in the real world. Furthermore, simulation results show that there is lower computational cost compared with the conventional PID-based control method.

不确定路面附着系数条件下一种基于双层非线性模型预测控制的自动驾驶卡车轨迹规划方法

王鸿超1,张伟伟1,吴训成1,曹昊天2,高巧明3,罗素云1
1上海工程技术大学机械与汽车工程学院,中国上海市,201620
2湖南大学汽车车身先进设计制造国家重点实验室,中国长沙市,410082
3广西科技大学汽车与交通学院,中国柳州市,545006

摘要:提出一种双层控制算法以规划配备四轮轮毂电机的自动驾驶卡车的行驶轨迹。该控制算法主要由主层非线性模型预测控制(MLN-MPC)算法和次层非线性模型预测控制(SLN-MPC)算法组成,其中,MLN-MPC控制算法用于规划合理的卡车行驶轨迹,SLN-MPC控制算法将车轮纵向滑移率限制在稳定区域,避免卡车在驱动过程中发生过度打滑。总体而言,该控制算法为一个闭环控制系统。在离线仿真环境下,通过AMESim、Simulink、dSPACE和TruckSim仿真软件联合仿真。仿真结果表明,本文所提算法能规划一条合理的车辆避障行驶轨迹,在不确定路面附着系数条件下能将车辆纵向滑移率控制在合理范围。此外,为评估该算法在实际应用中的可行性,在联合仿真系统中加入驾驶员模型验证该算法的稳定性与鲁棒性。与传统的基于PID控制算法相比,该算法具有更低的计算能耗。

关键词:自动驾驶卡车;轨迹规划;非线性模型预测控制;纵向滑移率

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Reference

[1]Amodeo M, Ferrara A, Terzaghi R, et al., 2010. Wheel slip control via second-order sliding-mode generation. IEEE Trans Intell Transp Syst, 11(1):122-131.

[2]Anderson SJ, Peters SC, Pilutti TE, et al., 2010. An optimal- control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios. Int J Veh Auton Syst, 8(2-4):190-216.

[3]Barraquand J, Langlois B, Latombe JC, 1992. Numerical potential field techniques for robot path planning. IEEE Trans Syst Man Cybern, 22(2):224-241.

[4]Borenstein J, Koren Y, 1991. The vector field histogram—fast obstacle avoidance for mobile robots. IEEE Trans Robot Autom, 7(3):278-288.

[5]Carvalho A, Gao YQ, Gray A, et al., 2013. Predictive control of an autonomous ground vehicle using an iterative linearization approach. Proc 16th Int IEEE Conf on Intelligent Transportation Systems, p.2335-2340.

[6]Cesari G, Schildbach G, Carvalho A, et al., 2017. Scenario model predictive control for lane change assistance and autonomous driving on highways. IEEE Intell Trans Syst Mag, 9(3):23-35.

[7]Chen H, 2013. Model Predictive Control. Science Press, Beijing, China (in Chinese).

[8]Chu K, Lee M, Sunwoo M, 2012. Local path planning for off- road autonomous driving with avoidance of static obstacles. IEEE Trans Intell Trans Syst, 13(4):1599-1616.

[9]de Castro R, Araújo RE, Tanelli M, et al., 2012. Torque blending and wheel slip control in EVs with in-wheel motors. Veh Syst Dynam, 20(1):71-94.

[10]de Castro R, Araújo RE, Freitas D, 2013. Wheel slip control of EVs based on sliding mode technique with conditional integrators. IEEE Trans Ind Electron, 60(8):3256-3271.

[11]Dixit S, Fallah S, Montanaro U, et al., 2018. Trajectory planning and tracking for autonomous overtaking: state-of- the-art and future prospects. Ann Rev Contr, 45:76-86.

[12]Gao Y, Gray A, Frasch J, et al., 2012. Spatial predictive control for agile semi-autonomous ground vehicles. Proc 11th Int Symp on Advanced Vehicle Control, p.1-6.

[13]Gao YQ, Gray A, Tseng HE, et al., 2014. A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles. Veh Syst Dynam, 52(6):802-823.

[14]Glaser S, Vanholme B, Mammar S, et al., 2010. Maneuver- based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction. IEEE Trans Intell Trans Syst, 11(3):589-606.

[15]Katrakazas C, Quddus M, Chen WH, et al., 2015. Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions. Trans Res Part C, 60:416-442.

[16]Kim B, Kim D, Park S, et al., 2016. Automated complex urban driving based on enhanced environment representation with GPS/map, radar, lidar and vision. IFAC- PapersOnLine, 49(11):190-195.

[17]Kim J, Lee J, 2018. Traction-energy balancing adaptive control with slip optimization for wheeled robots on rough terrain. Cogn Syst Res, 49:142-156.

[18]Kitazawa S, Kaneko T, 2017. Control target algorithm for direction control of autonomous vehicles in consideration of mutual accordance in mixed traffic conditions. Int Symp on Advanced Vehicle Control, p.151-156.

[19]Lanza G, Ferdows K, Kara S, et al., 2019. Global production networks: design and operation. CIRP Ann, 68:823-841.

[20]Laskaris KI, Kladas AG, 2010. Internal permanent magnet motor design for electric vehicle drive. IEEE Trans Ind Electron, 57(1):138-145.

[21]Li SH, Yang SP, 2015. Investigation on dynamics of a three- directional coupled vehicle-road system. J Vibroeng, 17(7):3887-3908.

[22]Ma L, Xue JR, Kawabata K, et al., 2014. A fast RRT algorithm for motion planning of autonomous road vehicles. Proc 17th Int IEEE Conf on Intelligent Transportation Systems, p.1033-1038.

[23]Mareev I, Becker J, Sauer DU, 2018. Battery dimensioning and life cycle costs analysis for a heavy-duty truck considering the requirements of long-haul transportation. Energies, 11(1):55.

[24]Mittal N, Udayakumar PD, Raghuram G, et al., 2018. The endemic issue of truck driver shortage—a comparative study between India and the United States. Res Trans Econ, 71:76-84.

[25]Mutoh N, 2012. Driving and braking torque distribution methods for front- and rear-wheel-independent drive-type electric vehicles on roads with low friction coefficient. IEEE Trans Ind Electron, 59(10):3919-3933.

[26]Nilsson J, Gao YQ, Carvalho A, et al., 2014. Manoeuvre generation and control for automated highway driving. IFAC Proc Vol, 47(3):6301-6306.

[27]Shamir T, 2004. How should an autonomous vehicle overtake a slower moving vehicle: design and analysis of an optimal trajectory. IEEE Trans Autom Contr, 49(4):607-610.

[28]Shim T, Adireddy G, Yuan HL, 2012. Autonomous vehicle collision avoidance system using path planning and model-predictive-control-based active front steering and wheel torque control. Proc Inst Mech Eng Part D, 226(6): 767-778.

[29]So J, Park B, Wolfe SM, et al., 2014. Development and validation of a vehicle dynamics integrated traffic simulation environment assessing surrogate safety. J Comput Civ Eng, 29(5):04014080.

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