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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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. https://doi.org/10.1109/TITS.2009.2035438
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. https://doi.org/10.1504/IJVAS.2010.035796
Barraquand J, Langlois B, Latombe JC, 1992. Numerical potential field techniques for robot path planning. IEEE Trans Syst Man Cybern, 22(2):224–241. https://doi.org/10.1109/21.148426
Borenstein J, Koren Y, 1991. The vector field histogram—fast obstacle avoidance for mobile robots. IEEE Trans Robot Autom, 7(3):278–288. https://doi.org/10.1109/70.88137
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. https://doi.org/10.1109/ITSC.2013.6728576
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. https://doi.org/10.1109/MITS.2017.2709782
Chen H, 2013. Model Predictive Control. Science Press, Beijing, China (in Chinese).
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. https://doi.org/10.1109/TITS.2012.2198214
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. https://doi.org/10.1080/00423114.2012.666357
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. https://doi.org/10.1109/TIE.2012.2202357
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. https://doi.org/10.1016/j.arcontrol.2018.02.001
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.
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. https://doi.org/10.1080/00423114.2014.902537
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. https://doi.org/10.1109/TITS.2010.2046037
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. https://doi.org/10.1016/j.trc.2015.09.011
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. IFACPapersOnLine, 49(11):190–195. https://doi.org/10.1016/jifacol.2016.08.029
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. https://doi.org/10.1016/j.cogsys.2018.01.007
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.
Lanza G, Ferdows K, Kara S, et al., 2019. Global production networks: design and operation. CIRP Ann, 68:823–841. https://doi.org/10.1016/j.cirp.2019.05.008
Laskaris KI, Kladas AG, 2010. Internal permanent magnet motor design for electric vehicle drive. IEEE Trans Ind Electron, 57(1):138–145. https://doi.org/10.1109/TIE.2009.2033086
Li SH, Yang SP, 2015. Investigation on dynamics of a three-directional coupled vehicle-road system. J Vibroeng, 17(7):3887–3908.
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. https://doi.org/10.1109/ITSC.2014.6957824
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. https://doi.org/10.3390/en11010055
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. https://doi.org/10.1016/j.retrec.2018.06.005
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. https://doi.org/10.1109/TIE.2012.2186772
Nilsson J, Gao YQ, Carvalho A, et al., 2014. Manoeuvre generation and control for automated highway driving. IFAC Proc Vol, 47(3):6301–6306. https://doi.org/10.3182/20140824-6-ZA-1003.00619
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. https://doi.org/10.1109/TAC.2004.825632
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. https://doi.org/10.1177/0954407011430275
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. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000403
Author information
Authors and Affiliations
Contributions
Hong-chao WANG designed the research. Hong-chao WANG, Wei-wei ZHANG, Hao-tian CAO, and Xun-cheng WU designed the algorithms and processed the data. Hong-chao WANG, Wei-wei ZHANG, Qiao-ming GAO, and Su-yun LUO drafted the manuscript. Hong-chao WANG and Wei-wei ZHANG revised and finalized the paper.
Corresponding author
Ethics declarations
Hong-chao WANG, Wei-wei ZHANG, Xun-cheng WU, Hao-tian CAO, Qiao-ming GAO, and Su-yun LUO declare that they have no conflict of interest.
Additional information
Project supported by the National Fund for Fundamental Research, China (No. 282017Y-5303), the National Natural Science Foundation of China (Nos. 51805312 and 51675324), the Shanghai Sailing Program, China (No. 18YF1409400), the Training and Funding Program of Shanghai College Young Teachers, China (No. ZZGCD15102), the Scientific Research Project of Shanghai University of Engineering Science, China (No. 2016–19), the Shanghai University of Engineering Science Innovation Fund for Graduate Students, China (No. 18KY0610), and the Technology and Innovation Projects of Guangxi Province, China (No. 2017-393)
Rights and permissions
About this article
Cite this article
Wang, Hc., Zhang, Ww., Wu, Xc. et al. A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions. Front Inform Technol Electron Eng 21, 1059–1073 (2020). https://doi.org/10.1631/FITEE.1900185
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.1900185