基于能量最优的无人驾驶汽车路径跟踪控制

doi:10.16182/j.issn1004731x.joss.20-0655

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (1): 163-169.doi: 10.16182/j.issn1004731x.joss.20-0655

• 仿真模型/系统置信度评估技术 • 上一篇下一篇

基于能量最优的无人驾驶汽车路径跟踪控制

吴小龙¹, 夏甫根², 陈静¹, 徐佳¹

1.重庆理工大学车辆工程学院,重庆 400054;
2.成都壹为新能源汽车有限公司,四川成都 611730

收稿日期:2020-09-03 修回日期:2020-11-16 出版日期:2022-01-18 发布日期:2022-01-14
第一作者简介:吴小龙(1994-)，男，硕士生，研究方向为新能源传动系统控制。E-mail：wuxlong@2018.cqut.edu.cn
基金资助:
四川省科技厅重点研发项目(2019YFG0528)

Autonomous Vehicle Path Tracking Control System Based on Energy Optimization

Wu Xiaolong¹, Xia Fugen², Chen Jing¹, Xu Jia¹

1. School of Vehicle Engineering, Chongqing University of Technology, Chongqing 400054, China;
2. Chengdu Yiwei New Energy Automobile Co., Ltd., Chengdu 610015, China

Received:2020-09-03 Revised:2020-11-16 Online:2022-01-18 Published:2022-01-14

摘要/Abstract

摘要： 动力总成控制对无人驾驶汽车的动态性能和经济性起着重要的作用。为了提高车辆在路径跟踪过程中的动力性和经济性,提出了一种基于能量最优化的路径跟踪控制策略。控制策略分为两部分,在上层控制器中使用非线性模型预测控制来计算所需要的动力参数和前轮转角。下层控制器采用基于电机能耗数值最优的方法进行设计。该方法可以确保电机一直运行在效率最优状态,并可根据电机状态动态调节无级变速器以满足车辆动力需求。仿真结果表明,控制方案具有良好的跟踪性能和节能潜力。

关键词: 无人驾驶汽车, 动力总成系统, 路径跟踪控制, 数值优化, 无级变速器

Abstract: Powertrain control is important to the dynamic performance and economy of driverless cars and a path following control strategy based on energy optimization is proposed. The control strategy includes two parts. The nonlinear model predictive control is used in the upper controller to calculate the required power parameters and front wheel angle. The lower-level controller is designed based on the optimal value of motor energy consumption which ensure the motor being always running at the optimal state of efficiency. In addition, the continuously variable transmission (CVT) is dynamically adjusted according to the motor state to meet the vehicle power demand. The simulation results show that the control scheme has good tracking performance and energy saving potential.

Key words: autonomous vehicle, powertrain system, path following control, numerical optimization, continuously variable transmission

中图分类号:

TP391
U463

吴小龙,夏甫根,陈静等 . 基于能量最优的无人驾驶汽车路径跟踪控制[J]. 系统仿真学报, 2022, 34(1): 163-169.

Wu Xiaolong,Xia Fugen,Chen Jing,et al . Autonomous Vehicle Path Tracking Control System Based on Energy Optimization[J]. Journal of System Simulation, 2022, 34(1): 163-169.

参考文献

[1] Barkenbus J N.Eco-driving: An Overlooked Climate Change Initiative[J]. Energy Policy (S0301-4215), 2010, 38(2): 762-769.
[2] Ritschel R, Schrödel F, Hädrich J, et al.Nonlinear Model Predictive Path-Following Control for Highly Automated Driving[J]. IFAC-Papers On Line (S2405-8963), 2019, 52(8): 350-355.
[3] Grigorescu S, Trasnea B, Cocias T, et al.A Survey of Deep Learning Techniques for Autonomous Driving[J]. Journal of Field Robotics (S1556-4959), 2020, 37(3): 362-386.
[4] De L D A, Pereira G A S. Navigation of an Autonomous Car Using Vector Fields and the Dynamic Window Approach[J]. Journal of Control, Automation and Electrical Systems (S2195-3899), 2013, 24(1/2): 106-116.
[5] Doherty K, Wang J, Englot B.Probabilistic map fusion for fast, incremental occupancy mapping with 3d hilbert maps[C]//2016 IEEE international conference on robotics and automation (ICRA). Stockholm, Sweden, IEEE 2016: 1011-1018.
[6] Droeschel D, Schwarz M, Behnke S.Continuous Mapping and Localization for Autonomous Navigation in Rough Terrain Using a 3D Laser Scanner[J]. Robotics and Autonomous Systems (S0921-8890), 2017, 88: 104-115.
[7] Daoud M A, Osman M, Mehrez M W, et al.Path-Following and Adjustable Driving Behavior of Autonomous Vehicles Using Dual-Objective Nonlinear MPC[C]//2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES). Cairo, Egypt, IEEE, 2019: 1-6.
[8] Sajadi-Alamdari S A, Voos H, Darouach M. Nonlinear Model Predictive Control for Ecological Driver Assistance Systems in Electric Vehicles[J]. Robotics and Autonomous Systems(S0921-8890), 2019, 112: 291-303.
[9] Ma F, Yang Y, Wang J, et al.Predictive Energy-Saving Optimization Based on Nonlinear Model Predictive Control for Cooperative Connected Vehicles Platoon with V2V Communication[J]. Energy(S0360-5442), 2019, 189: 116-120.
[10] Sajadi-Alamdari S A, Voos H, Darouach M. Nonlinear model predictive extended eco-cruise control for battery electric vehicles[C]//2016 24th mediterranean conference on control and automation (MED). Athens, Greece, IEEE, 2016: 467-472.
[11] Hofman T, Salazar M.Transmission ratio design for electric vehicles via analytical modeling and optimization[C]//2020 IEEE Vehicle Power and Propulsion Conference (VPPC). Gijon: Spain, 2020: 1-6.
[12] Verbruggen F, Salazar M, Pavone M, et al.Joint design and control of electric vehicle propulsion systems[C]// 2020 European Control Conference (ECC), St. Petersburg, Russia: IEEE, 2020: 1725-1731.
[13] Verbruggen F J R, Rangarajan V, Hofman T. Powertrain design optimization for a battery electric heavy-duty truck[C]//2019 American Control Conference (ACC). Philadelphia, PA, USA: IEEE, 2019: 1488-1493.
[14] Farag W.Complex track maneuvering using real-time MPC control for autonomous driving[J]. International Journal of Computing and Digital Systems(S2210-142X), 2020, 9(5): 909-920.
[15] Gao Y, Lin T, Borrelli F, et al.Predictive Control of Autonomous Ground Vehicles with Obstacle Avoidance on Slippery Roads[C]//Dynamic Systems and Control Conference. Cambridge, Massachusetts, USA,ASME 2010, 44175: 265-272.
[16] Guzzella L, Sciarretta A.Vehicle Propulsion Systems[M]. Berlin, Heidelberg, Springer-Verlag Berlin Heidelberg, 2007.
[17] Mahmoudi A, Soong W L, Pellegrino G, et al.Loss Function Modeling of Efficiency Maps of Electrical Machines[J]. IEEE Transactions on Industry Applications (S2644-1241), 2017, 53(5): 4221-4231.
[18] Hofman T, Salazar M.Transmission ratio design for electric vehicles via analytical modeling and optimization[C]//2020 IEEE Vehicle Power and Propulsion Conference (VPPC), 2020: 1-6.
[19] Zhao J.Design and Control Co-Optimization for Advanced Vehicle Propulsion Systems[D]. Paris, France Université Paris-Scalay IFP Energies Nouvelles, 2017.
[20] Pourabdollah M, Murgovski N, Grauers A, et al.Optimal Sizing of a Parallel PHEV Powertrain[J]. IEEE Transactions on Vehicular Technology (S1939-9359), 2013, 62(6): 2469-2480.
[21] Rawlings J B, Mayne D Q, Diehl M.Model Predictive Control: Theory, Computation, and Design[M]. Madison, WI: Nob Hill Publishing, 2017.
[22] Nocedal J, Wright S.Numerical Optimization[M]. Berlin, Heidelberg ,Springer Science & Business Media, 2006.
[23] Andersson J A E, Gillis J, Horn G, et al. CasADi: A Software Framework for Nonlinear Optimization and Optimal Control[J]. Mathematical Programming Computation (S1867-2949), 2019, 11(1): 1-36.
[24] Wächter A, Biegler L T.On the Implementation of an Interior-Point Filter Line-Search Algorithm for Large- Scale Nonlinear Programming[J]. Mathematical Programming (S0025-5610), 2006, 106(1): 25-57.
[25] Kong J, Pfeiffer M, Schildbach G, et al.Kinematic and Dynamic Vehicle Models for Autonomous Driving Control Design[C]//2015 IEEE Intelligent Vehicles Symposium (IV), eoul, Korea (South): IEEE 2015: 1094-1099.

基于能量最优的无人驾驶汽车路径跟踪控制

Autonomous Vehicle Path Tracking Control System Based on Energy Optimization

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