This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Nonlinear Model Predictive Control of Autonomous Vehicles Considering Dynamic Stability Constraints
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Autonomous vehicle performance is increasingly highlighted in many highway driving scenarios, which leads to more priorities to vehicle stability as well as tracking accuracy. In this paper, a nonlinear model predictive controller for autonomous vehicle trajectory tracking is designed and verified through a real-time simulation bench of a virtual test track. The dynamic stability constraints of nonlinear model predictive control (NLMPC) are obtained by a novel quadrilateral stability region criterion instead of the conventional phase plane method using the double-line region. First, a typical lane change scene of overtaking is selected and a new composited trajectory model is proposed as a reference path that combines smoothness of sine wave and comfort of linear functional path. Reference lateral velocity, azimuth angle, yaw rate, and front wheel steering angle are subsequently taken into account. Then, by establishing a nonlinear vehicle dynamics model where Magic Formula of nonlinear tire model is adapted, the quadrilateral vehicle stability region is defined in consideration of designed velocity, road adhesion coefficient, and front wheel steering angle. Working condition-variant constraints determined by the boundaries of the quadrilateral region are subsequently obtained to guarantee the stability and vehicle performance. Finally, a nonlinear motion state space model with measured and unmeasured disturbance for NLMPC tracking maneuver is proposed, Meanwhile, a multi-objective cost function based on track error, ride comfort, and the smoothness of control derivative is established. Laguerre functions are applied to design optimal control trajectory and Hildreth’s quadratic programming procedure is introduced to find converged solutions meeting constraints derived from previously investigated quadrilateral stability region for sake of lightening computation load and finding better numerically conditioned solutions of control when NLMPC is implemented online. The configuration of a real-time virtual test track is explained and the NLMPC algorithm is validated. The simulation and experiment results are illustrated to show the effectiveness of the designed nonlinear model predictive control scheme under the test of the overtaking scene compared with the conventional driver control. This work may provide a useful basis for researches of autonomous vehicle lane change in terms of track accuracy, ride comfort as well as stability.
CitationChen, X., Wu, G., and Ren, M., "Nonlinear Model Predictive Control of Autonomous Vehicles Considering Dynamic Stability Constraints," SAE Technical Paper 2020-01-1400, 2020.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- González, D., Pérez, J., Milanés, V., and Nashashibi, F. , “A Review of Motion Planning Techniques for Automated Vehicles,” IEEE Transactions on Intelligent Transportation Systems 17(4):1135-1145, 2016, doi:10.1109/TITS.2015.2498841.
- Ding, Y., Zhuang, W., Qian, Y., and Zhong, H. , “Trajectory Planning for Automated Lane-Change on a Curved Road for Collision Avoidance,” SAE Technical Paper 2019-01-0673, 2019, https://doi.org/10.4271/2019-01-0673.
- Xu, W., Wei, J., Dolan, J., Zhao, H., and Zha, H. , “A Real-Time Motion Planner with Trajectory Optimization for Autonomous Vehicles,” Proc. IEEE ICRA2061-2067, May 2012.
- Li, H., Luo, Y., and Wu, J. , “Collision-Free Path Planning for Intelligent Vehicles Based on Bézier Curve,” IEEE Access 7:123334-123340, 2019, doi:10.1109/ACCESS.2019.2938179.
- Yang, Z., Zhijin, Q., and Yan, H. , “Trajectory Planning of Lane Changing for Intelligent Vehicles,” Journal of Chongqing Jiaotong University (Natural Science) 32(3):520-524, 2013, doi:10.3969/j.issn.1674-0696.2013.03.35.
- Wang, Y.Y., Pan, D., Liu, Z., and Feng, R. , “Study on Lane Change Trajectory Planning Considering of Driver Characteristics,” SAE Technical Paper 2018-01-1627, 2018, https://doi.org/10.4271/2018-01-1627.
- Majidi, M., Arab, M., and Tavoosi, V. , “Optimal Real-Time Trajectory Planning of Autonomous Ground Vehicles for Overtaking Moving Obstacles,” SAE Technical Paper 2017-01-0081, 2017, https://doi.org/10.4271/2017-01-0081.
- Mastinu, G., Della Rossa, F., Gobbi, M., and Previati, G. , “Bifurcation Analysis of a Car Model Running on an Even Surface - A Fundamental Study for Addressing Autonomous Vehicle Dynamics,” SAE Int. J. Veh. Dyn., Stab., and NVH 1(2), 2017, doi:10.4271/2017-01-1589.
- Chenchen, Z., Qunsheng, X., and He, L. , “A Study on the Influence of Sideslip Angle at Mass Center on Vehicle Stability,” Automotive Engineering 33(4):277-282, 2011, doi:10.19562/j.chinasae.qcgc.2011.04.001.
- Fei, L., Lu, X., Deng, L., Yuan, F. et al. , “Vehicle Stability Criterion Based on Phase Plane Method,” Journal of South China University of Technology (Natural Science Edition) 42(11):63-70, 2014, doi:10.3969/j.issn.1000-565X.2014.11.010.
- Liu, W., Xiong, L., Leng, B., Meng, H. et al. , “Vehicle Stability Criterion Research Based on Phase Plane Method,” SAE Technical Paper 2017-01-1560, 2017, doi:10.4271/2017-01-1560.
- Chung, T. and Yi, K. , “Design and Evaluation of Side Slip Angle-Based Vehicle Stability Control Scheme on a Virtual Test Track,” IEEE Transactions on Control Systems Technology 14(2):224-234, 2006.
- Hang, P., Chen, X., Luo, F., and Fang, S. , “Robust Control of a Four-Wheel-Independent-Steering Electric Vehicle for Path Tracking,” SAE Int. J. Veh. Dyn., Stab., and NVH 1(2), 2017, https://doi.org/10.4271/2017-01-1584.
- Liu, Y. and Cui, D. , “Application of Optimal Control Method to Path Tracking Problem of Vehicle,” SAE Int. J. Veh. Dyn., Stab., and NVH 3(3):209-219, 2019, https://doi.org/10.4271/10-03-03-0014.
- Wang, Z., Deng, W., Zhang, S., and Shi, J. , “Vehicle Automatic Lane Changing Based on Model Predictive Control,” SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 9(1), 2016, https://doi.org/10.4271/2016-01-0142.
- Wang, Y., Feng, R., Pan, D., Liu, Z. et al. , “The Trajectory Planning of the Lane Change Assist Based on the Model Predictive Control with Multi-Objective,” SAE Technical Paper 2017-01-2004, 2017, https://doi.org/10.4271/2017-01-2004.
- Novi, T., Liniger, A., Capitani, R., Fainello, M. et al. , “The Influence of Autonomous Driving on Passive Vehicle Dynamics,” SAE Int. J. Veh. Dyn., Stab., and NVH 2(4):285-295, 2018, https://doi.org/10.4271/2018-01-0551.
- Yu, J., Guo, X., Pei, X., Chen, Z. et al. , “Robust Model Predictive Control for Path Tracking of Autonomous Vehicle,” SAE Technical Paper 2019-01-0693, 2019, https://doi.org/10.4271/2019-01-0693.
- Wang, L. , “Model Predictive Control System Design and Implementation Using MATLAB,” 209-229, doi:10.1007/978-1-84882-331-0.
- Yangyang, Q., Guangqiang, W., and Xiaoxiao, G. , “Model Predictive Control Study of Electronic Throttle Based on Laguerre Functions,” Automobile Technology 1:33-37, 2017.
- Tosolin, G. and Ko, K. , “On the Use of Driver-in-the-Loop (DIL) Systems in Commercial Vehicle Chassis Development,” SAE Int. J. Veh. Dyn., Stab., and NVH 1(1), 2017, https://doi.org/10.4271/2017-26-0242.
- Brems, W., Kruithof, N., Uhlmann, R., Wagner, A. et al. , “New Motion Cueing Algorithm for Improved Evaluation of Vehicle Dynamics on a Driving Simulator,” SAE Int. J. Veh. Dyn., Stab., and NVH 1(2), 2017, https://doi.org/10.4271/2017-01-1566.