基于MPC的智能车轨迹跟踪算法
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摘要
针对前轮转向的智能汽车,设计了一种轨迹跟踪控制算法。基于车辆运动学模型,建立了车辆轨迹跟踪状态空间方程,采用模型预测控制算法,以考虑乘坐舒适性下动态跟踪偏差最小为控制目标,同时考虑车辆实际情况对模型添加了控制约束和状态约束,通过滚动优化和反馈校正实现了带约束的智能车轨迹跟踪的最优控制。为了验证该算法的性能,将该算法在常用于自动驾驶仿真的PreScan上进行了硬件在环仿真试验,并与LQR算法作比较。结果表明,该算法能有效适用于约束条件下的智能车轨迹跟踪控制。
A trajectory tracking control algorithm was designed for intelligent vehicle with front wheel steering.Based on the vehicle kinematics model,the state-space equation of vehicle trajectory tracking was established.The model predictive control algorithm was adopted with the control target to minimize dynamic tracking error under the consideration of ride comfort,and control constraints and state constraints were added to the model considering the actual situation of the vehicle.The optimal control of intelligent vehicle trajectory tracking with constrains was realized by rolling optimization and feedback correction.In order to verify the performance of proposed algorithm,it was compared with LQR algorithm,a hardware-in-the-loop simulation experiment on PreScan platform with proposed algorithm was conducted.The PreScan platform has been widely used in automatic driving simulation.The results show that the algorithm is more effective and applicable to the intelligent vehicle trajectory tracking control under constraint conditions.
A trajectory tracking control algorithm was designed for intelligent vehicle with front wheel steering.Based on the vehicle kinematics model,the state-space equation of vehicle trajectory tracking was established.The model predictive control algorithm was adopted with the control target to minimize dynamic tracking error under the consideration of ride comfort,and control constraints and state constraints were added to the model considering the actual situation of the vehicle.The optimal control of intelligent vehicle trajectory tracking with constrains was realized by rolling optimization and feedback correction.In order to verify the performance of proposed algorithm,it was compared with LQR algorithm,a hardware-in-the-loop simulation experiment on PreScan platform with proposed algorithm was conducted.The PreScan platform has been widely used in automatic driving simulation.The results show that the algorithm is more effective and applicable to the intelligent vehicle trajectory tracking control under constraint conditions.
引文
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[4]Enzeng D,Shuxiang G,Xichuan L,et al.A neural network-based self-tuning PID controller of an autonomous underwater vehicle[C]//IEEE International Conference on Mechatronics and Automation,2012:898-903.
[5]陈无畏,王家恩,汪明磊,等.视觉导航智能车辆横向运动的自适应预瞄控制[J].中国机械工程,2014,25(5):698-704.
[6]Jinghua G,Ping H,Linhui L,et al.Design of automatic steering controller for trajectory tracking of unmanned vehicles using genetic algorithms[J].IEEETransactions on Vehicular Technology,2012,61(7):2913-2924.
[7]Mashadi B,Ahmadizadeh P,Majidi M.Integrated controller design for path following in autonomous vehicles[C]//SAE 2011World Congress,2011.
[8]Snider Jarrod M.Automatic steering methods for autonomous automobile path tracking[D].Pennsylvania:Carnegie Mellon University,2009.
[9]Levinson J,Askeland J,Becker J,et al.Towards fully autonomous driving:systems and algorithms[C]//IEEEIntelligent Vehicles Symposium,2011:163-168.
[10]陈慧岩.无人驾驶汽车概论[M].北京:北京理工大学出版社,2014.
[11]张琨.智能汽车自主循迹控制策略研究[D].哈尔滨:哈尔滨工业大学,2013.
[12]龚建伟,姜岩,徐威.无人驾驶车辆模型预测控制[M].北京:北京理工大学出版社,2014.
[13]刘豹,唐万生.现代控制理论[M].北京:机械工业出版社,2006.
[14]席裕庚.预测控制[M].北京:国防工业出版社,2013.
[15]Gill P E.SNOPT:An SQP algorithm for large-scale constrained optimization[J].Siam Review,2005,47(1):99-131.
[16]Gill P E,Murray W,Saunders M A.User’s guide for SNOPT version 7:software for large-scale nonlinear programming[R].Stamford:Department of Operations Research,Stanford University,2012.
[17]Miao Q,Tang X,Wang D,et al.The application of PRESCAN in the concept development of active safety system[C]//The 9th International Forum of Automotive Traffic Safety,2011:884-886.
[18]赵剑,管迪华.独立悬架汽车转向系统刚度测量[J].汽车工程,2001,23(5):337-339.
[19]郑宏宇,宗长富,田承伟,等.基于理想转向传动比的汽车线控转向控制算法[J].吉林大学学报(工),2007,37(6):1229-1235.