基于禁区惩罚函数和MPC倍增预测的车辆避障研究
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摘要
为了增强无人驾驶汽车对障碍物预测及躲避的可靠性,以模型预测控制(MPC)和车辆动力学模型为基础,提出了一种基于障碍物禁区惩罚函数和MPC预测距离倍增法的避障控制策略。考虑车辆的尺寸,对于障碍物边界的确定引入以阶跃函数为基础的禁区边界惩罚函数。同时在障碍预测环节中对预测距离进行倍数扩增,提高了车辆在较远距离处对障碍物的预测能力。仿真实验以车辆动力学平台Car Sim为基础,结合Matlab/Simulink的S函数,对不同仿真工况进行测试。实验结果表明车辆可以避开给定障碍物并能够返回到原始路径,结果达到预期要求,验证了算法的可行性。
To improve the reliability of predicting and avoiding obstacles of unmanned vehicle, based on MPC method and vehicle dynamic model, the paper puts forward an obstacle-avoidance-control strategy based on penalty function of restricted areas of the obstacles and multiplication of predictive distance. Taking the size of vehicle into consideration, the step function based penalty function of restricted boundaries is introduced for defining obstacle borders. Meanwhile, in the process of obstacle prediction, the predictive distance is multiplied, improving the ability of obstacle prediction of vehicle in a far distance. Simulation test is based on vehicle dynamics platform Car Sim, combined with S-function in Matlab/Simulink. Different operating modes are tested. Results show the vehicle can avoid the obstacle given and return to the original path, which meets the expected requirement, verifying the feasibility of the algorithm.
To improve the reliability of predicting and avoiding obstacles of unmanned vehicle, based on MPC method and vehicle dynamic model, the paper puts forward an obstacle-avoidance-control strategy based on penalty function of restricted areas of the obstacles and multiplication of predictive distance. Taking the size of vehicle into consideration, the step function based penalty function of restricted boundaries is introduced for defining obstacle borders. Meanwhile, in the process of obstacle prediction, the predictive distance is multiplied, improving the ability of obstacle prediction of vehicle in a far distance. Simulation test is based on vehicle dynamics platform Car Sim, combined with S-function in Matlab/Simulink. Different operating modes are tested. Results show the vehicle can avoid the obstacle given and return to the original path, which meets the expected requirement, verifying the feasibility of the algorithm.
引文
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[14]Falcone P.Nonlinear model predictive control for autonomous vehicles[D].Benevento:Universitàdel Sannio,2007.
[15]Falcone P,Eric Tseng H,Borrelli F,et al.MPC-based yaw and lateral stablization via active front steering and braking[J].Vehicle System Dynamics,2008,46:611-628.
[2]席裕庚,李德伟,林姝.模型预测控制--现状与挑战[J].自动化学报,2013,39(3):222.
[3]余如,郭洪艳,陈虹.自主驾驶车辆的预测避障控制[J].信息与控制,2015,44(1):117-124.
[4]曹阳,贺登博,喻凡,等.基于主动转向的车辆路径跟随广义预测控制[J].上海交通大学学报,2016,50(3):401-406.
[5]王浩,林棻,张尧文.基于模拟退火算法的无人驾驶车辆轨迹跟踪控制[J].重庆理工大学学报:自然科学版,2015,29(11):106-111.
[6]Balachandran A,Brown M,Erlien S M,et al.Predictive haptic feedback for obstacle avoidance based on model predictive control[J].IEEE Transactions on Automation Science and Engineering,2016,13(1):26-31.
[7]Liu J C,Jayakumar P,Stein J L,et al.A study on model fidelity for model predictive control-based obstacle avoidance in high-speed autonomous ground vehicles[J].Vehicle System Dynamics,2016,54(11):1629-1650.
[8]Jiang H J,Wang Z H P,Chen,Q J,et al.Obstacle avoidance of autonomous vehicles with CQP-based model predictive control[C]//2016 IEEE International Conference on Systems,Man,and Cybernetics,2016:1668-1673.
[9]Mousavi M A,Heshmati Z,Moshiri B,et al.LTV-MPCbased path planning of an autonomous vehicle via convex optimization[C]//21st Iranian Conference on Electrical Engineering,2013:1-7.
[10]Qian X J,La Fortelle A,Moutarde F,et al.A hierarchical model predictive control framework for on-road formation control of autonomous vehicles[C]//2016 IEEEIntelligent Vehicles Symposium,2016:376-381.
[11]Pacejka H.Tyre and vehicle dynamics[M].[S.l.]:Butterworth-Heinemann,2006.
[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,2014:19-81.
[13]李升波,王建强,李克强.软约束线性模型预测控制系统的稳定性方法[J].清华大学学报:自然科学版,2010,11:1848-1852.
[14]Falcone P.Nonlinear model predictive control for autonomous vehicles[D].Benevento:Universitàdel Sannio,2007.
[15]Falcone P,Eric Tseng H,Borrelli F,et al.MPC-based yaw and lateral stablization via active front steering and braking[J].Vehicle System Dynamics,2008,46:611-628.