基于MPC下车辆自主避障算法
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摘要
车辆在复杂路况下避障会出现稳定性与避免障碍物的行为冲突,提出了一种基于MPC的车辆避障控制算法。首先对车辆进行动力学建模,并加入轮胎非线性模型,再对其进行离散系统线性化。将路径跟踪、车辆稳定性和避免障碍物集成在一起,通过设定其优先级来协调行为冲突。最后通过MATLAB仿真验证算法性能,并利用CarSim/MATLAB联合仿真了该算法应对复杂路况的可行性。仿真结果表明:该控制算法在避障过程中能够协调好稳定性与避障的行为冲突,能够较为准确地表示出车辆的避障路径,为开发无人驾驶和优化智能车辆系统提供了重要依据。
The behavior of vehicle obstacle avoidance in complicated road conditions will conflict with the behavior of obstacle avoidance. An algorithm for vehicle obstacle avoidance control based on MPC is proposed.Firstly,the vehicle is modeled by dynamics,and the tire nonlinear model is added,and then the discrete system is linearized. Integrate path tracking,vehicle stability,and avoidance obstacles,and coordinate behavioral conflicts by setting their priorities. Finally,the performance of the algorithm is verified by MATLAB simulation,and the feasibility of the algorithm dealing with complex road conditions is simulated using CarSim/MATLAB. Simulation results show that the control algorithm can coordinate the behavior conflict between stability and obstacle avoidance during the obstacle avoidance process,and can accurately represent the obstacle avoidance path of the vehicle,providing an important basis for the development of driverless and optimization of intelligent vehicle systems.
The behavior of vehicle obstacle avoidance in complicated road conditions will conflict with the behavior of obstacle avoidance. An algorithm for vehicle obstacle avoidance control based on MPC is proposed.Firstly,the vehicle is modeled by dynamics,and the tire nonlinear model is added,and then the discrete system is linearized. Integrate path tracking,vehicle stability,and avoidance obstacles,and coordinate behavioral conflicts by setting their priorities. Finally,the performance of the algorithm is verified by MATLAB simulation,and the feasibility of the algorithm dealing with complex road conditions is simulated using CarSim/MATLAB. Simulation results show that the control algorithm can coordinate the behavior conflict between stability and obstacle avoidance during the obstacle avoidance process,and can accurately represent the obstacle avoidance path of the vehicle,providing an important basis for the development of driverless and optimization of intelligent vehicle systems.
引文
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[2]崔佳超.无人驾驶智能车在动态环境中的避障方法[D].西安:西安工业大学,2015.
[3]JANSSON J.Decision making for collision avoidance system[C]∥Society of Automotive Engineers,2002:10.
[4]MARKKULA G.A review of near-collision driver behavior models[J].Human Factors,2012,54(6):1117-1143.
[5]汤沛,魏民祥,赵万忠.汽车避障工况下稳定性控制研究[J].中国安全科学学报,2016,26(9):96-100.
[6]LEE S E,LLANERAS E.Analyses of rear-end crashes and nearcrashes in the 100-car naturalistic driving study to support rearsignaling countermeasure development[R].Washington DC:National Highway Traffic Safety Administration,2007,810:846.
[7]朱麒融.无人驾驶汽车避障方法探析[J].工业技术,2016,14(21):53-54,56.
[8]刘卉,张广明,欧阳慧珉.基于模型预测控制的轨迹跟踪算法研究[J].控制工程,2016,23(S1):61-65.
[9]朱伟达.基于改进型人工势场法的车辆避障路径规划研究[D].镇江:江苏大学,2016.
[10]FALCONE P,BORRELLI F,ASGARI J,et al.Predictive active steering control for autonomous vehicle systems[J].IEEE Control Systems Society,2007,15(3):566-580.
[11]FALCONE P.Linear time-varying model predictive control and its application to active steering systems:Stability analysis and experimental validation[J].International Journal of Robust and Nonlinear Control,2008,18(8):862-875.
[12]KATRINIOK J,MASCHUW P,CHRISTEN F,et al.Optimal vehicle dynamics control for combined longitudinal and lateral autonomous vehicle guidance[C]∥European Control Conference(ECC),IEEE,2013:974-979.
[13]FALCONE P,BORRELLI F,ASGARI J,et al.A hierarchical model predictive control framework for autonomousground vehicles[C]∥IEEE American Control Conference,2008:3719-3724.
[14]GAO Y,LIN T,BORRELLI F,et al.Predictive control of autonomous ground vehicles with obstacle avoidance onslippery roads[C]∥ASME 2010 Dynamic Systems and Control Conference,2010:265-272.
[15]BROWNN M,FUNKE J,ERLIEN S,et al.Safe driving envelopes for path tracking in autonomous vehicles[J].Control Engineering Practice,2017,61:307-316.
[16]刘英杰,刘云,郭姗姗.基于模型预测控制的汽车操纵逆问题研究[J].系统仿真学报,2018,30(1):304-310.
[17]HONG Y G,CHEN S,ZHANG H.Simultaneous trajectory planning and tracking using an MPC method for cyber-physical systems:A case study of obstacle avoidance for an intelligent vehicle[J].IEEETransaction on Industrial Informatics,2018,14(9):4273-4283.