智能车横向跟踪控制模式切换策略及评价方法

Lateral tracking control mode switching strategy and evaluation method for intelligent vehicle

针对智能车横向跟踪控制算法传统切换策略中模型匹配策略总是处于激活状态,占用计算资源和运动平顺性变差的问题,设计了一种基于最小二乘支持向量机(least squares support vector machine,LSSVM)的控制模式切换策略,在不同工况下根据误差判断策略切换控制模式.从安全性、舒适性和跟踪精度3个方面构建控制效果评估函数,并根据多目标优化数学模型实时执行系统最优控制量.仿真试验结果表明:该切换策略能够保证智能车在不同工况下具有优秀的横向跟踪性能,路径跟踪偏差范围为±0.1 m,横摆角速度波动范围为±2(°)/s,jerk值的范围为±10 m/s^(3),与传统的单一控制算法相比,具有更好的跟踪精度、稳定性和舒适性.

To solve the problem that in traditional switching strategies for lateral tracking control algorithms in intelligent vehicles,the model matching strategy was always in active state with consuming computational resources and reducing motion smoothness,a control mode switching strategy based on least squares support vector machine(LSSVM)was designed.By the strategy,the control modes were dynamically switched based on error assessment under varying operational conditions.The control effectiveness was evaluated from three aspects of safety,comfort and tracking accuracy.The optimal control amount was executed in real time based on a multi-objective optimization mathematical model.The simulation results show that by the switching strategy,the superior lateral tracking performance in intelligent vehicles can be achieved under varying conditions with path tracking deviation range of±0.1 m,yaw rate fluctuation range of±2(°)/s and jerk value range of±10 m/s^(3).Compared to traditional single control algorithms,the proposed strategy exhibits improved tracking accuracy,stability and comfort.