参数不确定和扰动下智能汽车路径跟踪控制

Intelligent vehicle path tracking control under parametric uncertainties and external disturbances

针对智能汽车在路径跟踪过程中因模型参数不确定性、外部干扰、系统状态时变性非线性等导致跟踪精度较低、鲁棒性较差的问题,设计基于鲁棒预测控制(RMPC)的路径跟踪控制方法.考虑轮胎的非线性特性,对轮胎侧偏刚度进行修正.考虑纵向车速时变性,利用有限个多胞体顶点描述车辆纵向车速,建立离散的车辆多胞不确定模型.根据Lyapunov渐进稳定性和无穷时域二次型性能指标,采用带松弛变量的线性矩阵不等式(LMI),求解优化问题.利用改进后的离线鲁棒预测控制算法,提高了控制器的实时性并降低了保守性.通过SimulinkCarsim联合仿真和硬件在环试验,验证了控制器的有效性.仿真结果表明,所设计的控制器具有较好的跟踪精度和鲁棒性.

A path tracking method based on robust model predictive control(RMPC)was designed aiming at the problems of low tracking accuracy and poor robustness caused by model parameter uncertainties,external disturbance,system state time-varying and nonlinearities in the process of path tracking of intelligent vehicles.The nonlinear characteristic of tire was considered,and the cornering stiffness of the tire was modified.Then the timevarying longitudinal speed was considered and a polytope with finite vertices was used to describe the longitudinal vehicle speed.A discrete vehicle polytopic uncertainty model was established.Linear matrix inequalities(LMI)with slack variables were derived to solve the optimization problem according to the Lyapunov asymptotic stability and quadratic performance index in infinite horizon.An improved offline robust model predictive control algorithm was proposed to improve the real-time performance of the controller and reduce its conservativeness.The effectiveness of the controller was verified by Simulink-Carsim co-simulation and hardware-in-the-loop tests.The simulation results showed that the designed controller had good tracking accuracy and robustness.