机械弹性电动轮驱动车辆横摆稳定性协调控制

Yaw stability coordinated control of mechanical elastic electric wheeldrive vehicles

针对匹配机械弹性电动轮(MEEW)车辆的横摆稳定性控制问题,提出一种基于主动前轮转向(AFS)与直接横摆力矩控制(DYC)的稳定性协调控制策略。为修正车辆行驶过程中的前轮转角输入,设计了基于微分平坦与RBF神经网络的AFS控制器,从而提高车辆的转向能力。针对AFS控制器在极限工况下易失效的缺陷,引入基于线性二次型调节器(LQR)的直接横摆力矩控制算法,并依照轴荷比分配四轮力矩。最后,依据机械弹性电动轮的质心侧偏角-质心侧偏角速度相平面图划分稳定域,实现AFS与DYC的协调控制。通过Matlab/Simulink和Carsim进行联合仿真,结果表明:所提出的AFS控制算法在高速高附着工况下有良好的稳定控制性能,但在高速、低附着极限工况下控制效果受到影响。而AFS/DYC协调控制策略效果较好,跟踪精度优于单一控制器,质心侧偏角和横摆角速度的最大跟踪误差仅为3.03°和1.82(°)/s,可保证汽车在极限工况下转向时的横摆稳定性。

To address the yaw stability control of vehicles matched with mechanical elastic electric wheel(MEEW),this paper proposes a coordinated stability control strategy of active front steering(AFS)and direct yaw moment control(DYC).To correct the front-wheel angle input during vehicle driving,an AFS controller is designed on the basis of differential flatness and RBF neural network,thereby improving the vehicle steering ability.Meanwhile,as the AFS controller is prone to failure under extreme conditions,a direct yaw moment control algorithm based on linear quadratic regulator(LQR)is introduced,and the four-wheel torques are allocated according to the axle-load ratio.Finally,the stability domain is divided by means of the sideslip angle-slip angle rate phase portrait of MEEW to realize the coordinated control of AFS and DYC.Our joint simulations of Matlab/Simulink and Carsim show the proposed AFS control algorithm performs well in stability control under high-speed and high-adhesion conditions,and its performance is affected by extreme conditions of high velocity and low adhesion.However,the AFS/DYC coordinated control scheme performs better,and the tracking effectiveness is superior to single controller.The maximum tracking errors of side slip angle and yaw rate are 3.03°and 1.82(°)/s respectively,achieving vehicle yaw stability under extreme steering conditions.