EKF在机车最优黏着控制中的应用

Application of EKF in locomotive optimal adhesion control

为了使机车工作在最优黏着状态,提出了一种基于扩展卡尔曼滤波(EKF)的滑模控制算法。首先以轮轨动力学及机车黏着模型为基础,利用EKF对机车的轮对速度和车体速度进行近似估计;然后为了克服外界干扰对系统的影响,提出了一种基于指数收敛干扰观测器的滑模控制算法;同时考虑轮轨最优蠕滑速度未知的问题,根据机车黏着模型设计一种搜索算法用于跟踪搜索当前路况的最优蠕滑速度。仿真试验结果表明,基于EKF的滑模控制算法可以有效提高机车运行的稳定性并且提高黏着利用率。

In order to make the locomotive work in the optimal adhesion state,a sliding mode control algorithm based on extended Kalman filter(EKF)is proposed.Firstly,based on the wheel-rail dynamics and the locomotive adhesion model,the EKF is used to approximate the wheelset speed and the vehicle body speed of the locomotive.Then,in order to overcome the external disturbance on the system,a sliding mode control algorithm based on exponential convergence disturbance observer is proposed.Considering the problem that the optimal wheel-rail creep speed is unknown,a variable step-length search algorithm is designed according to the locomotive sticking model to track the optimal creep speed of the current track surface.Simulation results show that the EKF-based sliding mode control algorithm can effectively improve the stability of locomotive operation and the utilization of adhesion.