An extended Kalman filter-based versatile adaptive iteration learning control for heavy-haul train trajectory tracking control

In this paper, an extended Kalman filter-based versatile adaptive iteration learning control (EKF-based VAILC) algorithm is proposed for heavy-haul train to achieve precise speed and position tracking in different operating conditions. Firstly, the cyclic air braking control problem under continuous long steep downhill operation condition is incorporated into the adaptive iteration learning control (AILC) scheme. Meanwhile, the constraints of traction and electrical braking force, as well as, speed limits are fully considered to improve the practicability of the proposed controller. Further, an extended Kalman filter (EKF) is applied for estimating states of train on real-time and handling disturbances. Finally, the convergence of the proposed system in iteration and time domain are analyzed through barrier composite energy function (BCEF) method. Simulations under two typical operating conditions are conducted to verify the effectiveness and versatility of the developed EKF-based VAILC. The results demonstrate that the proposed control method is suitable for cyclic air braking mode, and has great tracking performance. Moreover, compared with the traditional AILC, this method also exhibits better anti-disturbance ability.