Adaptive iterative learning unified operation control for high-speed train considering electrical structure model and temperature compensation

In high-speed train operation control, the traction/braking force usually cannot be directly adjusted due to the nonlinear relationship with the motor armature current. In this paper, adaptive iterative learning operation control is studied for high-speed train. The traction and braking phases are simultaneously considered and an electrical structure model is proposed by using the armature current as control input. In order to improve the operation performance, the variations in air resistance coefficients are estimated by real-time temperature measurement and parameter compensation. The strength of this article resides in its proposal of a unified control strategy, which automatically toggles traction/braking signals during traction and braking phases by directly adjusting the current of the driven system. In addition, the speed constraints are considered to prevent the speed from exceeding safety limits. The stability of the closed-loop system is ensured by constructing a proper barrier composite energy function (BCEF), and the effectiveness of the proposed algorithm is validated through comparative simulations of case studies.