Adaptive iterative learning for heavy-haul trains trajectory tracking control on long steep downhill sections

• Main Authors: Wei, Mi, Pengfei, Sun, Qingyuan, Wang, ZiPei, Zhang, Chuanru, Wang, Chuanxin, Zhang
• Format: Conference Proceeding
• Language: English
• Subjects: Adaptive iterative learning control (AILC); cyclic air braking; heavy-haul trains; long steep downhill sections; Resistance; Simulation; Stability analysis; Timing; Trajectory tracking; trajectory tracking control; Uncertain systems; Uncertainty
• ISSN: 2158-2297
• DOI: 10.1109/ICIEA54703.2022.10005902

The cyclic air braking strategy on the long steep downhill sections is applied to ensure safety of heavy-haul trains. At present, the driver drives manually according to the fixed operation pattern due to the complex line conditions and multiple control constraints on the cyclic air braking procedure that causes operation difficulty and even security risk. In this paper, an adaptive iterative learning control (AILC) considering the operation strategy of cyclic air braking is designed to achieve the accuracy of the velocity and displacement tracking for heavy-haul trains on long steep downhill, in which the time-varying parameter uncertainties of the basic resistance and other integrated uncertainties during operation are taken fully into account. The composite energy function (CEF) method is utilized to describe the stability of the designed controller, with the development of iteration, the tracking error of velocity and displacement are both gradually decreased. The simulations demonstrate that the proposed algorithm can reliably choose proper timings of the implementing and releasing of cyclic air braking and have high trajectory tracking performance.