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Neuro-adaptive Anti-slip Brake Control of High-Speed Trains

机译：高速列车的神经自适应防滑制动控制

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This paper investigates the problem of anti-slip brake control of high-speed train.An adaptive neural network control strategy without using precise system parameters is proposed to counteract modeling uncertainties and unexpected disturbances.Moreover,various anomaly factors such as varying and uncertain operation environment,the unknown nature of the wheel/rail contact surface and unexpected geological hazards are taken into consideration in control design.Adaptive variable structure observer is constructed for estimating the adhesion force.Reference slip ratio generation algorithm using fuzzy logic is developed to determine the desired slip ratio for a large adhesion force.The resultant control algorithms are not only independent of the dynamic model,but also robust against the modeling uncertainties and external disturbances.The performance and robustness of control scheme is evaluated through computer simulation.
机译：本文研究了高速列车的防滑制动控制问题。提出了一种不使用精确系统参数的自适应神经网络控制策略来抵消建模的不确定性和意料之外的干扰。此外，还存在各种异常因素，如运行环境的变化和不确定。在控制设计中考虑了轮/轨接触面的未知性质和意外的地质危害。构造了自适应变结构观测器以估算附着力。开发了使用模糊逻辑的参考滑移率生成算法来确定所需的滑移率所得到的控制算法不仅独立于动力学模型，而且对建模的不确定性和外部干扰具有鲁棒性。通过计算机仿真对控制方案的性能和鲁棒性进行了评估。

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