Neuro-adaptive fault-tolerant control of high speed trains under traction-braking failures using self-structuring neural networks

Neuro-adaptive fault-tolerant control of high speed trains under traction-braking failures using self-structuring neural networks
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使用自结构化神经网络对牵引制动故障下的高速列车进行神经自适应容错控制

DOI:
10.1016/j.ins.2016.05.033
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发表时间:
2016-11
影响因子:
8.1
通讯作者:
高瑞贞
高瑞贞
中科院分区:
计算机科学1区
文献类型:
--
作者:
高瑞贞

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针对不确定系统非线性和执行器故障情况下高速列车的位置和速度跟踪控制,提出了一种自适应控制方案。将具有自结构能力的神经网络集成到控制设计中,其神经元数量可以在线自动调整,不仅避免了固定结构神经网络固有的问题,而且有效地减弱了非线性列车力、牵引/制动不确定动力学以及未知驱动故障所带来的负面影响。理论分析和数值仿真结果表明,所建立的控制算法能够在不同的运行条件下实现高精度的列车速度和位置跟踪。
This paper develops an adaptive control scheme for position and velocity tracking control of high speed trains under uncertain system nonlinearities and actuator failures. Neural networks with self-structuring capabilities are integrated into control design, where the number of the neurons can be adjusted online automatically, so that not only the problem inherent in the NN with fixed structure is avoided, but also the negative impacts arising from nonlinear in-train forces, traction/braking uncertain dynamics as well as the unknown actuation faults are effectively attenuated. It is shown that the resultant control algorithms are able to achieve high precision train speed and position tracking under varying operation railway conditions, as validated by theoretical analysis and numerical simulations.
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