高速列车数据驱动无模型自适应容错控制

Data-driven model-free adaptive fault tolerant control for high-speed trains

针对高速列车运行控制中的牵引/制动力约束和执行器故障问题,提出一种基于偏格式动态线性化的无模型自适应容错控制(PFDL-MFAFTC)算法.首先,利用无模型自适应控制框架下的伪梯度概念,将难以精确获取参数(列车质量、阻力以及执行器故障等)的高速列车动力学模型转化为偏格式动态线性化数据模型;其次,利用径向基函数神经网络(RBFNN)处理执行器故障引起的非线性;然后,通过压缩映射方法对算法进行严格的收敛性证明,保证算法的收敛性;最后,通过高速列车仿真验证PFDL-MFAFTC算法的有效性和容错能力.

A data-driven model-free adaptive fault-tolerant control algorithm based on partial form dynamic linearization(PFDL-MFAFTC)is proposed to solve the problems of traction/braking force constraint and actuator faults for high-speed train operation control.Firstly,using the concept of pseudo gradient in the model-free adaptive control framework,the dynamic model of a high-speed train,which is difficult to accurately obtain parameters such as train mass,resistance and actuator faults,is transformed into a partial format dynamic linearization data model.Secondly,the radial basis function neural network(RBFNN)is used to deal with the nonlinear function caused by actuator faults.Then,the convergence of the PFDL-MFAFTC algorithm is guaranteed by utilizing the contraction mapping method.Finally,the effectiveness of the PFDL-MFAFTC algorithm is verified by a high-speed train numerical simulation.