Neuro-adaptive electric traction and braking control of high-speed train

Electric motors are the core driving units for high speed train consisting of locomotives and carriages. This paper investigates high precision speed control of the driving motor systems. More specifically, the problem of speed and back e.m.f control of motors via automatically regulating armature and field voltage is studied. The underlying system is inherently nonlinear with unknown and time-varying parameters due to uncertain disturbances. In this paper, an approach based on robust adaptive radial basis function (RBF) neural network (NN) is proposed to achieve motor speed tracking. This method is shown to be able to achieve speed and back e.m.f tracking with high precision, as confirmed both theoretical analysis and computer simulation.