Adaptive finite-time fuzzy control for hybrid levitation system of maglev trains with active anti-lock constraints

Abstract

The great risk of the suspension contact tremendously restricts the practical public service of hybrid maglev trains. If an operational train contacts to the guideway resulting in the “lock” state, it would cut down its lifetime or even endanger passenger safety. To address the deadlock problem of hybrid maglev trains, a novel adaptive finite-time fuzzy control with active anti-lock constraints is proposed in this paper. First, an efficient fuzzy-logic system is applied to approximate the hybrid levitation dynamics, not only precisely describing the system but also reducing computation burden. Moreover, by a novel nonlinear coordinate transformation, an anti-lock levitation controller is designed to prevent suspension contact between trains and guideways via the back-stepping technique. In the process, command filtering is utilized to circumvent the derivatives of virtual control variables and to address practical input constraints. Differing from the barrier Lyapunov function technique, the proposed nonlinear transformation helps to directly address both positive lower and upper boundaries. In addition, finite time convergence is achieved by the proposed scheme, which enjoys the characteristics of a fast and quantifiable response. Numerical simulations verify the theoretical results.