An adaptive robust path-tracking control algorithm of multi-articulated and redundantly actuated virtual track train

Multi-articulated and redundantly actuated virtual track trains (VTTs) are inevitably exposed to unknown exogenous disturbances and various operating conditions. To address the complex over-actuated tracking-control problem, this study proposes a decoupling control scheme for an all-wheel independent drive and all-wheel active steering VTT composed of multiple modules, wherein the adaptive robust path-tracking and longitudinal speed control algorithms of the VTT are decoupled. An appropriate control-oriented nonlinear dynamic model for VTT path-tracking control is formulated using Lagrange's equations. To compensate for the model uncertainties and environmental disturbances without priors and parameter estimations, a robust control algorithm using radial basis function neural network and sliding mode is proposed. The asymptotic stability of the algorithm is proved, and its adaptation law is derived using the Lyapunov theory. Moreover, a novel traction/braking control algorithm based on the wheel speed distribution model and double closed-loop control algorithm is suggested. Using a multi-body dynamic model of the VTT with seven modules, simulations are performed to evaluate the effectiveness and superiority of the proposed path-tracking algorithm. The obtained results indicate that the proposed algorithm could realise the expectable performance of the VTT path tracking and longitudinal speed control. Moreover, the proposed control algorithm has good scalability for multi-articulated and redundantly actuated VTT.