Fault-tolerant Control of Steer-by-Wire System Based on Sliding Mode Observer With Neural Network

This paper proposes a novel automatic disturbance rejection fault-tolerant control method based on a sliding mode observer with neural network for steer-by-wire (SbW) systems subjected to internal uncertainty, external disturbance, and steering motor fault. Firstly, to mitigate the adverse effects of non-smooth desired steering angle signals on the transient performance of the control system, a third-order tracking-differentiator is employed to smooth the desired signal. Secondly, a sliding mode observer with neural network is designed, which utilizes an adaptive radial basis function (RBF) neural network to reconstruct the internal and external uncertainties, as well as the fault-induced uncertainty, and employs the terminal sliding mode algorithm to ensure fast convergence of observation errors. The asymptotic stability of the observer is demonstrated by Lyapunov stability theory. Subsequently, a finite-time feedback control law is developed. To compensate for these uncertainties in the closed-loop system, the reconstruction results from the observer are incorporated into the control channel. On this basis, stability analysis shows that the tracking errors can converge to zero within a finite time under the nonlinear feedback control. Finally, the effectiveness and superiority of the proposed control method are verified through simulations conducted using Matlab/Simulink and CarSim.