Adaptive Command Filtered Backstepping Control of Connected Vehicles with Nonlinear Disturbances

This paper investigates the problem of adaptive control of connected vehicles subject to uncertain nonlinearity. A third-order nonlinear error model is established, which considers the effect of nonlinear vehicle dynamics and unknown acceleration disturbances. To deal with the nonlinear dynamics and uncertainty, the command filtering technique is used to design the controller, where the radial basis function neural networks (RBFNNs) is introduced to estimate and compensate for the uncertainty. It is evident that the problem of "explosion of complexity" is solved when the backstepping method is involved. Both the individual vehicle stability and the disturbance string stability can be guaranteed by the designed controller. The effectiveness of the proposed method is illustrated by the numerical simulations.