Adaptive neural control of vehicular platoons with unknown functions and full state constraints

This paper investigates the adaptive neural control of vehicular platoons subject to unknown nonlinear functions and full-state constraints. To address the challenges posed by unknown functions, the neural network technology is integrated into the backstepping control framework. Additionally, the time-varying constraints on position, velocity, and acceleration are effectively managed through the application of tangent barrier Lyapunov functions. Notably, the proposed approach successfully avoids the singularity problem. Based on Lyapunov stability theory, it is rigorously shown that the closed-loop system remains bounded, with system states and error signals strictly confined within the prescribed constraint boundaries. Finally, a numerical example is presented to validate the effectiveness and feasibility of the proposed control scheme.