Adaptive control of vehicle platoon with uncertain target trajectory and unknown control direction

Abstract

This paper addresses the adaptive control problem for vehicle platoon systems (VPSs) with uncertain target trajectory and unknown control direction. Unlike the existing control methods, an online real‐time reconstruction method is introduced to predict the reference trajectory utilizing the generalized regression neural network (GRNN) according to some historical data. On this basis, an adaptive control strategy based on a dynamic‐memory event‐triggered mechanism (DMETM) is developed, and the Nussbaum‐type function method is utilized to handle the unknown control gain. The presented control strategy allows the vehicle to trace the predicted position value of the uncertain target. Under the proposed adaptive neural controller, it can be proved that all signals within the closed‐loop system maintain semi‐global uniform boundedness. Finally, a simulation example validates the effectiveness and feasibility of the proposed control method.