Distributed integrated sliding mode control via neural network and disturbance observer for heterogeneous vehicle systems with uncertainties

Abstract

A distributed sliding mode control based on neural network and disturbance observer is investigated for heterogeneous vehicle systems in this paper. The vehicle systems in this paper consider both parameter uncertainty and disturbance. First, the radial basis function neural network is applied to estimate the parameter uncertainty owing to its universal approximation ability, and the disturbance observer is designed to compensate for the external disturbance. Compared with the existing adaptive methods, the disturbance observer can estimate the external disturbance directly and effectively. Then, constant time headway policy is employed to regulate the inter-vehicle distance and improve the string stability. Unlike most existing sliding mode control strategy for vehicle platoons, the string stability is achieved via employing sufficient conditions on the control parameters rather than employing coupled sliding mode control. Afterward, modified constant time headway policy is designed to reduce the effect of nonzero initial inter-vehicle spacing and improve the traffic density. Finally, the simulation results with constant time headway policy and modified constant time headway policy are provided to demonstrate the effectiveness and advantages of the proposed approaches.