Learning-Based Platooning Control of Automated Vehicles via Multiple Description Encoding Mechanisms

The growing urbanization and the development of new energy automobiles have motivated a significant increase in the number of vehicles on the highway, which has put increasing pressure on existing transportation systems. Platooning control is an effective scheme to enhance road traffic safety and alleviate traffic congestion. In this paper, platooning control under multiple description encoding schemes (MDESs) is investigated for automated vehicles with nonlinear dynamics. A learning-based observer with MDESs is first utilized to acquire the vehicular state, where a neural network with a weight updating rule is created to approximate unknown nonlinearities. In light of estimated states as well as the features of encoding-decoding errors, some sufficient conditions are derived in the framework of the Lyapunov stability to attain the desired platoon with given constant inter-vehicle spacing. In addition, a set of matrix inequalities is used to solve the desired gain parameters based on the maximum and minimum eigenvalues of the communication topology. Finally, an illustrative example demonstrates the validity of the developed control scheme.