Adaptive Control and Robust MPC for Linearising Longitudinal Vehicle Dynamics for Platooning Applications

Vehicle platooning is a promising cooperative driving vision where a group of consecutive connected autonomous vehicles (CAVs) travel at the same speed with the aim of improving fuel efficiency, road safety, and road usage. To achieve the benefits promised through platooning, platoon control algorithms must coordinate the dynamics of CAVs such that the closed-loop system is stable, errors between consecutive vehicles do not amplify along the string, and the time for reestablish the platoon formation to changes in the operating conditions does not diverge when the number of CAVs increases. Linear longitudinal vehicle dynamics are often assumed in the literature to guarantee such stringent platoon control requirements and they can be attained by equipping vehicles in the fleet with mid-level control systems. However, model uncertainties and disturbances can jeopardise the tracking of the reference linear behaviour. Hence, this paper presents for the first time, at the best of the authors' knowledge, the design and the performance of an adaptive control strategy and a robust model predictive control method as possible solutions for the mid-level control problem. Numerical results confirm that both control techniques are effective at imposing the dynamics of a linear time-invariant system to the longitudinal vehicle motion and they outperform model-based feedback linearisation methods when the parameters of the nonlinear longitudinal vehicle model are affected by uncertainties.