Quadratic programming-based cooperative adaptive cruise control under uncertainty via receding horizon strategy

Cooperative longitudinal motion control can greatly contribute to safety, mobility, and sustainability issues in today's transportation systems. This article deals with the development of cooperative adaptive cruise control (CACC) under uncertainty using a model predictive control strategy. Specifically, uncertainties arising in the system are presented as disturbances acting in the system and measurement equations in a state-space formulation. We aim to design a predictive controller under a common goal (cooperative control) such that the equilibrium from initial condition of vehicles will remain stable under disturbances. The state estimation problem is handled by a Kalman filter and the optimal control problem is formulated by the quadratic programming method under both state and input constraints considering traffic safety, efficiency, as well as driving comfort. In the sequel, adopting the CACC system in four-vehicle platoon scenarios are tested via MATLAB/Simulink for cooperative vehicle platooning control under different disturbance realizations. Moreover, the computational effectiveness of the proposed control strategy is verified with respect to different platoon sizes for possible real-time deployment in next-generation cooperative vehicles.