Efficient Nonlinear Model Predictive Control of Automated Vehicles

In this paper, an efficient model predictive control (MPC) of velocity tracking automated vehicles is proposed, in which a reference signal given priori. Five degree-of-freedom vehicle dynamics with nonlinear tires chosen as the prediction model, coupling characteristics longitudinal and lateral are taken into account. order to balance computational burden accuracy, Koopman operator theory adopted transform global linear model. Then, used design MPC reduce online avoid solving nonconvex/nonlinear optimization problems. Furthermore, effectiveness verified using Matlab/Simulink environment. Validation results demonstrate that dynamic mode decomposition (DMDc) extended (EDMD) algorithms more accurate validation than local linearization, DMDc algorithm has less on problems EDMD algorithm.