A data-driven predictive controller combined with the vector autoregressive with exogenous input model and the propagator estimation method for vehicle lateral stabilization

Abstract

A general predictive controller based on the subspace model identification method is proposed for vehicle stabilization. Traditional predictive controllers are always developed based on the principle model of vehicles, which inevitably suffers from parameter uncertainty and poor adaptability. In contrast to that, the proposed subspace-based general predictive controller is realized by a data-driven process and presents good adaptability in vehicle stability control. Inspired by subspace-based predictor construction, the keys of the predictive controller are as follows: (1) system model identification according to the model structure of the control object by input and output data; (2) output prediction of the system by the identified model; and (3) optimal control law designed by combining the linear–quadratic–Gaussian index with the predictive output. The main problem in the controller development lies in the recursive estimation of relevant matrices, which is limited by the subspace model identification theory. The implementation of the vector autoregressive with exogenous input model and the propagator method in subspace identification algorithm effectively solves the problem of estimation accuracy and calculation efficiency. Combined with a linear–quadratic–Gaussian index function, the predictive law for vehicle stability control is derived in detail. Finally, based on the vehicle model validated by standard road test, the effectiveness and robustness of the predictive controller are proved through the numerical simulations of various maneuvers under different road adhesive conditions.