Adaptive optimal control for integrated active front steering and direct yaw moment based on approximate dynamic programming

Abstract

In this paper, a novel adaptive optimal control algorithm based on approximate dynamic programming (ADP) approach is proposed for integrated active front steering (AFS) and direct yaw moment control (DYC). The corrective yaw moment and active steering angle are generated online without knowing system dynamics, which is realised by using a neural network (NN) identifier to identify the unknown system dynamics and a critic NN to calculate the optimal control action, respectively. Control commands are executed via active steering angle on front wheels and proper brake torque distribution on the effective wheels. Computer simulations under three different driving manoeuvres, i.e., lane change manoeuvre, step steer manoeuvre and sine with dwell manoeuvre, are carried out to evaluate the proposed control method. Simulation results show that the proposed ADP-based control method demonstrates improved tracking performance in terms of enhancing vehicle handling and stability performance when encountering the varying longitudinal velocity, the uncertain cornering stiffness and the different road/tyre friction coefficients. Model-free and self-adaptive properties of the proposed method provide a new solution to vehicle stability controller design instead of the commonly used model-based methods.