The invention is suitable for the technical field of vehicle
intelligent control, and provides a
model prediction controller parameter self-calibration method based on deep
reinforcement learning. According to the method, modeling expression is carried out on the
delay characteristic of a vehicle
steering system by utilizing a first-order inertial link, the
delay characteristic is used as a
state variable to be augmented to an error tracking control architecture, and a
model prediction controller considering the steering
delay characteristic is designed according to the
state variable; the problem that the transverse
motion control performance of the automatic driving vehicle is deteriorated due to
hysteresis between the target wheel rotation angle and the actual vehicle rotation angle is solved. Meanwhile, a deep Q network (DQN) of a multi-target reward mechanism is designed, and an optimal strategy is trained to quickly calibrate multiple parameters of
model predictive control (MPC). The generalization performance of
reinforcement learning enables the vehicle to adapt to most complex working conditions after sufficient training, the problems that in a traditional method, the parameter calibration time of a
control system is long, and robustness is insufficient are solved, and high-precision and stable transverse control over the full-drive-by-wire four-wheel steering vehicle under the complex working conditions is achieved.