Integrated strategy for vehicle dynamics stability considering the uncertainties of side-slip angle

In this study, a side-slip angle estimator was presented, capable of compensating the model bias between test data and model prediction under the uncertainties of vehicle speed and friction coefficient, to obtain the actual side-slip angle. Specifically, the model validation method was adopted to optimise the irrelative coefficient of the principal component analysis model for calibrating the side-slip angle model bias. To calibrate all the model bias under other conditions without test data, the response surface methodology was developed to approximate all the side-slip angle model bias under any conditions. Based on the estimator, an integrated method based on the fuzzy model predictive control (MPC) algorithm was proposed with respect to the yaw moment and side-slip angle, taking brake pressure and steering angle as the controlled objects. Such a method integrates the function of active front steering and direct yaw-moment control into the MPC model and coordinates their proportion in accordance with the relevant parameters using the fuzzy control method. Lastly, the results of simulations with the hardware-in-loop platform verified that the proposed algorithm could enhance the vehicle stability and the computational accuracy of the side-slip angle.