Intelligent vehicle path tracking based on feedback linearization and LQR under extreme conditions

It proposes a path following control strategy for intelligent vehicles under extreme conditions based on feedback linearization and linear quadratic regulator (LQR). Firstly, the vehicle dynamics model and the vehicle kinematics model are established, and the affine nonlinear vehicle system model under the extreme condition is established by these two models. Second, the complex nonlinear vehicle system model is linearized using the feedback linearization method to obtain a simpler linear system model. Finally, according to the obtained linear vehicle system model, the LQR method is used to design the tracking controller to obtain the optimal control input, so as to ensure the stability of the system and the optimality of the target, and realize the intelligent vehicle path tracking. To affirm the effectiveness of the tracking method, a series of Carsim/Simulink co-simulation experiments are carried out. The results show that the effects of path tracking combined with feedback linearization and LQR are significantly better than the other two methods, thus the path tracking method proposed in the paper is effectively verified.