In this paper, a preview theory-based steering control approach considering vehicle dynamic constraints is presented. The constrained variables are predicted by an error states system and utilized to adjust the control law once the established dynamic constraints are violated. The simulated annealing optimization algorithm for preview length is conducted to improve the adaptability of the controller to varying velocities and road adhesion coefficients. The theoretical stability of a closed-loop system is guaranteed using Lyapunov theory, and further analysis of the system response in time domain and frequency domain is discussed. The results of simulations implemented on Carsim–Simulink demonstrate the favorable performance of the proposed control in tracking accuracy and system stability under extreme conditions.

This study proposes a lateral and longitudinal control strategy for intelligent vehicles based on the preview control theory and linear quadratic regulator (LQR) to enhance the trajectory tracking ability and stability. The two-degree-of-freedom dynamic model of the vehicle and the road-vehicle error dynamic model is established, and the future road curvature is incorporated as a disturbance into the LQR state vector using the preview control theory. An augmented LQR problem is solved according to the optimal theory to obtain the analytical solution of the control quantity. This strategy also enhances the adaptive ability of the intelligent vehicle to extreme conditions by taking into account the dynamic constraints. The preview time is optimized using the simulated annealing algorithm to obtain the optimal preview time under different vehicle speeds and road friction coefficients. The stability of the closed-loop control system composed of the new algorithm is analyzed to verify its feasibility. Simulation results on the Carsim/Simulink joint platform demonstrate that it has excellent trajectory tracking ability, stability, and robustness to the vehicle speed. The proposed strategy has the potential to significantly advance the field of intelligent vehicle control and improve the safety and efficiency of transportation systems.