Abstract:

The optimal control method has become the mainstream research and industry deployment method for lateral motion control in autonomous driving. The LQR method is widely used due to its advantages of low online computational load and good real-time performance， but it cannot consider time-varying references and steering delay. The presence of delay can cause the LQR method to lose stability at high speed， so it is essential to solve this problem while maintaining the characteristic of small computational load of LQR. In this paper， under the premise of ensuring real-time performance， the problem of LQR's inability to consider time-varying references and steering delay is solved. By incorporating road curvature as time-varying references， steering delay characteristics as pure delay， and first-order inertial section into the tracking error state equation， and by looking up the KKT inverse matrix part corresponding to the control time domain into the real-time solver， the aim is to reduce computational load and ensure controller real-time performance. The simulation results demonstrate that the constructed EqLPV-MPC controller can effectively handle road curvature changes. Compared to the LQR method， under the condition of dual lane change at a speed of 72 km/h， the lateral error decreases by 39%， with the heading error decreasing by 52%， and the lateral deviation of the center of mass decreasing by 28%. The results from real vehicle tests show that under dual lane change conditions， the controller constructed in this paper can keep the maximum lateral error within 0.1 m.

Key words: trajectory tracking, model predictive control, road curvature, steering lag, inertial element