Robust Model Predictive Control-Based Autonomous Steering System for Collision Avoidance

Harsh road conditions and sudden obstacles often arise when autonomous vehicles (AVs) operate on real roads. Therefore, designing control for autonomous steering systems under these conditions is challenging. To overcome this challenge, we introduce a robust model predictive control-based (RMPC) autonomous steering system. To deal with a sudden obstacle that appears on real roads, an artificial potential field (APF) approach is introduced. It can generate an optimal-safe trajectory based on the receding horizon control (RHC) algorithm. In addition, to ensure the AV system operates well in slippery road conditions with highly varied coefficients, the RMPC is proposed, considering uncertain parameters and the varying velocity of the AV system. Specifically, the optimal control is designed by solving an optimization problem based on linear matrix inequalities to ensure a fast convergence rate. Furthermore, the input and output constraints are considered to guarantee the AV system works safely in complex and dynamic environments. Finally, various simulation results are provided to verify the effectiveness of the proposed control method.