MPC Based Path-Tracking Algorithm Using Apriori Known Road Friction Condition for the Over-Actuated Subscale Vehicle Platform

Recent advancements in vehicle technology and autonomous systems necessitate more sophisticated control algorithms, including path-tracking, which is critical for self-driving cars. This paper introduces a path-tracking solution that utilizes an over-actuated platform with steering redundancy, and leverages prior knowledge of road conditions. The prediction of road condition is assumed to be known and available through V2V communication or vision-based predictions. The paper compares the performance of the Stanley Control Law, serving as a benchmark solution, and the Model Predictive Control (MPC) algorithm under varying road friction conditions using a scaled-down vehicle platform. The results demonstrate that the MPC algorithm, with its adaptive capabilities and integrated use of front and rear steering controls, surpasses the Stanley Control Law in maintaining stable and accurate path-tracking under changing conditions.