Adaptive Model Predictive Control on Unknown Deformable Terrains Using Physics-Informed Learning Tire Models

Vehicle mobility and control performance on deformable terrains is governed by the complex interaction that occurs at the tire-terrain interface. Unfortunately, on deformable terrains, accurately measuring terrain information is challenging, and discrepancies between assumed and actual parameters can degrade control performance and cause a loss of vehicle mobility. To address these challenges, this paper proposes an online adaptive Model Predictive Control (MPC) framework for autonomous vehicles operating in off-road environments with deformable terrains. First, we develop a physics-informed learning tire model for deformable terrains that is adaptable online and compatible with MPC. A novel Model Predictive Control formulation is presented for autonomous vehicles operating on deformable terrains and the efficacy of the formulation and proposed tire model is evaluated in simulation with Project Chrono. Comparative experiments, with and without online adaptation, highlight improved speed and path tracking performance through online adaptation when a mismatch between assumed and actual terrain parameters is present.