A Slip-Based Model Predictive Control Approach for Trajectory Following of Unmanned Tracked Vehicles

In the field of tracked vehicle dynamics, studies show that vertical loads are concentrated under road wheels on firm road conditions, allowing slip-based models of tracked vehicles to be designed similar to wheeled vehicle models. This paper proposes a slip-based nonlinear two-track prediction model for model predictive control (MPC), where track forces under road wheels are calculated with a simplification procedure implemented onto shear displacement theory. The study includes a comparative analysis with a kinematic prediction model, examining scenarios such as constant speed cornering and spiral maneuvers. Validation is carried out by comparing the simulation results of the proposed controller with field test data acquired from a five-wheeled tracked vehicle platform, including measurements on asphalt and stabilized road conditions. The results demonstrate that the slip-based model excels in trajectory tracking, with lateral deviations consistently below 0.25 m and typically around 0.02-0.08 m RMS depending on the scenario. By improving the computational efficiency and ensuring precise navigation, this approach offers an advanced control solution for tracked vehicles on firm terrain.