Real-Time Surrogate Modeling of Tracked Vehicle Terramechanics via Run-Level Learning and Deterministic Verification

Abstract

Real-time simulation of tracked skid-steer vehicles is frequently bottlenecked by the computational cost of track– terrain interaction models. To address this, we replace the physics-based terramechanics function in a Simulink model with a neural network surrogate tailored for hardware-in-the-loop deployment. We train separate Multi-Layer Perceptrons (MLPs) for rigid (friction-limited) and soil (strength-limited) regimes to predict body-frame forces and yaw moment (Qx,Qy,Mz). Crucially, we deviate from standard offline training by enforcing input truthfulness: dataset generation relies strictly on logged block interfaces—including signal delays and solver artifacts—rather than idealized command profiles. To prevent temporal leakage, we utilize run-level data splitting and a custom weighted loss function that penalizes errors in the sensitive yaw moment channel. Finally, we demonstrate that a manual MEX deployment eliminates runtime overhead, reducing latency below the physics baseline to enable real-time execution.