Real-Time Surrogate Modeling of Tracked Vehicle Terramechanics via Run-Level Learning and Deterministic Verification
12th International Conference on Control, Decision and Information Technologies, CoDIT 2026, Bari, İtalya, 13 - 16 Temmuz 2026, ss.306-311, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/codit70676.2026.11631150
- Basıldığı Şehir: Bari
- Basıldığı Ülke: İtalya
- Sayfa Sayıları: ss.306-311
- Anahtar Kelimeler: neural networks, real-time simulation, surrogate modeling, system identification, terramechanics, tracked vehicles
- Hacettepe Üniversitesi Adresli: Evet
Özet
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.