TY - GEN
T1 - Terrain-Adaptive Model Predictive Control for Autonomous Off-Road Vehicles in Unstructured Environments
AU - He, Xiaorui
AU - Ju, Zhiyang
AU - Tu, Yuantao
AU - Han, Xu
AU - Su, Youtao
AU - Gong, Jianwei
AU - Xi, Junqiang
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Autonomous navigation in unstructured off-road environments faces fundamental challenges from terrain-induced dynamics, multi-axle complexity, and computational intractability. This paper proposes a Terrain-Adaptive Model Predictive Control (TA-MPC) framework, which constructs a 3D Frenetbased parametric surface to encode real-time elevation and employs a semi-explicit differential-algebraic equation (DAE) model to specifically resolve high-order statically indeterminate load transfer issues and dynamic modeling issues of multi-axle vehicles under nonplanar constraints through suspension dynamics and wheel decoupling. This framework bridges a critical gap in terrain-adaptive control algorithms for multi-axle heavy-duty vehicles. Validated via TruckSim-MATLAB co-simulation on an 8 × 8 all-wheel-drive heavy-duty vehicle, TA-MPC demonstrates remarkable performance under extreme operating conditions.
AB - Autonomous navigation in unstructured off-road environments faces fundamental challenges from terrain-induced dynamics, multi-axle complexity, and computational intractability. This paper proposes a Terrain-Adaptive Model Predictive Control (TA-MPC) framework, which constructs a 3D Frenetbased parametric surface to encode real-time elevation and employs a semi-explicit differential-algebraic equation (DAE) model to specifically resolve high-order statically indeterminate load transfer issues and dynamic modeling issues of multi-axle vehicles under nonplanar constraints through suspension dynamics and wheel decoupling. This framework bridges a critical gap in terrain-adaptive control algorithms for multi-axle heavy-duty vehicles. Validated via TruckSim-MATLAB co-simulation on an 8 × 8 all-wheel-drive heavy-duty vehicle, TA-MPC demonstrates remarkable performance under extreme operating conditions.
KW - Multi-axle Heavy Vehicle
KW - Terrain-Adaptive Model Predictive Control
UR - https://www.scopus.com/pages/publications/105033519387
U2 - 10.1109/IESES66335.2025.11359845
DO - 10.1109/IESES66335.2025.11359845
M3 - Conference contribution
AN - SCOPUS:105033519387
T3 - 2025 IEEE 4th International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2025
SP - 752
EP - 757
BT - 2025 IEEE 4th International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2025
Y2 - 22 September 2025 through 24 September 2025
ER -