  In this work, a neural network based terramechanics model and terrain
estimator are presented with an outlook for optimal control applications such
as model predictive control. Recognizing the limitations of the
state-of-the-art terramechanics models in terms of operating conditions,
computational cost, and continuous differentiability for gradient-based
optimization, an efficient and twice continuously differentiable terramechanics
model is developed using neural networks for dynamic operations on deformable
terrains. It is demonstrated that the neural network terramechanics model is
able to predict the lateral tire forces accurately and efficiently compared to
the Soil Contact Model as a state-of-the-art model. Furthermore, the neural
network terramechanics model is implemented within a terrain estimator and it
is shown that using this model the estimator converges within around 2% of the
true terrain parameter. Finally, with model predictive control applications in
mind, which typically rely on bicycle models for their predictions, it is
demonstrated that utilizing the estimated terrain parameter can reduce
prediction errors of a bicycle model by orders of magnitude. The result is an
efficient, dynamic, twice continuously differentiable terramechanics model and
estimator that has inherent advantages for implementation in model predictive
control as compared to previously established models.
