论文标题
基于神经网络的人形力学建模和可变形地形的估计
Neural network based terramechanics modeling and estimation for deformable terrains
论文作者
论文摘要
在这项工作中，介绍了基于神经网络的Terramegranics模型和地形估计器，并具有最佳控制应用程序（例如模型预测性控制）的前景。认识到最先进的地形力学模型在运行条件，计算成本以及基于梯度的优化的连续不同性方面的局限性，使用神经网络在可变形地形上动态操作开发了有效且两次连续可区分的Terramogenics模型。已经证明，与土壤接触模型作为最新模型相比，神经网络园艺模型能够准确，有效地预测横向轮胎力。此外，在地形估计器中实现了神经网络terramogenical模型，并显示使用该模型在真实地形参数的2％左右收敛。最后，考虑到模型预测控制应用程序通常依靠自行车模型进行预测，因此证明，利用估计的地形参数可以通过数量级来减少自行车模型的预测误差。结果是一个有效的，动态的，两次连续可区分的Terramogenics模型和估计器，与先前建立的模型相比，在模型预测控制中具有固有的优势。
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.
