Controller tuning is a vital step to ensure the controller delivers its designed performance. DiffTune has been proposed as an automatic tuning method that unrolls the dynamical system and controller into a computational graph and uses auto-differentiation to obtain the gradient for the controller's parameter update. However, DiffTune uses the vanilla gradient descent to iteratively update the parameter, in which the performance largely depends on the choice of the learning rate (as a hyperparameter). In this paper, we propose to use hyperparameter-free methods to update the controller parameters. We find the optimal parameter update by maximizing the loss reduction, where a predicted loss based on the approximated state and control is used for the maximization. Two methods are proposed to optimally update the parameters and are compared with related variants in simulations on a Dubin's car and a quadrotor. Simulation experiments show that the proposed first-order method outperforms the hyperparameter-based methods and is more robust than the second-order hyperparameter-free methods.

翻译：控制器调优是确保其实现设计性能的关键步骤。DiffTune 作为一种自动调优方法被提出，它将动态系统与控制器展开为计算图，并利用自动微分获取控制器参数更新的梯度。然而，DiffTune 采用原始梯度下降法迭代更新参数，其性能在很大程度上依赖于学习率（作为超参数）的选择。本文提出使用无超参数方法更新控制器参数。我们通过最大化损失下降量来寻找最优参数更新，其中基于近似状态和控制的预测损失被用于该最大化过程。提出了两种最优参数更新方法，并在杜宾车和四旋翼飞行器的仿真中与相关变体进行了比较。仿真实验表明，所提出的一阶方法优于基于超参数的方法，且比二阶无超参数方法具有更强的鲁棒性。