This paper presents the Task-Parameter Nexus (TPN), a learning-based approach for online determination of the (near-)optimal control parameters of model-based controllers (MBCs) for tracking tasks. In TPN, a deep neural network is introduced to predict the control parameters for any given tracking task at runtime, especially when optimal parameters for new tasks are not immediately available. To train this network, we constructed a trajectory bank with various speeds and curvatures that represent different motion characteristics. Then, for each trajectory in the bank, we auto-tune the optimal control parameters offline and use them as the corresponding ground truth. With this dataset, the TPN is trained by supervised learning. We evaluated the TPN on the quadrotor platform. In simulation experiments, it is shown that the TPN can predict near-optimal control parameters for a spectrum of tracking tasks, demonstrating its robust generalization capabilities to unseen tasks.

翻译：本文提出任务-参数关联（TPN），一种基于学习的方法，用于在线确定基于模型控制器（MBC）在执行跟踪任务时的（近）最优控制参数。在TPN中，引入深度神经网络来在运行时预测任意给定跟踪任务的控制参数，尤其当新任务的最优参数无法立即获得时。为训练该网络，我们构建了包含不同速度和曲率的轨迹库，以表征多种运动特性。随后，针对库中每条轨迹，我们离线自动调优最优控制参数，并将其作为对应的真实标签。基于该数据集，TPN通过监督学习进行训练。我们在四旋翼平台上评估了TPN。仿真实验表明，TPN能够为一系列跟踪任务预测近最优控制参数，展现了其对未见任务的强大泛化能力。