用神经网络在线预测飞行器控制参数，提升任务适应性。
Task-Parameter Nexus: Task-Specific Parameter Learning for Model-Based Control
- 构建轨迹库并自动调优参数，训练神经网络实时预测控制参数
- 仿真中对多种轨迹任务均能预测近优参数，泛化能力强
- 适合需要快速适应新任务的模型预测控制场景
本文提出任务-参数关联（Task-Parameter Nexus, TPN），一种基于学习的在线方法，用于确定模型预测控制器（MBC）在跟踪任务中的（近）最优控制参数。在TPN中，引入深度神经网络，在运行时为任意给定的跟踪任务预测控制参数，尤其适用于新任务缺乏即时最优参数的情况。为此，我们构建了一个包含不同速度和曲率的轨迹库，代表多种运动特征；对库中每条轨迹，离线自动调优获得最优控制参数作为真实标签。利用该数据集，通过监督学习训练TPN。在四旋翼平台上的评估表明，仿真中TPN能为一系列跟踪任务预测近最优控制参数，展现出对未见任务的强大泛化能力。
原文摘要 · Abstract (English)
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.
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