Learning-based control uses data to design efficient controllers for specific systems. When multiple systems are involved, experience transfer usually focuses on data availability and controller performance yet neglects robustness to variations between systems. In contrast, this letter explores experience transfer from a robustness perspective. We leverage the transfer to design controllers that are robust not only to the uncertainty regarding an individual agent's model but also to the choice of agent in a fleet. Experience transfer enables the design of safe and robust controllers that work out of the box for all systems in a heterogeneous fleet. Our approach combines scenario optimization and recent formulations for direct data-driven control without the need to estimate a model of the system or determine uncertainty bounds for its parameters. We demonstrate the benefits of our data-driven robustification method through a numerical case study and obtain learned controllers that generalize well from a small number of open-loop trajectories in a quadcopter simulation.

翻译：基于学习的控制利用数据为特定系统设计高效控制器。当涉及多个系统时，经验迁移通常侧重于数据可用性和控制器性能，却忽略了系统间差异的鲁棒性。本文从鲁棒性视角探讨经验迁移问题，利用迁移技术设计的控制器不仅对单个智能体模型的不确定性具有鲁棒性，还能适应异构编队中不同智能体的选择。经验迁移使得控制器能够即插即用地安全、鲁棒地运用于异构编队中的所有系统。本方法结合情景优化与直接数据驱动控制的最新框架，无需估计系统模型或确定参数的不确定性边界。通过数值案例研究验证了数据驱动鲁棒化方法的优势，并在四旋翼仿真中仅用少量开环轨迹即可获得泛化性能良好的学习控制器。