3 research outputs found
Experience Transfer for Robust Direct Data-Driven Control
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