Data-based Control of Partially-Observed Robotic Systems

摘要

This paper presents a data-based approach to control robotic systems with partially-observed feedback. First, an open-loop optimization problem is solved to generate the nominal trajectory and then a linear time-varying Autoregressive–Moving-Average (ARMA) model of the system is calculated along the trajectory from the output measurement data. The system is then described in information state, which contains input-output information of the past few steps. Finally, a feedback gain which is calculated by solving a specific LQG problem along the nominal trajectory. The separate design of the open-loop and the closed-loop problem is used following the Decoupled Data-based Control (D2C) approach. Simulation results are also shown for complex models with fluid-structure interaction in the presence of both process and measurement noise.