Generation of exploratory schedules in closed loop for enhanced machine learning

摘要

The work presented here is an extension of previous work, where estimation of the parameters of a plant was incorporated through exploratory schedules (ES), which are reference input trajectories designed to enhance the learning of system parameters. ESes were earlier generated off-line and used in an open-loop fashion. Moreover, these ESes were used between actual control tasks, therefore limiting the process of estimation during idle time. Here the authors attempt to generate ESes in a closed-loop manner. Such trajectories in general may not be the desired trajectories, resulting in larger tracking errors. However, ESes offer faster convergence to the system parameters and therefore yield smaller long-term tracking errors. The automation for the design of ESes requires on-line modification of the desired trajectory to enhance learning at the expense of poorer initial tracking.