A new approach to learning control via multiobjective optimization

Summary form only given. Previous work where estimation of the parameters of a plant was incorporated through exploratory schedules (ESs) which are reference input trajectories designed to enhance the learning of system parameters, is extended. In that work, ESs were generated offline and used in an open-loop fashion. Moreover, these ESs were used in between actual control tasks, therefore limiting the process of estimation during idle time. In this work the authors present an approach for generating ESs in a closed-loop manner. Such trajectories in general may not be the desired trajectories, since they result in larger tracking errors. However, ESs offer faster convergence to the system parameters and therefore yield smaller long-term tracking errors. The automation for the design of ESs requires online modification of the desired trajectory to enhance learning at the expense of poorer initial tracking