A singular value maximizing data recording algorithm for concurrent learning

We present a singular value maximizing algorithm for recording data to be used by concurrent learning adaptive controllers. These controllers use recorded and current data concurrently and can have exponential stability guarantees, with the rate of convergence directly proportional to the minimum singular value of the matrix containing recorded data. The presented algorithm selects data for recording to improve the minimum singular value, and hence results in improved tracking performance, this is established through comparison with previously studied data recording methods that record points that are sufficiently different.