Recursively updated least squares based modification term for adaptive control

We present an approach for combining standard recursive least squares based regression with proven direct model reference adaptive control using a recursively updated modification term. This approach is applicable to adaptive control problems where the uncertainty can be linearly parameterized. The combined training law drives the adaptive weights smoothly to a recursively updated least squares estimate of the ideal weights and is shown to have a stability proof. Expected improvement in performance of the adaptive law is validated through simulation.