Hybrid model reference adaptive control for unmatched uncertainties

This paper presents a hybrid model reference adaptive control approach for systems with both matched and unmatched uncertainties. This approach extends concurrent learning adaptive control to a wider class of systems with unmatched uncertainties that lie outside the space spanned by the control input, and therefore cannot be directly suppressed with inputs. The hybrid controller breaks the problem into two parts. First, a concurrent learning identification law guarantees the estimates of the unmatched parameterization converges to the actual values in a determinable rate. While this begins, a robust reference model and controller maintain stability of the tracking and matched parameterization error. Once the unmatched estimates have converged, the system exploits this information to switch to a more aggressive controller to guarantee global asymptotic convergence of all tracking, matched, and unmatched errors to zero. Simulations of simple aircraft dynamics demonstrate this stability and convergence.