Exponentially Stable Model Reference Adaptive Control with Deterministic Disturbances

This paper presents an exponentially stable model reference adaptive control system for processes with unmeasured deterministic disturbances. It includes a least squares algorithm to estimate the process parameters plus the parameters of an internal model of the disturbances. In the presence of persistent excitation the parameters and the control error converge exponentially at a rate specified by the user. If the disturbance is lower-order than assumed in the formulation (or totally absent) then the estimated parameters converge to a linear hyperplane defined by the process model and a common factor polynomial.