Robust Adaptive Control Using Reduced Order Models

Abstract This paper deals with the problem of adaptively controlling a linear time-invariant plant with unknown parameters based on a reduced order model. It is now well known that when bounded disturbances are present, the global boundedness of all signals in the system can be assured (i) either by modifying the standard adaptive law or (ii) by making the reference input sufficiently persistently exciting. The unmodeled part of the plant introduces a signal which can be considered as a state-dependent disturbance in the analysis of the adaptive system. In this paper it is shown that the above methods can be extended to the problem of adaptive control using reduced order models as well.