Identification of linear parametrically varying systems

Addresses the problem of identification of linear parametrically varying systems with one measurable varying parameter. Under the assumption of full state measurements, the authors show that the problem can be reduced to a set of n (dimension of state space) recursive least squares problems. Further, the authors show that these recursions do estimate the parameters of the original model accurately under certain assumptions on the parameter variations. In the case of noisy state measurements the authors set up the problem as a set of n instrument variable recursions. Once again the authors demonstrate strong consistency of estimates. Simulations are presented to illustrate the results.