document: Identification of linear parameter varying models We consider identification of a certain class of discrete-time nonlinear systems known as linear parameter varying system . We assume that inputs , outputs and the scheduling parameters are directly measured , and a form of the functional dependence of the system coefficients on the parameters is known . We show how this identification problem can be reduced to a linear regression , and provide compact formulae for the corresponding least mean square and recursive least-squares algorithms . We derive conditions on persistency of excitation in terms of the inputs and scheduling parameter trajectories when the functional dependence is of polynomial type . These conditions have a natural polynomial interpolation interpretation , and do not require the scheduling parameter trajectories to vary slowly . This method is illustrated with a simulation example using two different parameter trajectories
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