Direct identification of continuous-time LPV models

Controllers in the linear parameter-varying (LPV) framework are commonly designed in continuous time (CT) requiring accurate and low-order CT models of the system. Nonetheless, most of the methods dedicated to the identification of LPV systems are addressed in the discrete-time setting. In practice when discretizing models which are naturally expressed in CT, the dependency on the scheduling variables becomes non-trivial and over-parameterized. Consequently, direct identification of CT-LPV systems in an input-output setting is investigated. To provide consistent model parameter estimates in this setting, a refined instrumental variable approach is proposed. The statistical properties of this approach are demonstrated through a Monte Carlo simulation example.