We study families of time-varying linear systems with restrictions on the derivative of the parameter variation. This includes the systems usually considered in the area of linear parameter varying (LPV) systems. We show that it is possible to construct exact parameterized Lyapunov norms for a wide class of such systems. This may be used to derive (locally Lipschitz) continuous dependence of th...

This paper extends existing results on a technique for gain-scheduled, L 2-gain control of linear parameter-varying (LPV) systems with bounded rates of variation. Speciically, it introduces the use of both memoryless and dynamic (i.e., ltered) parameter measurements when the parameters' accelerations are bounded. Information from the ltered parameter signal can be used to reduce the conservatis...

A suitable design of state estimators requires a representative model for capturing the plant behavior and knowledge about the noise statistics, which are generally not known in practical applications. While the measurement noise covariance can be directly derived from the measurement device reproducibility, the choice of the process noise covariance is much less straightforward. Further, proce...

This paper studies the induced L2-norm problem for switched linear parameter varying (LPV) systems using a blending method. For a switched LPV system where the parameters are grouped into slow-varying and fast-varying parameters, the blending method is used to construct blended Lyapunov functions based on the multiple Lyapunov functions conditions in terms of linear matrix inequalities (LMIs). ...

In this paper, a novel and adaptive method is proposed for parameter identification performance estimation of photovoltaic (PV) module under varying operating conditions. By comprehensively considering the irradiance temperature dependence physical parameters in single-diode model, set new transforming equations to model calculate This developed without setting any reference condition, thereby ...

Many systems arising in biological applications are subject to periodic forcing. In these systems the forcing parameter is not only time-varying but also known to have a periodic structure. We present an approach to estimate periodic, time-varying parameters using nonlinear Bayesian filtering. Most parameter estimation methodology in the literature is aimed at estimating constant parameters or ...